Timothy Ronan Leahy
email: leahyt@tcd.ie

MD, FRCPC Yvonne C W Yau

Eshetu Atenafu

PhD Mary Corey

MD Felix Ratjen

MD, MSc, FRCPC Valerie Waters

M B Mrcpi

aureus in pediatric cystic fibrosis. 2 3

Keywords: methicillin resistant Staphylococcus aureus, mecA, pulsed-field gel electrophoresis methicillin resistant Staphylococcus aureus (MRSA), mecA, pulsed-field gel 20 electrophoresis. 21

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

later. Data collected included information on patients' demographics, pulmonary function 93 tests (PFTs), respiratory cultures, hospital admissions, acute pulmonary exacerbations 94 and antibiotic use. An acute pulmonary exacerbation was defined as a change in 95 pulmonary status considered by the attending physician to require antibiotic treatment, 96 either oral or intravenous. An "antibiotic day" was defined as one day for each antibiotic 97 used, for example a 14-day course of two antibiotics was defined as 28 "antibiotic days". 98

The purpose of this distinction was to capture more complex antibiotic regimens that 99 might be employed to treat MRSA infected patients. Patients were classified as being on 100 oral cephalexin if they were prescribed cephalexin at the time of index culture of at least 101 one month's duration (in all instances, this occurred in the absence of signs or symptoms 102 of a pulmonary exacerbation). Patients were classified as being on inhaled tobramycin if 103 they were prescribed inhaled tobramycin at the time of index culture of at least one 104 month's duration (for previous P. aeruginosa infection). Patients were classified as being 105 on oral ciprofloxacin if they were prescribed at least one course of ciprofloxacin (in all 106 instances, this occurred in the presence of signs or symptoms of a pulmonary 107 exacerbation). Patients were categorized as co-colonized with other CF pathogens using 108 previously defined criteria 8 . Only patients with at least 6 months of follow-up after the 109 index culture were included in the analysis of outcome measures.

Specimen Processing 115

Respiratory specimens from CF patients were screened for MRSA using media 116 containing 2 µg/ml of oxacillin: an in-house mannitol salt agar (MRSA agar base, 117 Acumedia, Lansing, MI, and 1% D-mannitol, Difco, Detroit, MI ) supplemented with 1% 118 thymidine and 2 µg/ml of oxacillin (both from Sigma, St. Louis, MO) was used from 119 1992 to 2001, and Oxacillin Resistance Screening Agar Base (ORSAB, Oxoid, Nepean, 120 Ontario, Canada) was used from 2002 onward. Plates were incubated at 35 o C aerobically 121 and observed for the presence of growth at 24 hours and at 48 hours. Suspicious colonies 122 were identified as S. aureus by Gram stain, catalase, slide coagulase, and tube coagulase 123 tests. 124 125

Antimicrobial Susceptibility 126

Antimicrobial susceptibity of S. aureus isolates was tested by a combination of 127 oxacillin 6µg/ml screen plate, disk diffusion 6,9 and an automated system (Vitek, bio-128 Merieux, St. Laurent, Quebec, from 1992 to Nov 2002, and the BD Automated Phoenix 129 System, BD Diagnostic Systems, Sparks, MD from Nov 2002 onward). Inducible 130 resistance to clindamycin was detected by disk diffusion using the D-zone test 6,9 . In 131 addition, each strain was examined for the presence of PBP2a by latex agglutination 132 (PBP2' test kit, Oxoid, Hants, UK). Organisms that were negative for PBP2a by latex 133 agglutination but grew on the oxacillin screen plate were sent to the reference laboratory 134 (Central Provincial Health Laboratory, Ontario) for minimum inhibitory concentration 135 (MIC) by agar dilution and polymerase chain reaction (PCR) for the detection of the 136 PBP2a was absent and the mecA PCR was negative 7 . An isolate was deemed as MRSA if 138 the oxacillin MIC was ≥4ug/ml, the latex agglutination for PBP2a was positive and/or the 139 mecA PCR was positive 6 . 140 141 β-Lactamase Production 142 All BORSAs were tested for the production of β-lactamase as a potential 143 mechanism of oxacillin resistance by disk diffusion with ampicillin (10 µg) and 144 amoxicillin-clavulinic (20 µg/ 10 µg) disks as previously described ( 13). An isolate was 145 deemed as hyper β-lactamase producing if the ampicillin zone size was resistant and the 146 amoxicillin-clavulanic zone size was susceptible, suggesting borderline resistance was 147 secondary to large amounts of β-lactamase 9 (13). 148 149

Molecular Characterization 150

The mecA and nuc gene were co-amplified with a multiplex real time PCR as 151 described previously 10 . The presence of Panton-Valentine leukocidin was examined by 152 amplification of the lukF-PV and lukS-PV genes using primer and protocols previously 153 described 11 . Pulsed-field gel electrophoresis (PFGE) was conducted using SmaI as per 154 manufacturer protocol (Bio-rad Laboratories, Hercules, CA) on all newly identified 155 MRSA strains. Electrophoretically-generated DNA profiles were analyzed using the 156 criteria proposed by Tenover et al to determine strain relatedness 12

Patient population 173

Between 1992 and 2007, 38 patients had at least one respiratory specimen 174 positive for either BORSA or MRSA. Of these, three were excluded from our analysis 175 because their care was not primarily based at our hospital, thus we had no clinical data 176 for these patients. In addition, mecA PCR could not be performed on one isolate 177 retrospectively therefore 34 patients were included in the study. Of these 34 patients, two 178 patients were initially colonized with mecA-negative and subsequently mecA-positive 179 strains and were excluded from the final statistical analysis as it was unclear how to 180 categorize them in terms of patient characteristics and outcomes. Ultimately 16 patients 181 with mecA positive MRSA and 16 with mecA negative BORSA on the index culture were 182 compared for baseline characteristics. For the longitudinal assessment, 15 patients in each 183 group were compared since one patient in each group did not have follow-up data for at 184 least 6 months. 185 186 Patient characteristics and risk factors for BORSA and MRSA 187

The two groups were similar in terms of baseline characteristics (Table 1). There 188 was no difference in pulmonary function or rate of decline of pulmonary function in the 189 year prior to the index culture. In addition, there was no difference in the number of acute 190 pulmonary exacerbations, hospital admissions or overall antibiotic use for the treatment 191 of acute pulmonary exacerbations. A significantly greater proportion of BORSA infected 192 patients were colonized with MSSA in the year prior (94% vs 25%, p<0.0001 were related by the Tenover criteria 13 (Figure 1). Within the MRSA group, we found 235 four separate instances of strain relatedness during the course of the study: a pair of 236 siblings shared an indistinguishable strain, four patients shared possibly related strains, 237 two patients shared a closely related strain with a third patient sharing a possibly related 238 To compare the clinical impact of BORSA versus MRSA infection on disease 243 progression, we assessed 99.7 patient-years of follow-up after the index culture with a 244 comparable mean duration of follow up of 3.37 years in the MRSA group and 3.27 years 245 in the BORSA group (p=0.507) (Table 3). Patients infected with MRSA were more 246 likely to have persistent infection at the end of follow up than patients with BORSA 247 infection (80% vs 53%) but this was not statistically significant (p=0.121). The annual 248 decline in percent predicted forced expiratory volume in 1 second (FEV 1 ), was greater 249 among the MRSA infected patients (5.8%) than those infected with BORSA (2.8%) 250 although this difference was not significant (p = 0.77). Neither was there a significant 251 difference in the number of acute pulmonary exacerbations, hospital admissions, inpatient 252 days or clinic visits. One patient in the MRSA group died, and another progressed to lung 253 transplant. Three patients in the BORSA group had a lung transplant during the study 254 This is the first study to show that a significant proportion (50%) of S. aureus 259 isolates with methicillin resistance infecting CF patients are BORSA which lack the 260 mecA gene, the main genetic determinant of methicillin resistance in S. aureus 7 . The 261 present study demonstrates that antibiotic use in CF patients colonized with MSSA is a 262 risk factor specifically for infection with BORSA. Previous studies have shown that 263 ciprofloxacin and cephalosporin use is a risk factor for acquisition of MRSA in CF 264 patients 14 but, to our knowledge, no other study has examined the differential risk factors 265 for BORSA compared to MRSA acquisition. 266

Transmission of traditional MRSA between patients 15,16 , within the community 17 267 and even within households 18 has been well described in the CF population. Evidence of 268 person to person transmission of mecA-positive MRSA was also present in this study 269 with shared strains among CF patients and siblings. The mechanism of resistance is 270 relevant to how CF patients acquire MRSA as the mecA gene is absent in its entirety from 271 MSSA 19 7 . It is believed to have originated from another staphylococcus species, such as 272 Staphylococcus sciuri, and been transposed into MSSA 20 . Although horizontal transfer 273 of mecA between a S. epidermidis and MSSA isolate within the same patient has been 274 described 21 , transmission of mecA-positive MRSA isolates between individuals is likely 275 more common as there are a well defined number of mecA-positive MRSA clones 276 worldwide 22 . 277

Our data would suggest that CF patients may acquire mecA-negative BORSA 278 isolates in a different way. In contrast to MRSA isolates, BORSA isolates had unique 279 MICs to the susceptible range with the addition of a β-lactamase inhibitor 23 . This 287 predicts the response of oxacillin MICs to the addition of a β-lactamase inhibitor and 288 suggests that a large amount of β-lactamase production is the mechanism of resistance to 289 oxacillin in the majority of BORSA, as observed in 12 of 16 isolates in our study. 290 However, in the 4 remaining BORSA strains where the ampicillin zone size did not 291 change significantly with the addition of clavulanic acid, the mechanism of methicillin 292 resistance is unknown. In the aforementioned study, the authors described S. aureus 293 strains with intermediate MICs to oxacillin that remained the same in the presence of 294 clavulanic acid. The authors suggested that oxacillin resistance was due to factors other 295 than β-lactamase inactivation 23 . It is possible that it is due to a modification of "normal" 296 penicillin-binding proteins (PBPs) such as PBP 1,PBP 2 and PBP 4 as was previously 297

shown in clinical BORSA isolates by Tomasz et al 24 . Exposure of susceptible 298 staphylococci to increasing concentrations of β-lactam antibiotics in the laboratory is 299 known to cause PBP alterations associated with increased MICs 25 . In this study, 300 persistent antibiotic pressure may have similarly modified PBPs in endogenous MSSA 301 may not be as important as the judicious use of antibiotics. 305

In our study, the majority of patients treated with oral cephalexin were colonized 306 with MSSA. Although there were no signs and symptoms of a pulmonary exacerbation at 307 the time, we are limited in a retrospective study to the information that is recorded in the 308 medical charts. Their antibiotic treatment was thus likely driven by their MSSA positive 309 respiratory cultures and/or more subtle symptoms. Primary prophylaxis for S. aureus (the 310 treatment of all newly diagnosed CF patients with antistaphylococcal agents irrespective 311 of respiratory culture results) is not routinely practiced in North America due to concerns 312 of P. aeruginosa acquisition 26,27 . A systematic review of prophylactic antibiotics in 313 cystic fibrosis, however, suggests that P. aeruginosa acquisition in this context is 314 associated less frequently with narrow spectrum anti-staphylococcal antibiotics such as 315 flucloxacillin compared with broader spectrum anti-staphylococcal antibiotics such as 316 cephalexin 28 . In our study, although patients treated with oral cephalexin were not 317 receiving prophylaxis against S. aureus, they were receiving prolonged courses of 318 cephalexin for MSSA detected on routine culture of the respiratory tract. This prolonged 319 cephalexin use was identified as an independent risk factor for the acquisition of BORSA 320 isolates and occurred recently, primarily in the last 5 years of the study. 321

The clinical significance of BORSA infection in CF patients is unknown. Our 322 study lacked a sufficient sample size and is not powered to clearly delineate the clinical 323 impact of BORSA infection. Previous studies have reported accelerated deterioration in 324 pulmonary function 2 , increased hospitalization 3 , impaired growth, increased antibiotic 325 4 and increased mortality 5 in CF patients infected with MRSA. However, these 326 studies are limited by a lack of information on the laboratory methodology of identifying 327 MRSA and thus it is not known to what degree these effects are attributable to BORSA or 328

MRSA. 329

Detection of BORSA isolates may also influence the choice of antibiotics in the 330 treatment of these infections in the setting of a pulmonary exacerbation. In our study, 331 BORSA isolates were more susceptible to erythromycin and clindamycin than the MRSA 332 isolates. This finding is not surprising as the mecA gene is located on a mobile genetic 333 element (the Staphylococcal Cassette Chromosome mec: SCCmec) which can contain 334 additional drug resistance genes 22 . Although earlier studies suggested that infections due 335 to mecA negative BORSA isolates could be treated with β-lactam antibiotics 29,30 , more 336 recent cases report failure of cloxacillin and even vancomycin in the treatment of 337 endocarditis caused by BORSA, depending on the mechanism of methicillin resistance 338 31,32 . This has implications for how respiratory specimens from CF patients should be 339 screened for MRSA. Some of the newer MRSA selective media such as chromogenic 340 media for MRSA that use cefoxitin as the selective agent will not detect mecA-negative 341 BORSA isolates as we have observed in our own clinical laboratory. Thus these isolates 342 could be missed from CF respiratory specimens unless full antimicrobial susceptibilities 343 were performed on all S. aureus isolates. Hence, CF patients could potentially be treated 344 with suboptimal antibiotics during a pulmonary exacerbation. 345 There were several limitations to this study. It is unclear whether CF patients 346 infected with BORSA isolates behave more like CF patients infected with MSSA or CF 347 patients infected with MRSA Future studies assessing the clinical impact of BORSA in 348 The majority of BORSA isolates were hyper β-lactamase producers and susceptible to a 39 greater range of antibiotics. Strain relatedness was not evident within the BORSA group. 40

There was no difference in disease progression between the two groups. This is the first 41 study to demonstrate that a significant proportion of S. aureus isolates with methicillin 42 resistance in the CF population are BORSAs that lack mecA. significant rise in MRSA infection among CF patients, from 2.1% in 1996 to 21.2% in 50 2007 1 . MRSA infection has been associated with accelerated deterioration in pulmonary 51 function 2 , increased hospitalization 3 , impaired growth, increased antibiotic usage 4 and 52 increased mortality in CF patients 5 . 53

According to the Clinical Laboratory Standards Institute (CLSI), methicillin 54 resistance in S. aureus is defined as having an oxacillin minimum inhibitory 55 concentration (MIC) > 4 µg/ml 6 . The primary mechanism of resistance for MRSA is the 56 production of an altered penicillin binding protein (PBP2a) with reduced affinity for 57 methicillin that is encoded by the mecA gene 7 . We have identified a group of CF patients 58 infected with borderline resistant S. aureus (BORSA) isolates that are characterized by 59 oxacillin MICs at or just above the susceptibility breakpoint of 4 µg/ml and lack the 60 mecA gene 7 . The implications of isolating BORSA in a respiratory culture from CF 61 patients have not previously been explored. The aim of this study was therefore to 62 compare the risk factors for acquisition, the molecular epidemiology and subsequent 63 either oral or intravenous. An "antibiotic day" was defined as one day for each antibiotic 97 used, for example a 14-day course of two antibiotics was defined as 28 "antibiotic days". 98

The purpose of this distinction was to capture more complex antibiotic regimens that 99 might be employed to treat MRSA infected patients. Patients were classified as being on 100 oral cephalexin if they were prescribed cephalexin at the time of index culture of at least 101 one month's duration (in all instances, this occurred in the absence of signs or symptoms 102 of a pulmonary exacerbation). Patients were classified as being on inhaled tobramycin if 103 they were prescribed inhaled tobramycin at the time of index culture of at least one 104 month's duration (for previous P. aeruginosa infection). Patients were classified as being 105 on oral ciprofloxacin if they were prescribed at least one course of ciprofloxacin (in all 106 instances, this occurred in the presence of signs or symptoms of a pulmonary 107 exacerbation). Patients were categorized as co-colonized with other CF pathogens using 108 previously defined criteria 8 . Only patients with at least 6 months of follow-up after the 109 index culture were included in the analysis of outcome measures. PBP2a was absent and the mecA PCR was negative 7 . An isolate was deemed as MRSA if 138 the oxacillin MIC was ≥4ug/ml, the latex agglutination for PBP2a was positive and/or the 139 mecA PCR was positive 6 . 140 141 β-Lactamase Production 142 All BORSAs were tested for the production of β-lactamase as a potential 143 mechanism of oxacillin resistance by disk diffusion with ampicillin (10 µg) and 144 amoxicillin-clavulinic (20 µg/ 10 µg) disks as previously described ( 13). An isolate was 145 deemed as hyper β-lactamase producing if the ampicillin zone size was resistant and the 146 amoxicillin-clavulanic zone size was susceptible, suggesting borderline resistance was 147 secondary to large amounts of β-lactamase 9 (13). 148 149

Molecular Characterization 150

The mecA and nuc gene were co-amplified with a multiplex real time PCR as 151 described previously 10 . The presence of Panton-Valentine leukocidin was examined by 152 amplification of the lukF-PV and lukS-PV genes using primer and protocols previously 153 described 11 . Pulsed-field gel electrophoresis (PFGE) was conducted using SmaI as per 154 manufacturer protocol (Bio-rad Laboratories, Hercules, CA) on all newly identified 155 MRSA strains. Electrophoretically-generated DNA profiles were analyzed using the 156 criteria proposed by Tenover et al to determine strain relatedness 12 . 157 Between 1992 and 2007, 38 patients had at least one respiratory specimen 174 positive for either BORSA or MRSA. Of these, three were excluded from our analysis 175 because their care was not primarily based at our hospital, thus we had no clinical data 176 for these patients. In addition, mecA PCR could not be performed on one isolate 177 retrospectively therefore 34 patients were included in the study. Of these 34 patients, two 178 patients were initially colonized with mecA-negative and subsequently mecA-positive 179 strains and were excluded from the final statistical analysis as it was unclear how to 180 categorize them in terms of patient characteristics and outcomes. Ultimately 16 patients 181 with mecA positive MRSA and 16 with mecA negative BORSA on the index culture were 182 compared for baseline characteristics. For the longitudinal assessment, 15 patients in each 183 group were compared since one patient in each group did not have follow-up data for at 184 least 6 months. 185 186 Patient characteristics and risk factors for BORSA and MRSA 187

The two groups were similar in terms of baseline characteristics (Table 1). There 188 was no difference in pulmonary function or rate of decline of pulmonary function in the 189 year prior to the index culture. In addition, there was no difference in the number of acute 190 3). Patients infected with MRSA were more 246 likely to have persistent infection at the end of follow up than patients with BORSA 247 infection (80% vs 53%) but this was not statistically significant (p=0.121). The annual 248 decline in percent predicted forced expiratory volume in 1 second (FEV 1 ), was greater 249 among the MRSA infected patients (5.8%) than those infected with BORSA (2.8%) 250 although this difference was not significant (p = 0.77). Neither was there a significant 251 difference in the number of acute pulmonary exacerbations, hospital admissions, inpatient 252 days or clinic visits. One patient in the MRSA group died, and another progressed to lung 253 transplant. Three patients in the BORSA group had a lung transplant during the study 254 This is the first study to show that a significant proportion (50%) of S. aureus 259 isolates with methicillin resistance infecting CF patients are BORSA which lack the 260 mecA gene, the main genetic determinant of methicillin resistance in S. aureus 7 . The 261 present study demonstrates that antibiotic use in CF patients colonized with MSSA is a 262 risk factor specifically for infection with BORSA. Previous studies have shown that 263 ciprofloxacin and cephalosporin use is a risk factor for acquisition of MRSA in CF 264 patients 14 but, to our knowledge, no other study has examined the differential risk factors 265 for BORSA compared to MRSA acquisition. 266

Transmission of traditional MRSA between patients 15,16 , within the community 17 267 and even within households 18 has been well described in the CF population. Evidence of 268 person to person transmission of mecA-positive MRSA was also present in this study 269 with shared strains among CF patients and siblings. The mechanism of resistance is 270 relevant to how CF patients acquire MRSA as the mecA gene is absent in its entirety from 271 MSSA 19 7 . It is believed to have originated from another staphylococcus species, such as 272 Staphylococcus sciuri, and been transposed into MSSA 20 . Although horizontal transfer 273 of mecA between a S. epidermidis and MSSA isolate within the same patient has been 274 described 21 , transmission of mecA-positive MRSA isolates between individuals is likely 275 more common as there are a well defined number of mecA-positive MRSA clones 276 worldwide 22 4 and increased mortality 5 in CF patients infected with MRSA. However, these 326 studies are limited by a lack of information on the laboratory methodology of identifying 327 MRSA and thus it is not known to what degree these effects are attributable to BORSA or 328

MRSA. 329

Detection of BORSA isolates may also influence the choice of antibiotics in the 330 treatment of these infections in the setting of a pulmonary exacerbation. In our study, 331 BORSA isolates were more susceptible to erythromycin and clindamycin than the MRSA 332 isolates. This finding is not surprising as the mecA gene is located on a mobile genetic 333 element (the Staphylococcal Cassette Chromosome mec: SCCmec) which can contain 334 additional drug resistance genes 22 . Although earlier studies suggested that infections due 335 to mecA negative BORSA isolates could be treated with β-lactam antibiotics 29,30 , more 336 recent cases report failure of cloxacillin and even vancomycin in the treatment of 337 endocarditis caused by BORSA, depending on the mechanism of methicillin resistance 338 31,32 . This has implications for how respiratory specimens from CF patients should be 339 screened for MRSA. Some of the newer MRSA selective media such as chromogenic 340 media for MRSA that use cefoxitin as the selective agent will not detect mecA-negative 341 BORSA isolates as we have observed in our own clinical laboratory. Thus these isolates 342 could be missed from CF respiratory specimens unless full antimicrobial susceptibilities 343 were performed on all S. aureus isolates. Hence, CF patients could potentially be treated 344 with suboptimal antibiotics during a pulmonary exacerbation. 345 There were several limitations to this study.

  Staphylococcus aureus (MRSA) has emerged as an important 48 respiratory pathogen in the cystic fibrosis (CF) population. The US CF registry reported a was assessed by examination of the time period from index 91 culture until discharge from the clinic or until December 31 st 2008, whichever date was 92

7 mecA

 7 gene. An isolate was deemed as BORSA if the oxacillin MIC was > 4 µg/ml, 137

  finally, three patients (including a pair of siblings) shared an indistinguishable

  appeared not to be shared between patients in our study. CF patients 280 colonized with MSSA and exposed to oral cephalexin or inhaled tobramycin (which can 281 act synergistically with β-lactam antibiotics against S. aureus) were at significantly 282 increased risk of acquiring BORSA infection. This suggests that BORSA isolates may 283 arise from endogenous MSSA isolates that develop other mechanisms of resistance to 284 methicillin in response to antibiotic pressure. Previous studies have shown that S. aureus 285 strains with borderline susceptibility to oxacillin have a significant drop in the penicillin 286

  producing BORSA isolates. Thus, in the prevention of the emergence 303 of BORSA in CF patients, other infection control measures, such as patient segregation, 304

  compare BORSA, MRSA and MSSA colonized patients in a longitudinal 349 fashion, controlling for potential confounding factors and include more extensive PFGE 350 comparisons to determine the evolution of BORSA. In addition, the small number of 351 patients limited the number of variables that could be included in the logistic regression 352 model of risk factors. This study was also not powered to detect differences in clinical 353 outcomes between the groups. It was a single centre study and may not reflect findings in 354 other CF populations. Finally, in a quarter of patients with mecA-negative BORSA, the 355 mechanism of resistance to methicillin remains unknown. 356In conclusion, CF patients with mecA-positive MRSA most commonly acquire 357 their infection through person to person transmission whereas BORSA is likely 358 preferentially selected out from endogenous MSSA in CF patients due to persistent 359 antibiotic pressure. Prospective studies are needed to examine the prevalence, clinical 360 impact and appropriate management of BORSA in the CF population, as well as the 361 underlying mechanism of resistance.

Figure 1 . 1 Epidemiology of borderline oxacillin-resistant Staphylococcus 1 aureus in pediatric cystic fibrosis. 2 3 17 * 23 Page 24 A

 1112172324 Figure 1.

  Antibiotic pressure may 43 lead to the development of BORSA in CF patients. Prospective studies are needed to 44 assess its clinical impact. 45 Introduction 46 47 Methicillin-resistant Staphylococcus aureus (MRSA) has emerged as an important 48 respiratory pathogen in the cystic fibrosis (CF) population. The US CF registry reported a 49

  clinical impact on disease progression of BORSA infection in comparison with mecA-64 positive MRSA infection in pediatric CF patients. single-centre retrospective comparative study of pediatric CF patients 71 whose care was primarily based at the CF clinic at the Hospital for Sick Children, 72 Toronto between 1992 and 2007. The clinic currently cares for approximately 280 73 children up to the age of eighteen. Respiratory samples (expectorated sputum or 74 oropharyngeal swabs) were sent from all children attending the clinic to the microbiology 75 laboratory for culture at every 3-month visit and during acute pulmonary exacerbations 76 (APEs). Patients with one or more respiratory specimens positive for S. aureus with 77 methicillin resistance (either BORSA or MRSA) were included in the analysis. Cases 78 with three or more specimens positive for BORSA or MRSA during the study period 79 were considered persistently colonized 2 . The study was approved by the Hospital for 80 Sick Children's Research Ethics Board. 81 82 Data collection 83 Patients with BORSA or MRSA isolated from their respiratory specimens were 84 identified using the microbiology database, and cross-referenced with the CF clinical 85 database. Date of the first BORSA or MRSA-positive respiratory culture (index culture) 86 for each patient was identified. The medical and laboratory records for each patient were 87 examined using a standardized data collection form. Baseline patient characteristics were 88 assessed at the date of index culture, and risk factors for acquisition of BORSA or MRSA 89 infection were assessed over the twelve-month period prior to the index culture. Impact 90 was assessed by examination of the time period from index 91 culture until discharge from the clinic or until December 31 st 2008, whichever date was 92 later. Data collected included information on patients' demographics, pulmonary function 93 tests (PFTs), respiratory cultures, hospital admissions, acute pulmonary exacerbations 94 and antibiotic use. An acute pulmonary exacerbation was defined as a change in 95 pulmonary status considered by the attending physician to require antibiotic treatment, 96

7 mecA

 7 specimens from CF patients were screened for MRSA using media 116 containing 2 µg/ml of oxacillin: an in-house mannitol salt agar (MRSA agar base, 117 Acumedia, Lansing, MI, and 1% D-mannitol, Difco, Detroit, MI ) supplemented with 1% 118 thymidine and 2 µg/ml of oxacillin (both from Sigma, St. Louis, MO) was used from 119 1992 to 2001, and Oxacillin Resistance Screening Agar Base (ORSAB, Oxoid, Nepean, 120 Ontario, Canada) was used from 2002 onward. Plates were incubated at 35 o C aerobically 121 and observed for the presence of growth at 24 hours and at 48 hours. Suspicious colonies 122 were identified as S. aureus by Gram stain, catalase, slide coagulase, and tube coagulase susceptibity of S. aureus isolates was tested by a combination of 127 oxacillin 6µg/ml screen plate, disk diffusion 6,9 and an automated system (Vitek, bio-128 Merieux, St. Laurent, Quebec, from 1992 to Nov 2002, and the BD Automated Phoenix 129 System, BD Diagnostic Systems, Sparks, MD from Nov 2002 onward). Inducible 130 resistance to clindamycin was detected by disk diffusion using the D-zone test6,9 . In 131 addition, each strain was examined for the presence of PBP2a by latex agglutination 132 (PBP2' test kit, Oxoid, Hants, UK). Organisms that were negative for PBP2a by latex 133 agglutination but grew on the oxacillin screen plate were sent to the reference laboratory 134 (Central Provincial Health Laboratory, Ontario) for minimum inhibitory concentration 135 (MIC) by agar dilution and polymerase chain reaction (PCR) for the detection of the 136 gene. An isolate was deemed as BORSA if the oxacillin MIC was > 4 µg/ml, 137

  statistics, frequency distributions and percentages were calculated for 161 the outcome variables and other covariates of interest. Normality assumption test was 162 carried out for the continuous variables using the Kolmogorov-Smirnov test. Student's t-163 test or Kruskal-Wallis test (if normality assumption failed) was conducted for continuous 164 variables within the two groups. The Chi-square or Fisher's exact test, as appropriate, 165 was carried out for the dichotomous variables to assess the association with the two 166 groups. To analyze independent risk factors, a multivariate analysis using a logistic 167 regression model was applied. A p-value of <0.05 was considered statistically significant. 168The statistical programme SAS (version 9.1; SAS Institute Inc., Cary, NC) was used.

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  pulmonary exacerbations, hospital admissions or overall antibiotic use for the treatment 191 of acute pulmonary exacerbations. A significantly greater proportion of BORSA infected 192 patients were colonized with MSSA in the year prior (94% vs 25%, p<0.0001). Likewise, 193 of BORSA infected patients received oral antibiotics (cephalexin) 194 (75% vs 25%, p=0.005). 195 Mean duration of therapy among those patients on cephalexin was 337 days (range 30-196 365 days) in the BORSA group and 311 days (range 150-365 days) in the MRSA group; 197 cephalexin treatment was instituted not for pulmonary exacerbation but as treatment for 198 MSSA identified on routine sputum cultures. Furthermore, a significantly greater 199 number of BORSA infected patients had received a course of oral ciprofloxacin (31% vs 200 0%, p=0.043) or were on inhaled antibiotics (tobramycin) (88% vs 44%, p=0.009) in the 201 year prior to the index culture than patients with MRSA. No inter-group difference was 202 detected in any of the other co-colonizing respiratory pathogens. Patients with BORSA 203 strains had significantly more clinic visits in the year prior to first isolate (n=7.94) than 204 patients with mecA-positive strains (n=4.69, p=0.049) although this was not a significant 205 risk factor in the logistic regression model 206 A logistic regression model was applied to determine the independence of 207 epidemiological and clinical variables that were correlated with BORSA infection. 208 Colonization with MSSA and use of oral cephalexin were significantly associated as 68% 209 of patients with MSSA were treated with cephalexin and 81% of patients treated with 210 cephalexin were colonized with MSSA (p<0.05). The use of oral cephalexin (regression 211 coefficient (β)=1.15, SD=0.47, p value=0.015) and inhaled tobramycin (regression 212 coefficient (β)=1.16, SD=0.53, p value=0.028) in the year prior to index culture were 213 significantly and independently associated with BORSA infection. 214 215 Microbiological characteristics of BORSA and MRSA isolates 216 finally, three patients (including a pair of siblings) shared an indistinguishable 239 strain. 240 241 Clinical outcomes of patients 242 To compare the clinical impact of BORSA versus MRSA infection on disease 243 progression, we assessed 99.7 patient-years of follow-up after the index culture with a 244 comparable mean duration of follow up of 3.37 years in the MRSA group and 3.27 years 245 in the BORSA group (p=0.507) (Table

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  . 277Our data would suggest that CF patients may acquire mecA-negative BORSA 278 isolates in a different way. In contrast to MRSA isolates, BORSA isolates had unique 279

	Pediatric Pulmonology
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  MRSA and MSSA colonized patients in a longitudinal 349 fashion, controlling for potential confounding factors and include more extensive PFGE 350 comparisons to determine the evolution of BORSA. In addition, the small number of 351 patients limited the number of variables that could be included in the logistic regression 352 model of risk factors. This study was also not powered to detect differences in clinical 353 outcomes between the groups. It was a single centre study and may not reflect findings in 354 other CF populations. Finally, in a quarter of patients with mecA-negative BORSA, the 355 mechanism of resistance to methicillin remains unknown. 356In conclusion, CF patients with mecA-positive MRSA most commonly acquire 357theirinfection through person to person transmission whereas BORSA is likely 358 preferentially selected out from endogenous MSSA in CF patients due to persistent 359 antibiotic pressure. Prospective studies are needed to examine the prevalence, clinical 360 impact and appropriate management of BORSA in the CF population, as well as the 361 Pfaller MA, Wakefield DS, Hammons GT, McNutt LA, Woolson RF, Helms CM. Implications of acquired oxacillin resistance in the management and control of Staphylococcus aureus infections. J Infect Dis 1988;158(4):702-709. 31. Skinner S, Murray M, Walus T, Karlowsky JA. Failure of cloxacillin in treatment of a patient with borderline oxacillin-resistant Staphylococcus aureus endocarditis. J Clin Microbiol 2009;47(3):859-861. 32. Nelson L, Cockram CS, Lui G, Lam R, Lam E, Lai R, Ip M. Community case of methicillin-resistant Staphylococcus aureus infection. Emerg Infect Dis 2006;12(1):172-174. . PFGE of SmaI restriction fragments of representative MRSA isolates (lanes 2-6). Strains in lanes 3, 4 are indistinguishable and strains in lanes 2, 6 are closely related. Lanes 1,7: lambda ladder. B. Representative BORSA isolates (lanes 1-5) that are unrelated. Lane 6: lambda ladder.

		Pediatric Pulmonology
	362 363 364 365 366 367	F o r P e e r R e v i e w CF should compare BORSA, underlying mechanism of resistance. F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w F o r P e e r R e v i e w 30. Figure legends Figure 1. Massanari RM, F o r F o r P e P e e r e r R e R v e i e w v i e w
	346	It is unclear whether CF patients
	347	infected with BORSA isolates behave more like CF patients infected with MSSA or CF
	348	patients infected with MRSA Future studies assessing the clinical impact of BORSA in
		17 18 21 22

A

Table 1 .

 1 Characteristics of patients with MRSA and BORSA Abbreviations: BMI -body mass index, MSSA-methicillin sensitive Staphylococcus aureus, FEV 1 -Forced expiratory volume in 1 second.

	F o r
	P
	e
	e r
	R
	e
	v i e
	w

Table 1 .

 1 Characteristics of patients with MRSA and BORSA Abbreviations: BMI -body mass index, MSSA-methicillin sensitive Staphylococcus aureus, FEV 1 -Forced expiratory volume in 1 second.

	F o r
	P
	e
	e r
	R
	e
	v i e
	w

Table 2 .

 2 Microbiological characteristics of BORSA isolates †Ampicillin (AMP) susceptible zone size > 29 mm, resistant < 28 mm Amoxicillin clavulanic acid (AMC) susceptible zone size > 20 mm, resistant < 19 mm 9 * D-test negative , AMP-ampicillin, AMC-amoxicillin clavulanic acid ERY-erythromycin, CLI-clindamycin, SXT-trimethoprim-sulphamethoxazole S-susceptible I-intermediate R-resistant

	Isolate	Oxacillin MIC	PBP2a latex	mecA	AMP/AMC	Hyper β-	ERY CLI SXT
		(µg/ml)	agglutination	PCR	zone size	lactamase
					(mm) †	production
	SA 002	8	neg	neg	13/23	Yes	S	S	S
	SA 003	16	neg	neg	12/26	Yes	I	S*	S
	SA 004	4	neg	neg	38/40	No	S	S	S
	SA 006	8	neg	neg	35/36	No	S	S	S
	SA 011	8	neg	neg	18/29	Yes	S	S	S
	SA 016	4	neg	neg	15/26	Yes	S	S	S
	SA 018	8	neg	neg	14/28	Yes	S	S	S
	SA 020	4	neg	neg	17/28	Yes	S	S	S
	SA 021	4	neg	neg	16/26	Yes	S	S	S
	SA 022	8	neg	neg	30/29	No	S	S	S
	SA 023	8	neg	neg	16/21	Yes	R	S*	S
	SA 029	16	neg	neg	10/27	Yes	S	S	S
	SA 034	8	neg	neg	12/21	Yes	S	S	S
	SA 035	4	neg	neg	29/31	No	S	S	S
	SA 036	4	neg	neg	16/23	Yes	S	S	S
	SA 038	8	neg	neg	11/24	Yes	R	S*	S

Table 3 .

 3 Clinical outcomes of patients with MRSA and BORSA

				Page 48 of 49
		MRSA	BORSA	P-
	Clinical outcomes	(n = 15)	(n = 15)	value
	Mean duration (range) of follow up in years	3.37 (0.76-14.8)	3.27 (0.66-5.57)	0.507
	Mean annual change (range) in % predicted FEV1	-5.8 (-33.1 to +3.2) -2.8 (-10.2 to +7.7) 0.770
	Mean no. (range) of exacerbations per year	1.8 (0-5.3)	2.4 (0.3-7.6)	0.443
	Mean no. (range) of hospital admissions per year	0.9 (0-6.6)	1.3 (0-4.7)	0.242
	Mean no. (range) of inpatient days per year	11.5 (0-109.2)	16.1 (0-91.7)	0.226
	Mean no. (range) of IV antibiotic days per year	26.1 (0-224.1)	33.3 (0-200.6)	0.361
	Mean no. (range) of clinic visits per year	7.7 (3.7-37.4)	11.7 (2.8-80.9)	0.272
	No. (%) of patients with persistent infection	12 (80)	8 (53.3)	0.121

83x176mm (150 x 150 DPI)

Microbiological characteristics of BORSA and MRSA isolates

John Wiley & Sons, Inc.

John Wiley & Sons, Inc.Pediatric Pulmonology

Acknowledgements 368

The authors wish to acknowledge the assistance of Dr ML Mulvey at the National 369

Acknowledgements 368

The authors wish to acknowledge the assistance of Dr ML Mulvey at the National 369

John Wiley & Sons, Inc. Pediatric Pulmonology John Wiley & Sons, Inc. Pediatric Pulmonology


Gilles Simon
email: gilles.simon@loria.fr

Tracking-by-Synthesis Using Point Features and Pyramidal Blurring

Keywords: I.2.10 [Vision and Scene Understanding]: Motion-[I.3.7]: Three-Dimensional Graphics and Realism-Color, shading, shadowing, and texture

Tracking-by-synthesis is a promising method for markerless visionbased camera tracking, particularly suitable for Augmented Reality applications. In particular, it is drift-free, viewpoint invariant and easy-to-combine with physical sensors such as GPS and inertial sensors. While edge features have been used succesfully within the tracking-by-synthesis framework, point features have, to our knowledge, still never been used. We believe that this is due to the fact that real-time corner detectors are generally weakly repeatable between a camera image and a rendered texture.

In this paper, we compare the repeatability of commonly used FAST, Harris and SURF interest point detectors across view synthesis. We show that adding depth blur to the rendered texture can drastically improve the repeatability of FAST and Harris corner detectors (up to 100% in our experiments), which can be very helpful, e.g., to make tracking-by-synthesis running on mobile phones. We propose a method for simulating depth blur on the rendered images using a pre-calibrated depth response curve. In order to fulfil the performance requirements, a pyramidal approach is used based on the well-known MIP mapping technique. We also propose an original method for calibrating the depth response curve, which is suitable for any kind of focus lenses and comes for free in terms of programming effort, once the tracking-by-synthesis algorithm has been implemented.

INTRODUCTION

Tracking-by-synthesis is a promising method for camera tracking that is particularly suitable for Augmented Reality (AR) applications [START_REF] Reitmayr | Going out: Robust tracking for outdoor augmented reality[END_REF]. This method is based on the knowledge of a textured 3D model of the scene. Figure 1 illustrates the four steps of one iteration of the algorithm. An approximate camera pose and the camera intrinsic parameters are assumed known at the beginning of the iteration. The approximate pose can be obtained using some physical sensors (GPS, inertial, . . . ) or taken as the pose obtained at previous iteration. From these data, the 3D textured model can be rendered onto the image plane (step 1). Some features (edges, corners, . . . ) are then matched between the rendered image and the camera image (step 2). As the virtual camera pose corresponding to the rendered image is known exactly, features in the rendered image, and therefore corresponding features in the camera image, can be back-projected onto the 3D model (step 3), providing a set of 3D-2D correspondences between the model and the camera image, from which the current pose can be obtained (step 4).

This method has several advantages over other markerless vision-based tracking methods [START_REF] Simon | Markerless tracking using planar structures in the scene[END_REF][START_REF] Lepetit | Fully Automated and Stable Registration for Augmented Reality Applications[END_REF][START_REF] Davison | MonoSLAM: Real-time single camera SLAM[END_REF][START_REF] Klein | Parallel tracking and mapping for small AR workspaces[END_REF][START_REF] Ozuysal | Fast keypoint recognition using random ferns[END_REF]: (i) unlike recursive methods such as planar tracking [START_REF] Simon | Markerless tracking using planar structures in the scene[END_REF] or visual SLAM [START_REF] Davison | MonoSLAM: Real-time single camera SLAM[END_REF][START_REF] Klein | Parallel tracking and mapping for small AR workspaces[END_REF], errors do not accumulate from frame to frame and tracking is driftfree, (ii) as features are matched between close images, there is no need for scale or affine invariant features [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF][START_REF] Mikolajczyk | Scale & affine invariant interest point detectors[END_REF][START_REF] Bay | SURF: Speeded Up Robust Features[END_REF][START_REF] Ozuysal | Fast keypoint recognition using random ferns[END_REF] that are generally slow to compute or storage consuming, (iii) the system automatically performs detail culling and only searches for features that are likely to be visible at the current scale and (iv) merging with other sensor data is natural and can be used to get the initial pose [START_REF] Schall | Global pose estimation using multi-sensor fusion for outdoor augmented reality[END_REF], which is a recurring problem in AR.

Despite these advantages, tracking-by-synthesis has surprisingly not been widely studied in the literature. In [START_REF] Reitmayr | Going out: Robust tracking for outdoor augmented reality[END_REF], a coarse, textured 3D model of an urban environment is used and edgels are extracted from the rendered view using a standard edge detector. These edgels are then projected back onto the model to obtain the 3D coordinates of the sample points. The edge search is conducted using an appearance-based model of the edge instead of a simple edge detector. This method has proven accurate enough for AR requirements. However, edges can be difficult to match on occluding contours, and while these features are often available on fac ¸ades of buildings, these may be more rare on other kind of surfaces like grass-soils, carpets, and so on. Using point features, or combining point features with edgels, within the same framework, may therefore be advantageous in terms of robustness, accuracy and adaptability of the system.

However, up to our knowledge, point features have never been used within the tracking-by-synthesis framework as described above (see section 2.1). As our experiments show, this is probably due to the fact that state-of-the-art real-time corner detectors like FAST [START_REF] Rosten | Machine learning for high-speed corner detection[END_REF] or Harris [START_REF] Harris | A Combined Corner and Edge Detector[END_REF] are weakly repeatable between a camera image and a rendered texture, especially when the texture was captured from a distant viewpoint. Indeed, point features detection can be sensitive to several factors such as texture resampling, depth blur, motion blur and illumination changes between the time when the texture was captured and the time when it is rendered. In this paper, we tackle the depth blur issue and show that simply adding depth blur to the rendered texture can drastically improve the repeatability of corner detection (up to 100% in our experiments).

The main contributions of the current paper are: (i) we com-pare the repeatability of commonly used FAST, Harris and SURF interest point detectors across view synthesis (section 6), (ii) we propose a method for simulating depth blur on the rendered images using a pre-calibrated depth response curve associated with the camera (section 4). In order to fulfil the performance requirements, a pyramidal approach is used based on the well-known MIP mapping technique [START_REF] Williams | Pyramidal parametrics[END_REF]; (iii) we propose an original method for calibrating the depth response curve of the camera (section 5), which is suitable for any kind of focus lenses (fixed, manually adjustable and motorized) and comes for free in terms of programming effort, once the tracking-by-synthesis algorithm has been implemented, (iv) we compare the repeatability of interest point detection across view-and-blur simulation (section 6). We show that adding blur greatly improves the repeatability of FAST and Harris corner detectors, leading to performance similar to SURF but with much faster running times, which can be very helpful to make trackingby-synthesis running on mobile devices.

RELATED WORKS

Tracking-by-Synthesis Using Point Features

Point features have been used in visual panorama tracking where a purely rotational motion is assumed [START_REF] Diverdi | Envisor: Online environment map construction for mixed reality[END_REF][START_REF] Schall | Global pose estimation using multi-sensor fusion for outdoor augmented reality[END_REF][START_REF] Wagner | Real-time panoramic mapping and tracking on mobile phones[END_REF]. A pure rotation does not create a parallax effect and hence the environment can be mapped onto a closed two-dimensional surface, such as a cube, sphere or cylinder. The tracked video stream is used to create the environment map on the fly. Once the map is built, interest points in the camera image are compared against their counterpart in the map. In [START_REF] Schall | Global pose estimation using multi-sensor fusion for outdoor augmented reality[END_REF][START_REF] Wagner | Real-time panoramic mapping and tracking on mobile phones[END_REF], locations of the interest points are selected using the FAST corner detector [START_REF] Rosten | Machine learning for high-speed corner detection[END_REF]. Point correspondences are obtained using normalized cross correlation (NCC) between pixel patches in the camera image and pixel patches warped from the map. In [START_REF] Diverdi | Envisor: Online environment map construction for mixed reality[END_REF], the Shi and Tomasi's good features operator [START_REF] Shi | Good features to track[END_REF] is used to find the candidate points. These are tracked from frame to frame using a pyramidal version of Lucas and Kanade's optical flow algorithm [START_REF] Lucas | An iterative image registration technique with an application to stereo vision[END_REF] and features that are tracked for a number of consecutive frames are added to the map and used to combat drift. Dot products between normalized SURF descriptor vectors [START_REF] Bay | SURF: Speeded Up Robust Features[END_REF] are used to match features in the map with features in the camera images. A similar approach is used in [START_REF] Bleser | Advanced tracking through efficient image processing and visual-inertial sensor fusion[END_REF], though an Inertial Measurement Unit (IMU) is used to (re-)initialize the pose. Morevover, vision measurements and orientation data obtained from the IMU are fused using an Extended Kalman Filter. 6-degrees-of-freedom (6-dof) camera tracking based on pixel patches associated with a CAD model is presented in [START_REF] Lepetit | Fully Automated and Stable Registration for Augmented Reality Applications[END_REF]. During an off-line stage, a small set of images, called keyframes, representing the scene or the object from different viewpoints, is chosen and calibrated. When the projection matrix is known for every keyframe, the system performs interest point detection using the Harris corner detector [START_REF] Harris | A Combined Corner and Edge Detector[END_REF] and back-projects the 2D pixel patches around the points that lie on the object surface. Viewpoint invariant descriptors of these patches are obtained by rerendering each keyframe from different viewpoints around the camera position computed for the keyframe. Template matching is performed using the eigen image methodology.

Actually, all these techniques are tracking-by-synthesis in spirit, but not in letter, because once a feature is added to the map (or keyframe), it is used for the rest of the process. In tracking-bysynthesis as described above, the complete set of model features is replaced at each iteration by the set of features detected in the rendered image. This is the crucial step of this method, which makes it possible to handle large scale and perspective changes of the scene during camera tracking. It may be noticed that strict 6-dof tracking-by-synthesis based on point features has been studied in [START_REF] Schumann | Analysis by Synthesis Techniques for Markerless Tracking[END_REF], though using purely synthetic models instead of image-based textured models.

Rendering With Depth of Field

Rendering with depth of field at interactive frame is an important and challenging task in computer graphics [START_REF] Rokita | Fast generation of depth of field effects in computer graphics[END_REF][START_REF] Barsky | Camera models and optical systems used in computer graphics: part i and ii[END_REF][START_REF] Demers | Depth of field: A survey of techniques[END_REF][START_REF] Kraus | Depth-of-field rendering by pyramidal image processing[END_REF]. Comprehensive surveys of available techniques are provided, for example in [START_REF] Demers | Depth of field: A survey of techniques[END_REF] and [START_REF] Barsky | Camera models and optical systems used in computer graphics: part i and ii[END_REF]. Using MIP mapping for fast generation of depth of field effects was first published by [START_REF] Rokita | Fast generation of depth of field effects in computer graphics[END_REF] and is particularly well suited for GPU-based approaches as noted by [START_REF] Demers | Depth of field: A survey of techniques[END_REF]. However, our problem is easier in some ways, because we only consider planar surfaces. But it is also more difficult in other ways, because the simulated blur must fit as closely as possible the depth blur really observed in the camera images, while only a visual effect is the goal in the referenced papers.

PRELIMINARIES 3.1 Blurring Model

Depth blur is modeled using the ideal thin lens model [START_REF] Pentland | A New Sense for Depth of Field[END_REF], illustrated in figure 2: all the light rays emitted by an object point O in the scene and intercepted by the lens are refracted so that they converge to a point on the conjugate image plane. If the sensor image plane is not aligned with the conjugate image plane, the image point becomes a spot. For a circular diaphragm, the spot is a circle of diameter D called circle of confusion (CoC) or blur circle. Some simple geometric considerations provide the formula:

d o = f d s d s -f -δ |δ | D f L , (1)
where d o is the depth of the object point, f the focal length, L the diameter of the lens and d s the distance between the lens and the sensor image plane. δ is the algebraic distance between the conjugate image plane and the sensor image plane, so that δ /|δ | = 1 or -1 depending on whether the sensor image plane is in front of or (resp.) behind the conjugate image plane.

The blurring effect finally obtained in the digital image depends on the transfer function of the optical system into the spatial domain. Traditionally in computer vision, this function is modeled as a Gaussian, so that:

I b (i, j) = I s (i, j) * G(i, j), ( 2
)
where I b is the blurred image, I s the sharp (focused) image, * the bidimensional convolution product and G the bi-dimensional Gaussian function with spread parameter (standard deviation) σ :

G(i, j) = 1 2πσ 2 exp - i 2 + j 2 2σ 2 . ( 3
)
The relation between σ and the diameter D of the CoC is simply given by:

σ = kD, ( 4
)
where k is a proportionality constant depending on the camera. Introducing equation (4) in equation ( 1) and rearranging shows that the spread parameter σ is piecewise linear in the inverse depth 1/d o of the object point :

σ (d o ) = A 1 d o + B , (5)
where A = kLd s and B = kL(1d s / f ). In practice, parameters k, L, d s and f can not easily be obtained, but calibration methods exist to compute parameters A and B (see section 5).

The depth of the scene points that are in focus on the sensor image plane is called the focus distance. The focus distance depends on the distance d s between the lens and the sensor image plane, and is limited by mechanical constraints. In the following, the curve σ (d o ) obtained for a certain focus distance will be called the depth response curve (DRC) of this focus distance. The red curves in figure 4(b) show the DRCs obtained for different focus distances, using a standard webcam equipped with adjustable focus. Figure 4(b) also shows a horizontal line that corresponds to the maximum permissible CoC, that is the maximum diameter D max of the CoC for which the image is "acceptably" sharp (see section 6). Intersecting this line with the DRCs shows that each focus distance leads to a range of depths of scene points that appear acceptably sharp in the image. This range of depths is called the depth of field (DoF) in the literature. As it is shown in figure 4(b), the DoF increases with the focus distance. We call hyperfocal distance the nearest focus distance at which the DoF extends to infinity, that is at which σ (d o ) < D max for all depths d o greater than a certain depth.

MIP Mapping

MIP mapping is a well-known technique in computer graphics, intended to speed-up texture rendering and reduce aliasing artifacts [START_REF] Williams | Pyramidal parametrics[END_REF]. It is implemented in the OpenGL 3D graphics API and is also available in OpenGL ES, a subset of OpenGL designed for embedded devices such as mobile phones, PDAs and video game consoles. The principle of this technique is as follows. When a twodimensional texture map is applied (mapped) to a polygon, a pyramid of N prefiltered texture maps of decreasing resolutions, called mipmap images, is built. Typically, the original texture is at bottom of the pyramid (level 0) and the image size at level i(1 ≤ i ≤ N -1) is half of the image size at level i -1 in each coordinate direction. When the textured polygon is viewed, the renderer switches to a suitable mipmap image, or interpolate between the two nearest, according to the apparent size of the texture and the perspective distortion.

More precisely, when the polygon is rendered, a pixel in the final image rarely corresponds to a pixel in the original texture image (called a texel): a rendered pixel can be part of a texel (the textel is enlarged), or, conversely, contain several texels (the texels are reduced). Suppose a unit square corresponding to one pixel inside the rendered polygon is warped to the original texture according to the perspective distortion. Let ρ be the size of the largest side of the bounding box of the obtained parallelepiped, and let λ be a real number defined by:

λ = log 2 ρ. ( 6
)
When λ > 0, a texel reduction occurs, and the nearest integer value of λ is the level number of the nearest mipmap image in which the reduced texels can be extracted. The value of λ can also be used to interpolate between the two nearest mipmap images. In our OpenGL-based implementation, a trilinear interpolation is performed between the 2×4 nearest texels in the two nearest views.

PYRAMIDAL BLURRING

In this section, we assume that (i) the intrinsic parameters of the camera are known, (ii) the depth response curve of the camera has been calibrated (see section 5) and the focus is not changed during the application, (iii) a textured planar surface has been identified in one camera image (called the original image in the following) and (iv) the position and orientation of the plane with regard to the camera are known for that frame. In practice, these data can be obtained automatically using a fiducial marker, or interactively using such techniques as those described in [START_REF] Simon | In-Situ 3D Sketching Using a Video Camera as an Interaction and Tracking Device[END_REF].

A pyramid of square blurred images suitable for tracking-bysynthesis is generated from these data using the following process. Let's call image i the image at level i the pyramid and let r i × r i be the dimension of image i. For any i(1 ≤ i ≤ N -1), we take r i = r i-1 /2. The choice of r 0 and N depends on the available memory size of the device and determines the scene depth range that will be properly handled by the algorithm.

Particular case

We first consider the particular case where the following two conditions are satisfied: (i) the scene plane is fronto-parallel to the camera at a depth d ori and (ii) the scene plane is in focus in the original image, that is d s is such that δ = 0 and d o = d ori in equation [START_REF] Barsky | Camera models and optical systems used in computer graphics: part i and ii[END_REF].

In that case, building the image at level i of the pyramid is straightforward: let w × h be the size of the original image and s i the scale applied to that image so that the scaled image is fully contained in image i: s i = r i / max(w, h). As the scene plane is fronto-parallel to the camera, the scaled image can be seen as the image of the plane observed at distance d i = d ori /s i . Then, as the plane is in focus in the original image, the amount of blur that has to be applied to each pixel of image i simply given by (d i ) where σ () is defined in equation [START_REF] Crowley | Fast computation of scale normalised gaussian receptive fields[END_REF]. Actually, in order to apply blur before resampling, a Gaussian blur of standard deviation

σ i = σ (d i )/s i = σ (d ori /s i )/s i
is applied to the original image and the resulting image is resampled using bilinear interpolation.

In order to accelerate this procedure, cascade convolutions can also be applied to the original image [START_REF] Crowley | Fast computation of scale normalised gaussian receptive fields[END_REF], up and down the pyramid starting from the image having minimal σ i . However, as the pyramid is built once and for all at the beginning of the tracking process, we do not necessary have to reach real-time for this process. By contrast, during tracking-by-synthesis, MIP mapping is performed in real-time using this pyramid, providing approximate but (as shown experimentally) realistic non uniform depth blur, without having to compute explicitly per-pixel ray depths.

General case

The two conditions mentioned in section 4.1 may be too restrictive for practical use. For instance, in [START_REF] Simon | In-Situ 3D Sketching Using a Video Camera as an Interaction and Tracking Device[END_REF], the scene is modeled by performing camera rotations at a fixed position and clicking the vertices of the faces through the screen center. The textured planed obtained using this procedure are generally not fronto-parallel. Moreover, using a fixed-focus lens makes it impossible to focus a plane at arbitrary distance. Even an adjustable focus lens has a maximal focus distance which can be smaller than the distance required to acquire, for instance, the whole fac ¸ade of a large building. Our algorithm has therefore to be modified in order to be able to build the mipmap pyramid from a defocused non-fronto-parallel texture.

Without lose of generality, we assume that the camera pose R, t associated with the original image is expressed in a world coordinate system where the equation of the plane is z = 0. Let

K = f 0 u 0 0 a f v 0 0 0 1
be the intrinsic camera matrix, P = K (R|t)

the projection matrix and P the 3 × 3 matrix made of the first two and the last columns of P. P-1 is a planar homography that warps (rectify) the original image to the world plane, up to a scale. Let (x min , y min ) and (x max , y max ) be the lower left and (resp.) upper right corners of the bounding box of the shape obtained when applying P-1 to the boundaries of the plane in the original image. Let S i be a translation/scaling matrix defined by:

S i = r i m   1 0 -x min 0 1/a -y min /a 0 0 m/r i   ,
where m = max (x maxx min , (y maxy min )/a). A rectified image at resolution r i can be built using the planar homography H i given by:

H i = S i P-1 . (7)
Let Q i be the 3 × 3 matrix defined by

Q i = (q 1 |q 2 |q 3 ) = K -1 S i .
The matrix Q i is, up to a scale s, the 3 × 3 submatrix (first two and last column) of the camera pose associated with the rectified image at resolution r i . As 1 s q 1 and 1 s q 2 are columns of a rotation matrix, the scale is s = ||q 1 || = ||q 2 || = r i /(m f ) and the depth associated with all pixels of this image is d i = q 33 /||q 1 ||, that is:

d i = f m r i . (8)
Homography H i and depth d i can now be used to compute the non uniform blur that has to be applied to the original image so that it is transformed to a uniform blur of standard deviation σ (d i ) in the rectified image. Let us consider any pixel p in the original image. If the depth blur around this pixel is approximated by a Gaussian blur of standard deviation σ , then the depth blur around the pixel in the rectified image will be approximated by a Gaussian blur of standard deviation σ w = ρσ , where ρ is the size (as defined in section 3.2) of the warped pixel. As the convolution of two Gaussians of variance σ 2 1 and σ 2 2 results in a Gaussian of variance σ 2 3 = σ 2 1 + σ 2 2 , the variance σ 2 p of the blur to be applied to pixel p is therefore taken as:

σ 2 p = σ (d i ) ρ 2 -σ (d p ) 2 , (9)
where d i is given by equation ( 8), σ () is the DRC of the camera defined in equation ( 5) and d p is the depth of the pixel p, obtained by back-projecting the pixel ray onto the world plane, using the pose corresponding to the original image. The term σ (d p ) in equation ( 9) corresponds to the amount of blur we assume already present in the original image. Equation ( 9) can only be used when the required amount of blur σ (d i )/ρ is greater than the already present amount of blur σ (d p ). In the contrary case, no blur is applied to the original image at pixel p and the amount of blur obtained in the rectified image is higher than it should be. Tackling this issue is out of scope of this paper, and will be discussed in conclusion.

Once each pixel in the original image has been blurred, the resulting image is rectified using H i (a bilinear interpolation is performed). Figure 3 illustrates the blurring procedure at three levels of the pyramid (images are shown before rectification). The red circles are proportional to the amount of blur σ (d p ) already present in the original image and the blue circles to the expected amount of blur σ (d i )/ρ. For the image to be warped at resolution 216 × 216 (d i = 34.5m), all the red circles are inside the blue circles, which means that some amount of blur has to be added in the whole image, with increasing amount of blur from the background to the foreground. For the images to be warped at resolutions 512 × 512 (d i = 17.2m) and 1024 × 1024 (d i = 8.6m) the farthest part of the building is not blurred (blue circles are inside the red circles). The non-blurred region represents about a quarter of the image for r i = 512 and about half of the image for r i = 1024.

CALIBRATION OF THE DEPTH RESPONSE CURVES

methods have been proposed in the depth-fromfocus/defocus literature [START_REF] Pentland | A New Sense for Depth of Field[END_REF][START_REF] Xiong | Depth from Focusing and Defocusing[END_REF] to calibrate the DRC of a camera, that is, to compute the parameters A and B of equation [START_REF] Crowley | Fast computation of scale normalised gaussian receptive fields[END_REF]. Usually, a linear regression is performed on several pairs of values {1/d i , σ i }, obtained by moving the calibration target over different places. The distances between the camera and the calibration target being controlled, the problem amounts to measuring the value of the spatial constant σ i obtained in the resulting images. Different methods have being used, such as measuring the slope of the Laplacian at sharp discontinuities or comparing two images across different apertures e.g. using Fourier transforms [START_REF] Pentland | A New Sense for Depth of Field[END_REF]. When using camera motor systems, equation ( 5) can be arranged in order to put it in terms of motor counts, which are measurable and controllable, and therefore make the calibration of the DRC easier [START_REF] Xiong | Depth from Focusing and Defocusing[END_REF].

Our method differs from the literature in that (i) it can deal with any kind of lenses: fixed focus, manually adjustable focus and motorized focus, and (ii) it is free in terms of programming effort once the tracking-by-synthesis algorithm based on the method described in section 4 has been implemented. In a nutshell, rather than measuring σ i directly in the images, we resynthesize each image i with different amounts of blur and take σ i that provides the nearest image, in terms of matched features, to the real image.

Fixed-Focus Lenses

More precisely, the following procedure is used to compute the DRC of a fixed-focus lens. An image sequence is produced by moving the camera at different distances from a fronto-parallel textured plane containing an ARToolkit marker [START_REF] Kato | Marker Tracking and HMD Calibration for a Video-based Augmented Reality Conferencing System[END_REF]. The first frame of the sequence is assumed in focus and is used for texture mapping with different amounts of blur (typically between 0.05 and 3 with a step of 0.05). The marker is used to obtain that camera-plane distances d i as well as the camera poses used to generate the rendered views of the blurred textures. Harris corners [START_REF] Harris | A Combined Corner and Edge Detector[END_REF] are detected in both the real and the rendered images and feature matching is performed using NCC. For each standard deviation of the Gaussian blur, we count the number of inliers obtained by RANSAC computation of a planar homography, and take for σ i the standard deviation that maximizes this number. As a result, we get a set of pairs {1/d i , σ i , } from which a RANSAC linear fitting is per- formed. Actually, equation ( 5) is piecewise linear because of the absolute value. We therefore use values {1/d i , σ i } when d i ≥ d f and {1/d i , -σ i } when d i < d f , d f being the distance of the focused (first) image of the sequence.

In this procedure, we need a point detector that is not robust against blur, otherwise the number-of-inliers criterion is not discriminating enough to enable determining σ i . That is the reason why we chose to use the Harris corner detector, that has proven not robust against blur in our experiments (see section 6).

Adjustable-Focus Lenses

When using an adjustable-focus lens, we must be able to recompute the parameters A and B of the DRC each time the focus setting of the camera, that is the distance d s in figure 2, is changed. From equation ( 5), we know that

A = Cd s , ( 10
) B = C -A/ f , (11)
where C = kL is a camera constant. Parameters C and f can be estimated by computing the DRC parameters A 1 , B 1 and A 2 , B 2 of two different focus settings of the camera (obtained using the procedure described in section 5.1) and solving a system of two equations and two unknown provided by equation [START_REF] Kato | Marker Tracking and HMD Calibration for a Video-based Augmented Reality Conferencing System[END_REF]. Once C and f are known, it is possible to estimate the values A and B corresponding to any value of d s , using equations ( 10) and [START_REF] Kato | Marker Tracking and HMD Calibration for a Video-based Augmented Reality Conferencing System[END_REF]. Actually, it is usually not possible to get the distance d s directly, but if the focus distance d f between the camera and the scene object on which it is focused is known, writing equation ( 1) with d o = d f and δ = 0 provides:

d s = d f f d f -f . ( 12
)
It is therefore possible to recompute the DRC of the camera each time the focus setting is changed, assuming this change corresponds to a focus on a scene object whose distance to the camera can be estimated. In our implementation, an Artoolkit marker is used to estimate the focus distance, though any other measurement procedure could be used depending on the application context. Moreover, we show in section 6 that the repeatability of point detection can be significantly improved even with approximate focus distances and DRCs.

EXPERIMENTAL RESULTS

All our experiments were performed using a Logitech QuickCam E3500 at resolution 640×480, equipped with a manually adjustable focus lens.

Calibration

As seen above, the calibration of the C and f values of the camera can be done using a single Artoolkit marker and two sequences starting at different focus distances. However, in order to assess the predictive property of the procedure described in section 5.2, we produced three sequences starting at different focus distances (10cm, 27cm, 65cm, resp.) and used three markers of different sizes (2cm, 8cm, 26.5cm, resp.). The small and medium markers were surrounded with postcards put on a table and the large marker was surrounded with posters taped on a closet (see Fig. 6, first two rows). The camera was moved closer and farther from the marker (depth ranges are 7-24cm, 15-67cm, 50-250cm, resp.) and for each sequence, a DRC was obtained using the procedure described in section 5.1. Figure 4(a) shows the pairs of values (1/d i , σ i ) obtained on the small marker sequence as well as the RANSAC fitting. Figure 4(b) shows, in red, the measured DRCs of the three sequences and, in green, the DRCs predicted at each focus distance, using the DRCs of the other two sequences as explained in section 5.2. In blue is represented the DRC corresponding to the maximum focus distance of the camera d f max = 2050mm, which is below the hyperfocal distance if we consider a conventional maximum CoC of 1/1250 of the image width (red horizontal line).

In the following, we will refer as "optimal DRC-prediction parameters" to parameters C and f obtained by solving equation [START_REF] Kato | Marker Tracking and HMD Calibration for a Video-based Augmented Reality Conferencing System[END_REF] with the parameters A and B obtained in the three marker sequences.

Sensitivity against DRC accuracy

Sensitivity of point repeatability against accuracy of the DRC was estimated using a part of the large marker sequence, which is called the Posters sequence in the table 1. Five different DRCs have been tested: DRC 1 is the DRC directly measured from the large marker sequence (see section 6.1), DRC 2 is the DRC predicted at focus distance d f = 65cm, using the prediction parameters obtained from the small and medium marker sequences, DRC 3 is the DRC predicted at focus distance d f , using the optimal DRC-prediction parameters. DRCs 4 and 5 correspond to erroneous DRCs that may be obtained in practical situations, due to the difficulty to visually assess whether the camera image is perfectly focused. Actually, the error that can be obtained is given by the intersection of the maximum-CoC line with the correct DRC, as shown in figure 5(a): at a focus distance of 65cm, we get an image that is visually similar, in term of sharpness, to the images obtained at distances d min = 47cm and d max = 103cm (47-103cm is the depth of field). DRC4 and DRC5 are therefore the DRCs predicted at focus distances d min and (resp.) d max , using the optimal DRC-prediction parameters.

A pyramid of blur is built for each DRC, and RANSAC matching of Harris corners is used for tracking-by-synthesis, according to the procedure described in the introduction of the paper. Table 1 (lines [START_REF] Chen | Efficient extraction of robust image features on mobile devices[END_REF][START_REF] Crowley | Fast computation of scale normalised gaussian receptive fields[END_REF][START_REF] Davison | MonoSLAM: Real-time single camera SLAM[END_REF][START_REF] Demers | Depth of field: A survey of techniques[END_REF][START_REF] Diverdi | Envisor: Online environment map construction for mixed reality[END_REF] shows the mean recall obtained on the whole sequence without blurring and with blurring, as well as the improvement ratio between these two values. The recall of a point detection between two images is defined as the number of points matched (that is repeated) between the two images, divided by the minimum number of points detected in these images. These results show that blurring the rendered texture is always beneficial, even with poorly estimated DRCs. The improvement ratios are between 50 and 60 % for all five DRCs, including the distant DRC 4 and 5. Figure 5(b) shows in more details the recall values obtained over the sequence with DRCs 3, 4 and 5 and without blurring. In order to make this figure more legible, the recall curves are drawn using very thin lines, and superimposed with thick smooth curves obtained by low-pass filtering of the curves in Fourier domain. One can see that the gap between the red curve (without blurring) and the other three curves is relatively high in all images of the sequence.

Comparison of FAST, Harris and SURF across View Synthesis

Tracking-by-synthesis without and with pyramidal blurring was tested on several kinds of scenes, indoor and outdoor, from desktop to building size and with different amounts of texture. Figure 6 shows some snapshot of the used sequences, accompanied with information about the number of frames (from 298 to 796), focus distance (from 27cm to camera limit 2m) and scene depth range (from 10-44cm to 12-30m). The Postcards and Posters sequences are parts of the medium and (resp.) large marker sequences used to calibrate the DRC. They contain a lot of texture information and are easy to track. The Carpet and Wall sequences are much more difficult to track, the former due to the presence of repetitive patterns, the latter due to the rarity of texture information. The House and Building sequences show typical urban fac ¸ades with relatively few texture information.

Tracking is initialized using an Artoolkit marker, and then is performed using the procedure described in the introduction. Point features are matched using NCC between Harris or FAST's pixel patches or SURF's description vectors. Outliers are discarded using a RANSAC computation of the homography between the two set of points. Only one plane is used in each experiment, whose texture is captured in the first image. Just before the process starts, the camera is focused as sharp as possible on the marker by turning the focus ring in the limit of camera capability. When the process starts, the focus distance d f to the marker is computed using the marker, detected in the first image. If d f is below the maximum focus distance d f max , the DRC is computed for d f using the optimal DRC prediction parameters. Otherwise, the DRC corresponding to d f max is taken. It is important to note that the estimate of the DRC may also be done at a different time than just before tracking starts, providing that the focus ring is not changed between the two tasks.

The three interest point detectors that we chose to compare are FAST [START_REF] Rosten | Machine learning for high-speed corner detection[END_REF], Harris [START_REF] Harris | A Combined Corner and Edge Detector[END_REF] and SURF [START_REF] Bay | SURF: Speeded Up Robust Features[END_REF], which are all widely used in vision-based camera tracking systems. FAST and Harris are corner detectors that are fast to compute (implementations exist on mobile phones) but only rotation invariant. SURF is a blob detector which is scale, rotation and almost viewpoint invariant, but slower to compute. Even if an efficient implementation of SURF has been proposed, bringing 30% speed-up to the original algorithm [START_REF] Chen | Efficient extraction of robust image features on mobile devices[END_REF], performance is still not good enough for mobile phones. However, one main advantage of tracking-by-synthesis is that invariance to viewpoint is obtained without need of viewpoint invariant features, due to the fact that the camera and the rendered images to be matched are generally close to each other. It would therefore be very useful to be able to use FAST or Harris in tracking-by-synthesis, providing that the performance of these detectors is similar to that of SURF in term of repeatability across view synthesis.

Actually, this is not the case when no blur is added to the rendered views, as shown in table 1 (a dash is used when tracking failed using the related detector). The mean recall of SURF is always significantly higher than for the other two detectors, except for the Building sequence where it is comparable (though low) to the mean recall obtained by Harris. Actually, this result is not very surprising because it has been shown, e.g. in [START_REF] Juan | A Comparison of SIFT, PCA-SIFT and SURF[END_REF], that SURF is relatively repeatable across blur. Another result shown in table 1 is that FAST seems less resistant to blur than Harris. However, an important result of this paper is that when adding blur to the rendered image, the repeatability of FAST and Harris can be greatly improved. By contrast, the repeatability of SURF does not improve, which is, again, not surprising, for the same reason as just mentioned. As a result, Harris reaches mean recalls that are similar to those of SURF, and even much greater for the Building sequence (though much lower for the Wall sequence). The recall of FAST generally stays significantly below the recall of Harris. Figure 7 shows the recall obtained in all images of the Posters sequence: results are conform, all along the sequence, to the general tendency we just described.

Finally, virtual cube was added in the scene and tracking-bysynthesis was performed using Harris corner detector and pyramidal blurring. The snapshots in figure 6 show that the cube is rendered properly over a variety of viewpoints, which demonstrates that our system performs drift-free tracking under large scale viewpoint and perspective changes, and is therefore suitable for AR applications.

CONCLUSION AND FUTURE WORKS

The results of this work can be considered as a preliminary study to demonstrate that tracking-by-synthesis using point features is viable, and can run at high speed by combining fast corner detection and pyramidal blurring. In particular, we have shown that using Harris corners with image blurring is equivalent, in term of repeatability across view synthesis, to using SURF blobs without blurring. In addition, we have proposed a practical method to calibrate the depth response curve of the optical blur at a given focus distance. This work will be added to our interactive system [START_REF] Simon | In-Situ 3D Sketching Using a Video Camera as an Interaction and Tracking Device[END_REF] that allows online reconstruction of several textured planar surfaces, which are immediately used for camera tracking. Initialization of tracking-bysynthesis using a GPS and an inertial sensor will be tested. Moreover, as mentioned in section 4, a limit of pyramidal blurring is that shaper textures can not be obtained from more blurred textures. However, in an interactive system, we can envision updating the mipmaps on-the-fly as closer views of the textures become available. Nevertheless, this will have to be done carefully if we do not want to introduce drift in the process. Finally, repeatability across view synthesis may be improved by simulating other sources of differences between the camera images and the rendered images, such as illumination changes and motion blur.

Figure 1 :

 1 Figure 1: One iteration of the tracking-by-synthesis algorithm.

Figure 2 :

 2 Figure 2: The ideal thin lens model.

⇒Figure 3 :

 3 Figure 3: Blurring of the first image of the House sequence before perspective rectification for three levels of the mipmap pyramid. Red circles are proportional to the amount of blur already present in the original image, blue circles to the expected amount of blur.

Figure 4 :

 4 Figure 4: Calibration of the DRC prediction parameters. Top to bottom: (a) Linear fitting of pairs of values (1/d i ,σ i ) obtained on the small marker sequence. (b) DRCs of the three calibration sequences (red), predicted DRCs based on the other two DRCs (green) and DRC corresponding to the maximum focus distance of the camera (blue). The red horizontal line corresponds to the estimated maximum CoC.

Figure 5 :

 5 Figure 5: Sensitivity against DRC accuracy. Top to bottom: (a) DRCs obtained at the extremities of the depth of field (DRCs 4 and 5) and at focus distance (DRC 3) of the Poster sequence. (b) Recall values obtained over the Poster sequence using DRCs 3, 4 and 5 and without blurring.

Postcards

  199 frames, focus distance: 272 mm, depth range: 98-444 mm Posters 298 frames, focus distance: 648 mm, depth range: 1230-3421 mm Carpet 680 frames, focus distance: 1528 mm, depth range: 660-3675 mm Wall 252 frames, focus distance: max (2050 mm), depth range: 1603-5681 mm House 796 frames, focus distance: max (2050 mm), depth range: 5736-16115 mm Building 678 frames, focus distance: max (2050 mm), depth range: 12420-30082 mm

Figure 6 :

 6 Figure 6: Snapshots of the sequences used in our experiments.

Figure 7 :

 7 Figure 7: Recall values obtained in the Posters sequence.

Table 1 :

 1 Comparison of the repeatability of FAST, Harris and SURF without and with addition of blur in the rendered images.

							50				Harris without blurring
												Harris with blurring
							45				SURF without blurring SURF with blurring
											FAST without blurring
							40					FAST with blurring
		Point Mean recall (%) Mean recall (%)			35
	Sequence	detector wo blurring	w blurring % impr		30
	Postcards Postcards Postcards	Harris FAST SURF	15.6 12.2 24.0	19.8 9.3 23.9	26.7 -23.9 -0.4	Recall (%)	20 25
	Posters -DRC 1 Harris	17.2	27.3	58.6
	Posters -DRC 2 Harris	17.2	27.1	57.9		15
	Posters -DRC 3 Harris	17.2	27.5	60.0		10
	Posters -DRC 4 Harris	17.2	26.1	51.6
	Posters -DRC 5 Harris	17.2	27.4	59.2		5
	Posters	FAST	9.9	15.1	53.7		0
	Posters	SURF	28.2	26.1	-7.3		0	50	100	150 #frame	200	250	300
	Carpet	Harris	6.5	7.0	7.5
	Carpet	FAST	4.8	5.0	3.8
	Carpet	SURF	-	-	-
	Wall	Harris	3.8	6.1	60.4
	Wall	FAST	-	-	-
	Wall	SURF	15.9	16.1	1.2
	House	Harris	9.6	13.8	43.7
	House	FAST	7.2	13.8	91.7
	House	SURF	11.34	12.0	6.1
	Building	Harris	7.0	14.6	109.0
	Building	FAST	4.9	10.6	119
	Building	SURF	7.6	7.0	-7.4