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Image segmentation

In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different color respect to the same characteristic(s). When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of geometry reconstruction algorithms like marching cubes.

Applications

Some of the practical applications of image segmentation are:

 * Content-based image retrieval
 * Machine vision
 * Medical imaging, and imaging studies in biomedical research, including volume rendered images from computed tomography, magnetic resonance imaging, as well as volume electron microscopy techniques such as FIB-SEM.
 * Locate tumors and other pathologies
 * Measure tissue volumes
 * Diagnosis, study of anatomical structure
 * Surgery planning
 * Virtual surgery simulation
 * Intra-surgery navigation
 * Radiotherapy
 * Object detection
 * Pedestrian detection
 * Face detection
 * Brake light detection
 * Locate objects in satellite images (roads, forests, crops, etc.)
 * Recognition Tasks
 * Face recognition
 * Fingerprint recognition
 * Iris recognition
 * Prohibited Item at Airport security checkpoints
 * Traffic control systems
 * Video surveillance
 * Video object co-segmentation and action localization

Several general-purpose algorithms and techniques have been developed for image segmentation. To be useful, these techniques must typically be combined with a domain's specific knowledge in order to effectively solve the domain's segmentation problems.

Classes of segmentation techniques
There are two classes of segmentation techniques.
 * Classical computer vision approaches
 * AI based techniques

Groups of image segmentation

 * Semantic segmentation is an approach detecting, for every pixel, the belonging class. For example, in a figure with many people, all the pixels belonging to persons will have the same class id and the pixels in the background will be classified as background.
 * Instance segmentation is an approach that identifies, for every pixel, the specific belonging instance of the object. It detects each distinct object of interest in the image. For example, when each person in a figure is segmented as an individual object.
 * Panoptic segmentation combines both semantic and instance segmentation. Like semantic segmentation, panoptic segmentation is an approach that identifies, for every pixel, the belonging class. Moreover, like in instance segmentation, panoptic segmentation distinguishes different instances of the same class.

Thresholding
The simplest method of image segmentation is called the thresholding method. This method is based on a clip-level (or a threshold value) to turn a gray-scale image into a binary image.

The key of this method is to select the threshold value (or values when multiple-levels are selected). Several popular methods are used in industry including the maximum entropy method, balanced histogram thresholding, Otsu's method (maximum variance), and k-means clustering.

Recently, methods have been developed for thresholding computed tomography (CT) images. The key idea is that, unlike Otsu's method, the thresholds are derived from the radiographs instead of the (reconstructed) image.

New methods suggested the usage of multi-dimensional fuzzy rule-based non-linear thresholds. In these works decision over each pixel's membership to a segment is based on multi-dimensional rules derived from fuzzy logic and evolutionary algorithms based on image lighting environment and application.

Clustering methods
The K-means algorithm is an iterative technique that is used to partition an image into K clusters. The basic algorithm is

 * 1) Pick K cluster centers, either randomly or based on some heuristic method, for example K-means++
 * 2) Assign each pixel in the image to the cluster that minimizes the distance between the pixel and the cluster center
 * 3) Re-compute the cluster centers by averaging all of the pixels in the cluster
 * 4) Repeat steps 2 and 3 until convergence is attained (i.e. no pixels change clusters)

In this case, distance is the squared or absolute difference between a pixel and a cluster center. The difference is typically based on pixel color, intensity, texture, and location, or a weighted combination of these factors. K can be selected manually, randomly, or by a heuristic. This algorithm is guaranteed to converge, but it may not return the optimal solution. The quality of the solution depends on the initial set of clusters and the value of K.

The Mean Shift algorithm is a technique that is used to partition an image into an unknown apriori number of clusters. This has the advantage of not having to start with an initial guess of such parameter which makes it a better general solution for more diverse cases.

Motion and interactive segmentation
Motion based segmentation is a technique that relies on motion in the image to perform segmentation.

The idea is simple: look at the differences between a pair of images. Assuming the object of interest is moving, the difference will be exactly that object.

Improving on this idea, Kenney et al. proposed interactive segmentation. They use a robot to poke objects in order to generate the motion signal necessary for motion-based segmentation.

Interactive segmentation follows the interactive perception framework proposed by Dov Katz and Oliver Brock.

Another technique that is based on motion is rigid motion segmentation.

Compression-based methods
Compression based methods postulate that the optimal segmentation is the one that minimizes, over all possible segmentations, the coding length of the data. The connection between these two concepts is that segmentation tries to find patterns in an image and any regularity in the image can be used to compress it. The method describes each segment by its texture and boundary shape. Each of these components is modeled by a probability distribution function and its coding length is computed as follows:

 * 1) The boundary encoding leverages the fact that regions in natural images tend to have a smooth contour. This prior is used by Huffman coding to encode the difference chain code of the contours in an image. Thus, the smoother a boundary is, the shorter coding length it attains.
 * 2) Texture is encoded by lossy compression in a way similar to minimum description length (MDL) principle, but here the length of the data given the model is approximated by the number of samples times the entropy of the model. The texture in each region is modeled by a multivariate normal distribution whose entropy has a closed form expression. An interesting property of this model is that the estimated entropy bounds the true entropy of the data from above. This is because among all distributions with a given mean and covariance, normal distribution has the largest entropy. Thus, the true coding length cannot be more than what the algorithm tries to minimize.

For any given segmentation of an image, this scheme yields the number of bits required to encode that image based on the given segmentation. Thus, among all possible segmentations of an image, the goal is to find the segmentation which produces the shortest coding length. This can be achieved by a simple agglomerative clustering method. The distortion in the lossy compression determines the coarseness of the segmentation and its optimal value may differ for each image. This parameter can be estimated heuristically from the contrast of textures in an image. For example, when the textures in an image are similar, such as in camouflage images, stronger sensitivity and thus lower quantization is required.

Histogram-based methods
Histogram-based methods are very efficient compared to other image segmentation methods because they typically require only one pass through the pixels. In this technique, a histogram is computed from all of the pixels in the image, and the peaks and valleys in the histogram are used to locate the clusters in the image. Color or intensity can be used as the measure.

A refinement of this technique is to recursively apply the histogram-seeking method to clusters in the image in order to divide them into smaller clusters. This operation is repeated with smaller and smaller clusters until no more clusters are formed.

One disadvantage of the histogram-seeking method is that it may be difficult to identify significant peaks and valleys in the image.

Histogram-based approaches can also be quickly adapted to apply to multiple frames, while maintaining their single pass efficiency. The histogram can be done in multiple fashions when multiple frames are considered. The same approach that is taken with one frame can be applied to multiple, and after the results are merged, peaks and valleys that were previously difficult to identify are more likely to be distinguishable. The histogram can also be applied on a per-pixel basis where the resulting information is used to determine the most frequent color for the pixel location. This approach segments based on active objects and a static environment, resulting in a different type of segmentation useful in video tracking.

Edge detection
Edge detection is a well-developed field on its own within image processing. Region boundaries and edges are closely related, since there is often a sharp adjustment in intensity at the region boundaries. Edge detection techniques have therefore been used as the base of another segmentation technique.

The edges identified by edge detection are often disconnected. To segment an object from an image however, one needs closed region boundaries. The desired edges are the boundaries between such objects or spatial-taxons.

Spatial-taxons are information granules, consisting of a crisp pixel region, stationed at abstraction levels within a hierarchical nested scene architecture. They are similar to the Gestalt psychological designation of figure-ground, but are extended to include foreground, object groups, objects and salient object parts. Edge detection methods can be applied to the spatial-taxon region, in the same manner they would be applied to a silhouette. This method is particularly useful when the disconnected edge is part of an illusory contour

Segmentation methods can also be applied to edges obtained from edge detectors. Lindeberg and Li developed an integrated method that segments edges into straight and curved edge segments for parts-based object recognition, based on a minimum description length (MDL) criterion that was optimized by a split-and-merge-like method with candidate breakpoints obtained from complementary junction cues to obtain more likely points at which to consider partitions into different segments.

Isolated Point Detection
The detection of isolated points in an image is a fundamental part of image segmentation. This process primarily depends on the second derivative, indicating the use of the Laplacian operator. The Laplacian of a function $$f(x, y)$$ is given by:

 * $$ \nabla^2 f(x,y) = \frac + \frac $$

The Laplacian operator is employed such that the partial derivatives are derived from a specific equation. The second partial derivative of $$f(x, y)$$ with respect to $$x$$ and $$y$$ are given by:

 * $$ \frac = f(x+1,y) + f(x-1,y) - 2f(x,y) $$

 * $$ \frac = f(x,y+1) + f(x,y-1) - 2f(x,y) $$

These partial derivatives are then used to compute the Laplacian as:

 * $$ \nabla^2 f(x,y) = f(x+1,y) + f(x-1,y) + f(x,y+1) + f(x,y-1) - 4f(x,y) $$

This mathematical expression can be implemented by convolving with an appropriate mask. If we extend this equation to three dimensions (x,y,z), the intensity at each pixel location around a central pixel at (x, y, z) is replaced by their corresponding values. This equation becomes particularly useful when we assume that all pixels have unit spacing along each axis.

A sphere mask has been developed for use with three-dimensional datasets. The sphere mask is designed to use only integer arithmetic during calculations, thereby eliminating the need for floating-point hardware or software.

When applying these concepts to actual images represented as arrays of numbers, we need to consider what happens when we reach an edge or border region. The function $$g(x, y)$$ is defined as:

 * $$ g(x, y) = \begin{cases} 1 & \text{if } |R(x, y)| \geq T \\ 0 & \text{otherwise} \end{cases} $$

This above equation is used to determine whether a point in the image is an isolated point based on the response magnitude $$|R(x, y)|$$ and a threshold value $$T$$. If the response magnitude is greater than or equal to the threshold, the function returns 1, indicating the presence of an isolated point; otherwise, it returns 0. This helps in the effective detection and segmentation of isolated points in the image.

Application of Isolated Point Detection in X-ray Image Processing
The detection of isolated points has significant applications in various fields, including X-ray image processing. For instance, an original X-ray image of a turbine blade can be examined pixel-by-pixel to detect porosity in the upper-right quadrant of the blade. The result of applying an edge detector’s response to this X-ray image can be approximated. This demonstrates the segmentation of isolated points in an image with the aid of single-pixel probes.

Dual clustering method
This method is a combination of three characteristics of the image: partition of the image based on histogram analysis is checked by high compactness of the clusters (objects), and high gradients of their borders. For that purpose two spaces have to be introduced: one space is the one-dimensional histogram of brightness H = H(B); the second space is the dual 3-dimensional space of the original image itself B = B(x, y). The first space allows to measure how compactly the brightness of the image is distributed by calculating a minimal clustering kmin. Threshold brightness T corresponding to kmin defines the binary (black-and-white) image – bitmap b = φ(x, y), where φ(x, y) = 0, if B(x, y) < T, and φ(x, y) = 1, if B(x, y) ≥ T. The bitmap b is an object in dual space. On that bitmap a measure has to be defined reflecting how compact distributed black (or white) pixels are. So, the goal is to find objects with good borders. For all T the measure MDC = G/(k × L) has to be calculated (where k is difference in brightness between the object and the background, L is length of all borders, and G is mean gradient on the borders). Maximum of MDC defines the segmentation.

Region-growing methods
Region-growing methods rely mainly on the assumption that the neighboring pixels within one region have similar values. The common procedure is to compare one pixel with its neighbors. If a similarity criterion is satisfied, the pixel can be set to belong to the same cluster as one or more of its neighbors. The selection of the similarity criterion is significant and the results are influenced by noise in all instances.

The method of Statistical Region Merging (SRM) starts by building the graph of pixels using 4-connectedness with edges weighted by the absolute value of the intensity difference. Initially each pixel forms a single pixel region. SRM then sorts those edges in a priority queue and decides whether or not to merge the current regions belonging to the edge pixels using a statistical predicate.

One region-growing method is the seeded region growing method. This method takes a set of seeds as input along with the image. The seeds mark each of the objects to be segmented. The regions are iteratively grown by comparison of all unallocated neighboring pixels to the regions. The difference between a pixel's intensity value and the region's mean, $$\delta$$, is used as a measure of similarity. The pixel with the smallest difference measured in this way is assigned to the respective region. This process continues until all pixels are assigned to a region. Because seeded region growing requires seeds as additional input, the segmentation results are dependent on the choice of seeds, and noise in the image can cause the seeds to be poorly placed.

Another region-growing method is the unseeded region growing method. It is a modified algorithm that does not require explicit seeds. It starts with a single region $$A_1$$—the pixel chosen here does not markedly influence the final segmentation. At each iteration it considers the neighboring pixels in the same way as seeded region growing. It differs from seeded region growing in that if the minimum $$\delta$$ is less than a predefined threshold $$T$$ then it is added to the respective region $$A_j$$. If not, then the pixel is considered different from all current regions $$A_i$$ and a new region $$A_{n+1}$$ is created with this pixel.

One variant of this technique, proposed by Haralick and Shapiro (1985), is based on pixel intensities. The mean and scatter of the region and the intensity of the candidate pixel are used to compute a test statistic. If the test statistic is sufficiently small, the pixel is added to the region, and the region's mean and scatter are recomputed. Otherwise, the pixel is rejected, and is used to form a new region.

A special region-growing method is called $$\lambda$$-connected segmentation (see also lambda-connectedness). It is based on pixel intensities and neighborhood-linking paths. A degree of connectivity (connectedness) is calculated based on a path that is formed by pixels. For a certain value of $$\lambda$$, two pixels are called $$\lambda$$-connected if there is a path linking those two pixels and the connectedness of this path is at least $$\lambda$$. $$\lambda$$-connectedness is an equivalence relation.

Split-and-merge segmentation is based on a quadtree partition of an image. It is sometimes called quadtree segmentation.

This method starts at the root of the tree that represents the whole image. If it is found non-uniform (not homogeneous), then it is split into four child squares (the splitting process), and so on. If, in contrast, four child squares are homogeneous, they are merged as several connected components (the merging process). The node in the tree is a segmented node. This process continues recursively until no further splits or merges are possible. When a special data structure is involved in the implementation of the algorithm of the method, its time complexity can reach $$O(n\log n)$$, an optimal algorithm of the method.

Partial differential equation-based methods
Using a partial differential equation (PDE)-based method and solving the PDE equation by a numerical scheme, one can segment the image. Curve propagation is a popular technique in this category, with numerous applications to object extraction, object tracking, stereo reconstruction, etc. The central idea is to evolve an initial curve towards the lowest potential of a cost function, where its definition reflects the task to be addressed. As for most inverse problems, the minimization of the cost functional is non-trivial and imposes certain smoothness constraints on the solution, which in the present case can be expressed as geometrical constraints on the evolving curve.

Parametric methods
Lagrangian techniques are based on parameterizing the contour according to some sampling strategy and then evolving each element according to image and internal terms. Such techniques are fast and efficient, however the original "purely parametric" formulation (due to Kass, Witkin and Terzopoulos in 1987 and known as "snakes"), is generally criticized for its limitations regarding the choice of sampling strategy, the internal geometric properties of the curve, topology changes (curve splitting and merging), addressing problems in higher dimensions, etc.. Nowadays, efficient "discretized" formulations have been developed to address these limitations while maintaining high efficiency. In both cases, energy minimization is generally conducted using a steepest-gradient descent, whereby derivatives are computed using, e.g., finite differences.

Level-set methods
The level-set method was initially proposed to track moving interfaces by Dervieux and Thomasset in 1979 and 1981 and was later reinvented by Osher and Sethian in 1988. This has spread across various imaging domains in the late 1990s. It can be used to efficiently address the problem of curve/surface/etc. propagation in an implicit manner. The central idea is to represent the evolving contour using a signed function whose zero corresponds to the actual contour. Then, according to the motion equation of the contour, one can easily derive a similar flow for the implicit surface that when applied to the zero level will reflect the propagation of the contour. The level-set method affords numerous advantages: it is implicit, is parameter-free, provides a direct way to estimate the geometric properties of the evolving structure, allows for change of topology, and is intrinsic. It can be used to define an optimization framework, as proposed by Zhao, Merriman and Osher in 1996. One can conclude that it is a very convenient framework for addressing numerous applications of computer vision and medical image analysis. Research into various level-set data structures has led to very efficient implementations of this method.

Fast marching methods
The fast marching method has been used in image segmentation, and this model has been improved (permitting both positive and negative propagation speeds) in an approach called the generalized fast marching method.

Variational methods
The goal of variational methods is to find a segmentation which is optimal with respect to a specific energy functional. The functionals consist of a data fitting term and a regularizing terms. A classical representative is the Potts model defined for an image $$f$$ by

\operatorname{argmin}_u \gamma \| \nabla u \|_0 + \int (u - f)^2 \, dx. $$ A minimizer $$u^*$$ is a piecewise constant image which has an optimal tradeoff between the squared L2 distance to the given image $$f$$ and the total length of its jump set. The jump set of $$u^*$$ defines a segmentation. The relative weight of the energies is tuned by the parameter $$\gamma >0 $$. The binary variant of the Potts model, i.e., if the range of $$u$$ is restricted to two values, is often called Chan-Vese model. An important generalization is the Mumford-Shah model given by

\operatorname{argmin}_{u, K} \gamma |K| + \mu \int_{K^C} |\nabla u|^2 \, dx + \int (u - f)^2 \, dx. $$ The functional value is the sum of the total length of the segmentation curve $$K$$, the smoothness of the approximation $$u$$, and its distance to the original image $$f$$. The weight of the smoothness penalty is adjusted by $$\mu > 0$$. The Potts model is often called piecewise constant Mumford-Shah model as it can be seen as the degenerate case $$\mu \to \infty$$. The optimization problems are known to be NP-hard in general but near-minimizing strategies work well in practice. Classical algorithms are graduated non-convexity and Ambrosio-Tortorelli approximation.

Graph partitioning methods
Graph partitioning methods are an effective tools for image segmentation since they model the impact of pixel neighborhoods on a given cluster of pixels or pixel, under the assumption of homogeneity in images. In these methods, the image is modeled as a weighted, undirected graph. Usually a pixel or a group of pixels are associated with nodes and edge weights define the (dis)similarity between the neighborhood pixels. The graph (image) is then partitioned according to a criterion designed to model "good" clusters. Each partition of the nodes (pixels) output from these algorithms are considered an object segment in the image; see Segmentation-based object categorization. Some popular algorithms of this category are normalized cuts, random walker, minimum cut, isoperimetric partitioning, minimum spanning tree-based segmentation, and segmentation-based object categorization.

Markov random fields
The application of Markov random fields (MRF) for images was suggested in early 1984 by Geman and Geman. Their strong mathematical foundation and ability to provide a global optimum even when defined on local features proved to be the foundation for novel research in the domain of image analysis, de-noising and segmentation. MRFs are completely characterized by their prior probability distributions, marginal probability distributions, cliques, smoothing constraint as well as criterion for updating values. The criterion for image segmentation using MRFs is restated as finding the labelling scheme which has maximum probability for a given set of features. The broad categories of image segmentation using MRFs are supervised and unsupervised segmentation.

Supervised image segmentation using MRF and MAP
In terms of image segmentation, the function that MRFs seek to maximize is the probability of identifying a labelling scheme given a particular set of features are detected in the image. This is a restatement of the maximum a posteriori estimation method.

The generic algorithm for image segmentation using MAP is given below: 1. Define the neighborhood of each feature (random variable in MRF terms). Generally this includes 1st-order or 2nd-order neighbors.

2. Set initial probabilities $P(f_{i})$> for each feature as 0 or

3. where $f_{i} ∈ Σ$ is the set containing features extracted for pixel $i$ and define an initial set of clusters.

4. Using the training data compute the mean ($μ_{ℓ_{i}}$) and variance ($σ_{ℓ_{i}}|undefined$) for each label. This is termed as class statistics.

5. Compute the marginal distribution for the given labeling scheme $P(f_{i} | ℓ_{i})$ using Bayes' theorem and the class statistics calculated earlier. A Gaussian model is used for the marginal distribution.

\frac 1 {\sigma(\ell_i) \sqrt{2\pi} } e^{ -(f_i-\mu(\ell_i))^2/(2\sigma(\ell_i)^2) } \, d\ell_i $

6. Calculate the probability of each class label given the neighborhood defined previously. Clique potentials are used to model the social impact in labeling.

7. Iterate over new prior probabilities and redefine clusters such that these probabilities are maximized. This is done using a variety of optimization algorithms described below.

8. Stop when probability is maximized and labeling scheme does not change. The calculations can be implemented in log likelihood terms as well.

Optimization algorithms
Each optimization algorithm is an adaptation of models from a variety of fields and they are set apart by their unique cost functions. The common trait of cost functions is to penalize change in pixel value as well as difference in pixel label when compared to labels of neighboring pixels.

Iterated conditional modes/gradient descent
The iterated conditional modes (ICM) algorithm tries to reconstruct the ideal labeling scheme by changing the values of each pixel over each iteration and evaluating the energy of the new labeling scheme using the cost function given below,

 * $$ \alpha(1-\delta(\ell_i-\ell_{\text{initial }i})+ \beta \Sigma_{q \in N(i)}(1 - \delta(\ell_i,\ell_{q(i)})). $$

where $α$ is the penalty for change in pixel label and $β$ is the penalty for difference in label between neighboring pixels and chosen pixel. Here $$N(i)$$ is neighborhood of pixel i and $δ$ is the Kronecker delta function. A major issue with ICM is that, similar to gradient descent, it has a tendency to rest over local maxima and thus not obtain a globally optimal labeling scheme.

Simulated annealing (SA)
Derived as an analogue of annealing in metallurgy, simulated annealing (SA) uses change in pixel label over iterations and estimates the difference in energy of each newly formed graph to the initial data. If the newly formed graph is more profitable, in terms of low energy cost, given by:

 * $$\Delta U = U^\text{new} - U^\text{old}$$

\ell_i = \begin{cases} \ell^\text{new}_i, & \text{if } \Delta U \leq 0 ,\\ \ell^\text{new}_i, & \text{if } \Delta U > 0 \text{ and } \delta < e^{-\Delta U / T}, \ell^\text{old}_i \end{cases} $$

the algorithm selects the newly formed graph. Simulated annealing requires the input of temperature schedules which directly affects the speed of convergence of the system, as well as energy threshold for minimization to occur.

Alternative algorithms
A range of other methods exist for solving simple as well as higher order MRFs. They include Maximization of Posterior Marginal, Multi-scale MAP estimation, Multiple Resolution segmentation and more. Apart from likelihood estimates, graph-cut using maximum flow and other highly constrained graph based methods exist for solving MRFs.

Image segmentation using MRF and expectation–maximization
The expectation–maximization algorithm is utilized to iteratively estimate the a posterior probabilities and distributions of labeling when no training data is available and no estimate of segmentation model can be formed. A general approach is to use histograms to represent the features of an image and proceed as outlined briefly in this three-step algorithm:

1. A random estimate of the model parameters is utilized.

2. E step: Estimate class statistics based on the random segmentation model defined. Using these, compute the conditional probability of belonging to a label given the feature set is calculated using naive Bayes' theorem.

P(\lambda \mid f_i) = \frac{P(f_i \mid \lambda) P(\lambda)}{\Sigma_{\lambda \in \Lambda} P(f_i \mid \lambda) P(\lambda)} $$

Here $$\lambda \in \Lambda$$, the set of all possible labels.

3. M step: The established relevance of a given feature set to a labeling scheme is now used to compute the a priori estimate of a given label in the second part of the algorithm. Since the actual number of total labels is unknown (from a training data set), a hidden estimate of the number of labels given by the user is utilized in computations.

P(\lambda) = \frac{\Sigma_{\lambda \in \Lambda} P(\lambda \mid f_i)}{|\Omega|} $$ where $$\Omega$$ is the set of all possible features.

Disadvantages of MAP and EM based image segmentation

 * 1) Exact MAP estimates cannot be easily computed.
 * 2) Approximate MAP estimates are computationally expensive to calculate.
 * 3) Extension to multi-class labeling degrades performance and increases storage required.
 * 4) Reliable estimation of parameters for EM is required for global optima to be achieved.
 * 5) Based on method of optimization, segmentation may cluster to local minima.

Watershed transformation
The watershed transformation considers the gradient magnitude of an image as a topographic surface. Pixels having the highest gradient magnitude intensities (GMIs) correspond to watershed lines, which represent the region boundaries. Water placed on any pixel enclosed by a common watershed line flows downhill to a common local intensity minimum (LIM). Pixels draining to a common minimum form a catch basin, which represents a segment.

Model-based segmentation
The central assumption of model-based approaches is that the structures of interest have a tendency towards a particular shape. Therefore, one can seek a probabilistic model that characterizes the shape and its variation. When segmenting an image, constraints can be imposed using this model as a prior. Such a task may involve (i) registration of the training examples to a common pose, (ii) probabilistic representation of the variation of the registered samples, and (iii) statistical inference between the model and the image. Other important methods in the literature for model-based segmentation include active shape models and active appearance models.

Multi-scale segmentation
Image segmentations are computed at multiple scales in scale space and sometimes propagated from coarse to fine scales; see scale-space segmentation.

Segmentation criteria can be arbitrarily complex and may take into account global as well as local criteria. A common requirement is that each region must be connected in some sense.

One-dimensional hierarchical signal segmentation
Witkin's seminal work in scale space included the notion that a one-dimensional signal could be unambiguously segmented into regions, with one scale parameter controlling the scale of segmentation.

A key observation is that the zero-crossings of the second derivatives (minima and maxima of the first derivative or slope) of multi-scale-smoothed versions of a signal form a nesting tree, which defines hierarchical relations between segments at different scales. Specifically, slope extrema at coarse scales can be traced back to corresponding features at fine scales. When a slope maximum and slope minimum annihilate each other at a larger scale, the three segments that they separated merge into one segment, thus defining the hierarchy of segments.

Image segmentation and primal sketch
There have been numerous research works in this area, out of which a few have now reached a state where they can be applied either with interactive manual intervention (usually with application to medical imaging) or fully automatically. The following is a brief overview of some of the main research ideas that current approaches are based upon.

The nesting structure that Witkin described is, however, specific for one-dimensional signals and does not trivially transfer to higher-dimensional images. Nevertheless, this general idea has inspired several other authors to investigate coarse-to-fine schemes for image segmentation. Koenderink proposed to study how iso-intensity contours evolve over scales and this approach was investigated in more detail by Lifshitz and Pizer. Unfortunately, however, the intensity of image features changes over scales, which implies that it is hard to trace coarse-scale image features to finer scales using iso-intensity information.

Lindeberg studied the problem of linking local extrema and saddle points over scales, and proposed an image representation called the scale-space primal sketch which makes explicit the relations between structures at different scales, and also makes explicit which image features are stable over large ranges of scale including locally appropriate scales for those. Bergholm proposed to detect edges at coarse scales in scale-space and then trace them back to finer scales with manual choice of both the coarse detection scale and the fine localization scale.

Gauch and Pizer studied the complementary problem of ridges and valleys at multiple scales and developed a tool for interactive image segmentation based on multi-scale watersheds. The use of multi-scale watershed with application to the gradient map has also been investigated by Olsen and Nielsen and been carried over to clinical use by Dam. Vincken et al. proposed a hyperstack for defining probabilistic relations between image structures at different scales. The use of stable image structures over scales has been furthered by Ahuja and his co-workers into a fully automated system. A fully automatic brain segmentation algorithm based on closely related ideas of multi-scale watersheds has been presented by Undeman and Lindeberg and been extensively tested in brain databases.

These ideas for multi-scale image segmentation by linking image structures over scales have also been picked up by Florack and Kuijper. Bijaoui and Rué associate structures detected in scale-space above a minimum noise threshold into an object tree which spans multiple scales and corresponds to a kind of feature in the original signal. Extracted features are accurately reconstructed using an iterative conjugate gradient matrix method.

Semi-automatic segmentation
In one kind of segmentation, the user outlines the region of interest with the mouse clicks and algorithms are applied so that the path that best fits the edge of the image is shown.

Techniques like SIOX, Livewire, Intelligent Scissors or IT-SNAPS are used in this kind of segmentation. In an alternative kind of semi-automatic segmentation, the algorithms return a spatial-taxon (i.e. foreground, object-group, object or object-part) selected by the user or designated via prior probabilities.

Trainable segmentation
Most of the aforementioned segmentation methods are based only on color information of pixels in the image. Humans use much more knowledge when performing image segmentation, but implementing this knowledge would cost considerable human engineering and computational time, and would require a huge domain knowledge database which does not currently exist. Trainable segmentation methods, such as neural network segmentation, overcome these issues by modeling the domain knowledge from a dataset of labeled pixels.

An image segmentation neural network can process small areas of an image to extract simple features such as edges. Another neural network, or any decision-making mechanism, can then combine these features to label the areas of an image accordingly. A type of network designed this way is the Kohonen map.

Pulse-coupled neural networks (PCNNs) are neural models proposed by modeling a cat's visual cortex and developed for high-performance biomimetic image processing. In 1989, Reinhard Eckhorn introduced a neural model to emulate the mechanism of a cat's visual cortex. The Eckhorn model provided a simple and effective tool for studying the visual cortex of small mammals, and was soon recognized as having significant application potential in image processing. In 1994, the Eckhorn model was adapted to be an image processing algorithm by John L. Johnson, who termed this algorithm Pulse-Coupled Neural Network. Over the past decade, PCNNs have been utilized for a variety of image processing applications, including: image segmentation, feature generation, face extraction, motion detection, region growing, noise reduction, and so on. A PCNN is a two-dimensional neural network. Each neuron in the network corresponds to one pixel in an input image, receiving its corresponding pixel's color information (e.g. intensity) as an external stimulus. Each neuron also connects with its neighboring neurons, receiving local stimuli from them. The external and local stimuli are combined in an internal activation system, which accumulates the stimuli until it exceeds a dynamic threshold, resulting in a pulse output. Through iterative computation, PCNN neurons produce temporal series of pulse outputs. The temporal series of pulse outputs contain information of input images and can be utilized for various image processing applications, such as image segmentation and feature generation. Compared with conventional image processing means, PCNNs have several significant merits, including robustness against noise, independence of geometric variations in input patterns, capability of bridging minor intensity variations in input patterns, etc.

U-Net is a convolutional neural network which takes as input an image and outputs a label for each pixel. U-Net initially was developed to detect cell boundaries in biomedical images. U-Net follows classical autoencoder architecture, as such it contains two sub-structures. The encoder structure follows the traditional stack of convolutional and max pooling layers to increase the receptive field as it goes through the layers. It is used to capture the context in the image. The decoder structure utilizes transposed convolution layers for upsampling so that the end dimensions are close to that of the input image. Skip connections are placed between convolution and transposed convolution layers of the same shape in order to preserve details that would have been lost otherwise.

In addition to pixel-level semantic segmentation tasks which assign a given category to each pixel, modern segmentation applications include instance-level semantic segmentation tasks in which each individual in a given category must be uniquely identified, as well as panoptic segmentation tasks which combines these two tasks to provide a more complete scene segmentation.

Segmentation of related images and videos
Related images such as a photo album or a sequence of video frames often contain semantically similar objects and scenes, therefore it is often beneficial to exploit such correlations. The task of simultaneously segmenting scenes from related images or video frames is termed co-segmentation, which is typically used in human action localization. Unlike conventional bounding box-based object detection, human action localization methods provide finer-grained results, typically per-image segmentation masks delineating the human object of interest and its action category (e.g., Segment-Tube ). Techniques such as dynamic Markov Networks, CNN and LSTM are often employed to exploit the inter-frame correlations.

Other methods
There are many other methods of segmentation like multispectral segmentation or connectivity-based segmentation based on DTI images.

title: Image segmentation


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Philadelphia City Hall

Philadelphia City Hall is the seat of the municipal government of the City of Philadelphia in the U.S. state of Pennsylvania. Built in the ornate Second Empire style, City Hall houses the chambers of the Philadelphia City Council and the offices of the Mayor of Philadelphia.

This building is also a courthouse, serving as the seat of the First Judicial District of Pennsylvania. It houses the Civil Trial and Orphans' Court Divisions of the Court of Common Pleas of Philadelphia County. It also houses the Philadelphia facilities for the Supreme Court of Pennsylvania (which also holds session and accepts filings in Harrisburg and Pittsburgh).

Built using brick, white marble and limestone, Philadelphia City Hall is the world's largest free-standing masonry building and was the world's tallest habitable building upon its completion in 1894. It was designated as a National Historic Landmark in 1976; in 2006, it was also named a National Historic Civil Engineering Landmark by the American Society of Civil Engineers.

History and description

The building was designed by Scottish-born architect John McArthur Jr. and Thomas Ustick Walter in the Second Empire style, and was constructed from 1871 to 1901 at a cost of $24 million. City Hall's tower was completed by 1894, although the interior was not finished until 1901. Designed to be the world's tallest building, it was surpassed during construction by the Washington Monument, the Eiffel Tower and the Mole Antonelliana. The Mole Antonelliana was a few feet taller and was the tallest masonry (i.e. without the use of steel) building in the world until 1953. In that year a storm caused the spire to collapse and Philadelphia City Hall became the tallest masonry building in the world (excluding monuments). Upon completion of its tower in 1894, it became the world's tallest habitable building. It was also the first secular building to have this distinction, as all previous world's tallest buildings were religious structures, including European cathedrals and—for the previous 3,800 years—the Great Pyramid of Giza; even the Mole Antonelliana was supposed to be a religious building—a synagogue—but then received a different use.

The location chosen was one of the five center city urban park squares dedicated by William Penn, that geometrically is the center to the other four squares within Center City renamed as Penn Square. City Hall is a masonry building whose weight is borne by granite and brick walls up to 22 ft thick. The principal exterior materials are limestone, granite, and marble. The original design called for virtually no sculpture. The stonemason William Struthers and sculptor Alexander Milne Calder were responsible for the more than 250 sculptures, capturing artists, educators, and engineers who embodied American ideals and contributed to this country's genius. The final construction cost was $24 million.

At 548 ft, including the statue of city founder William Penn atop its tower, City Hall was the tallest habitable building in the world from 1894 to 1908. It remained the tallest in Pennsylvania until it was surpassed in 1932 by the Gulf Tower in Pittsburgh; it is now the 16th tallest. It was the tallest in Philadelphia until 1986 when the construction of One Liberty Place surpassed it, ending the informal gentlemen's agreement that had limited the height of buildings in the city to no higher than the Penn statue.

It was constructed over the time span from 1871 to 1901 and includes 700 rooms dedicated for uses of various governmental operations. The building structure used over 88 million bricks and thousands of tons of marble and granite. With almost 700 rooms, City Hall is the largest municipal building in the United States and one of the largest in the world. The building houses three branches of government: the city's executive branch (the Mayor's Office), its legislature (the Philadelphia City Council), and a substantial portion of the judicial activity in the city (the Civil Division and Orphan's Court of the Pennsylvania Court of Common Pleas for the First Judicial District are housed there, as well as chambers for some criminal judges and some judges of the Philadelphia Municipal Court).

It was the tallest clock tower in the world when it was completed; it was surpassed by the Metropolitan Life Insurance Company Tower in 1912, and is currently the 5th tallest building of this type. The tower features a clock face on each side that is 26 ft in diameter. The clock faces are larger in diameter than those on Big Ben which measure 23 ft. City Hall's clock was designed by Warren Johnson and built in 1898. The 1937 Philadelphia Guide noted that "shortly after the clock was installed the city inaugurated a custom which still continues. Every evening at three minutes of nine the tower lights are turned off, and then turned on again on the hour. This enables those within observation distance, though unable to see the hands, to set their timepieces. There are four bronze eagles, each weighing three tons with 12 ft wingspans, perched above the tower's four clocks.

City Hall's observation deck is located directly below the base of the statue, about 500 ft above street level. Once enclosed with chain-link fencing, the observation deck is now enclosed by glass. It is reached in a 6-person elevator whose glass panels allow visitors to see the interior of the iron superstructure that caps the tower and supports the statuary and clocks. Stairs within the tower are only used for emergency exit. The ornamentation of the tower has been simplified; the huge garlands that festooned the top panels of the tower were removed.

In the 1950s, the city council investigated tearing down City Hall for a new building elsewhere, but abandoned the plan due to the high cost of the demolition.

Beginning in 1992, Philadelphia City Hall underwent a comprehensive exterior restoration, planned and supervised by the Historical Preservation Studio of Vitetta Architects & Engineers, headed by renowned historical preservation architect Hyman Myers. The majority of the restoration was completed by 2007, although some work has continued, including the installation of four new ornamental courtyard gates, based on an original architectural sketch, in December 2015.

The building was voted 21st on the American Institute of Architects' list of Americans' 150 favorite U.S. structures in 2007.

William Penn statue
The building is topped by a 37 ft bronze statue weighing 53348 lb of city founder William Penn, one of 250 sculptures created by Alexander Milne Calder that adorn the building inside and out. The statue was cast at the Tacony Iron Works of Northeast Philadelphia and hoisted to the top of the tower in fourteen sections in 1894. The statue is the tallest atop any building in the world.

Despite its lofty perch, the city has mandated that the statue be cleaned about every ten years to remove corrosion and reduce deterioration due to weathering, with the latest cleaning done in May 2017. Penn's statue is hollow, and a narrow access tunnel through it leads to a 22 in hatch atop the hat.

Calder wished the statue to face south so that its face would be lit by the sun most of the day, the better to reveal the details of his work. The statue actually faces northeast, towards Penn Treaty Park in the Fishtown section of the city, which commemorates the site where Penn signed a treaty with the local Native American tribe. Pennsbury Manor, Penn's country home in Bucks County, is also located to the northeast.

By the terms of a gentlemen's agreement that forbade any structure from rising above the hat on the Penn statue, Philadelphia City Hall remained the tallest building in the city until it was surpassed by One Liberty Place in 1986. The abrogation of this agreement supposedly brought a curse onto local professional sports teams. Twice during the 1990s, the statue was partially clothed in a major league sports team's uniform when they were in contention for a championship: a Phillies cap in 1993 and a Flyers jersey in 1997—both teams lost. The supposed curse ended 22 years later when the Phillies won the 2008 World Series, a year and four months after a Penn statuette had been affixed to the final beam of the Comcast Center during its topping out ceremony in June 2007. Another Penn statuette was placed on the topmost beam of the Comcast Technology Center in November 2017, and the Eagles won the Super Bowl a few months later.

Centre Square
City Hall is situated on land that was reserved as a public square upon the city's founding in 1682. Originally known as Centre Square—later renamed Penn Square —it was used for public gatherings until the construction of City Hall began in 1871. Centre Square was one of the five original squares of Philadelphia laid out on the city grid by William Penn. The square had been located at the geographic center of Penn's city plan, but the Act of Consolidation in 1854 created the much larger and coterminous city and county of Philadelphia. Though no longer at the exact center of the city, the square remains situated in the center of the historic area between the Delaware and Schuylkill rivers; an area which is now called Center City.

Penn had intended that Centre Square be the central focus point where the major public buildings would be located, including those for government, religion, and education, as well as the central marketplace. However, the Delaware riverfront would remain the de facto economic and social heart of the city for more than a century.

Film appearances
City Hall has been a filming location for several motion pictures including Rocky (1976), Blow Out (1981), Trading Places (1983), Philadelphia (1993), 12 Monkeys (1995), National Treasure (2004), Transformers: Revenge of the Fallen (2009), and Limitless (2011).

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Philadelphia City Hall


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Wolfdog

A wolfdog is a canine produced by the mating of a domestic dog (Canis familiaris) with a gray wolf (Canis lupus), eastern wolf (Canis lycaon), red wolf (Canis rufus), or Ethiopian wolf (Canis simensis) to produce a hybrid.

Admixture
There are a range of experts who believe that they can tell the difference between a wolf, a dog, and a wolfdog, but they have been proven to be incorrect when providing their evidence before courts of law.

Admixture between domestic dogs and other subspecies of gray wolves are the most common wolfdogs since dogs and gray wolves are considered the same species, are genetically very close and have shared vast portions of their ranges for millennia. Such admixture in the wild have been detected in many populations scattered throughout Europe and North America, usually occurring in areas where wolf populations have declined from human impacts and persecutions.

At the same time, wolfdogs are also often bred in captivity for various purposes. Admixture of dogs and two other North American wolf species have also occurred historically in the wild, although it is often difficult for biologists to discriminate the dog genes in the eastern timber and red wolves from the gray wolf genes also present in these wolf species due to their historical overlaps with North American gray wolves as well as with coyotes, both of which have introgressed into the eastern timber and red wolf gene pools. At the same time, because many isolated populations of the three wolf species in North America have also mixed with coyotes in the wild, it has been speculated by some biologists that some of the coywolf hybrids in the northeastern third of the continent may also have both coydogs and wolfdogs in their gene pool. Hybrids between dogs and Ethiopian wolves discovered in the Ethiopian Highlands likely originated from past interactions between free-roaming feral dogs and Ethiopian wolves living in isolated areas.

Recognized wolfdog breeds by the FCI are the Czechoslovakian Wolfdog and the Saarloos Wolfdog.

History
Whole genome sequencing has been used to study gene flow between wild and domestic species. There is evidence of widespread gene-flow from dogs into wolf populations, and very few deliberate crossings of wolves with dogs, such as the Saarloos Wolfdog. However, the global dog population forms a genetic cluster with little evidence for gene flow from wolves into dogs. Ancient DNA shows that dogs from Europe over 5,000 years ago also show little evidence of interbreeding with wild canids.

Prehistoric wolfdogs
A 1982 study of canine skulls from Wyoming dating back 10,000 years ago identified some that match the morphology of wolfdogs. This study was rebutted as not providing convincing evidence four years later.

Teotihuacan wolfdogs
In 2010, archeologists found the remains of wolf-dogs in a warrior's burial in Mexico's central valley that date about two thousand years ago, therefore what was once thought as coyotes depicted in Teotihuacan civilization art are being re-examined.

New World black wolves
Genetic research revealed that wolves with black pelts owe their distinctive coloration to a mutation that entered the wolf population through admixture with the domestic dog. Adolph Murie was among the first wolf biologists to speculate that the wide color variation in wolves was due to interbreeding with dogs;

"'I suppose that some of the variability exhibited in these wolves could have resulted from crossings in the wild with dogs. Such crosses in the wild have been reported and the wolf in captivity crosses readily with dogs. Some years ago at Circle, Alaska, a wolf hung around the settlement for some time and some of the dogs were seen with it. The people thought that the wolf was a female attracted to the dogs during the breeding period. However, considerable variability is probably inherent in the species, enough perhaps to account for the variations noted in the park and in skins examined. The amount of crossing with dogs has probably not been sufficient to alter much the genetic composition of the wolf population.'"

In 2008 it was discovered that a gene mutation responsible for the protein beta-defensin 3 is responsible for the black coat color in dogs. The same mutation was responsible for black wolves in North America and the Italian Apennines, with the mutation having arisen in dogs 13,000 to 120,000 years ago, with a preferred date of 47,000 years ago after comparing large sections of wolf, dog, and coyote genomes. Robert K. Wayne, a canine evolutionary biologist, stated that he believed that dogs were the first to have the mutation. He further stated that even if it originally arose in Eurasian wolves, it was passed on to dogs who, soon after their arrival, brought it to the New World and then passed it to wolves and coyotes. Black wolves with recent dog ancestry tend to retain black pigment longer as they age.

North American gray wolf-domestic dog admixture
As of 1999 in the United States, over 100,000 wolfdogs exist. In first-generation wolfdogs, gray wolves are most often crossed with wolf-like dogs (such as German Shepherd Dogs, Siberian Huskies, and Alaskan Malamutes) for an appearance most appealing to owners desiring an exotic pet.

Documented breeding

The first record of wolfdog breeding in Great Britain comes from the year 1766 when what is thought was a male wolf mated with a dog identified in the language of the day as a "Pomeranian", although it may have differed from the modern Pomeranian breed. The union resulted in a litter of nine pups. Wolfdogs were occasionally purchased by English noblemen, who viewed them as a scientific curiosity. Wolfdogs were popular exhibits in British menageries and zoos.

Six breeds of dog exist that acknowledge a significant amount of recent wolf-dog admixture in their creation. One breed is the "wolamute", a.k.a. "malawolf", a cross between an Alaskan Malamute and a timber wolf. Four breeds were the result of intentional crosses with German Shepherd Dogs and have distinguishing characteristics of appearance that may reflect the varying subspecies of wolf that contributed to their foundation stock. Other, more unusual crosses have occurred; recent experiments in Germany were conducted in the crossing of wolves and Poodles. The intent behind creating the breeds has ranged widely from simply the desire for a recognizable companion high-content wolfdog to professional military working dogs.

The Saarloos Wolfdog

In 1932, Dutch breeder Leendert Saarloos crossed a male German Shepherd dog with a female European wolf. He then bred the female offspring back with the male German Shepherd Dog, creating the Saarloos wolfdog. The breed was created to be a hardy, self reliant companion and house dog. The Dutch Kennel Club recognized the breed in 1975. To honor its creator they changed the name to "Saarloos Wolfdog". In 1981, the breed was recognized by the Fédération Cynologique Internationale (FCI).

The Czechoslovakian Wolfdog
In the 1950s, the Czechoslovakian Wolfdog was also created to work on border patrol in the countries now known as Slovakia and the Czech Republic. It was originally bred from lines of German Shepherd Dogs with Carpathian grey wolves. It was officially recognized as a national breed in Czechoslovakia in 1982, and later was recognized by the Fédération Cynologique Internationale, the American Kennel Club's Foundation Stock Service and the United Kennel Club, and today is used in agility, obedience, search and rescue, police work, therapy work, and herding in Europe and the United States.

Volkosob
The Volkosob (Волкособ, plural: Волкособы) was initially developed in the 1990s after the fall of the Soviet Union. Russian border guards wanted a dog that would possess the trainability and pack mentality of the German Shepherd, combined with the strength, superior senses and cold-resistance of a wild wolf, able to cope in the harsh conditions of the vast Russian borders. In 2000, a Caspian Steppe Wolf, noted for being unusually friendly and cooperative towards humans, was bred with German Shepherds of an East European Shepherd line, until an F3 generation was standardised. Unlike the previous hybrids, the Volkosob was the only breed that was an effective border guardian as they are renowned for not being too shy.

Livestock guardian dogs
A 2014 study found that 20% of wolves and 37% of dogs shared the same mitochondrial haplotypes in Georgia. More than 13% of the studied wolves had detectable dog ancestry and more than 10% of the dogs had detectable wolf ancestry. The results of the study suggest that admixture between wolves and dogs is a common event in the areas where large livestock guardian dogs are held in a traditional way, and that gene flow between dogs and gray wolves was an important force influencing gene pool of dogs for millennia since early domestication events.

Wolfdogs in the wild
Hybridization between wolves and dogs typically occurs when the wolf population is under strong hunting pressure and its structure is disrupted due to a high number of free-ranging dogs. Wolves typically display aggressiveness toward dogs, but a wolf can change its behaviour and become playful or submissive when it becomes socially isolated.

Admixture in the wild usually occurs near human habitations where wolf density is low and dogs are common. However, there were several reported cases of wolfdogs in areas with normal wolf densities in the former Soviet Union. Wild wolfdogs were occasionally hunted by European aristocracy, and were termed lycisca to distinguish them from common wolves. Noted historic cases (such as the Beast of Gévaudan) of large wolves that were abnormally aggressive toward humans, may be attributable to wolf-dog mating. In Europe, unintentional mating of dogs and wild wolves have been confirmed in some populations through genetic testing. As the survival of some Continental European wolf packs is severely threatened, scientists fear that the creation of wolfdog populations in the wild is a threat to the continued existence of European wolf populations. However, extensive admixture between wolf and dog is not supported by morphological evidence, and analyses of mtDNA sequences have revealed that such mating are rare.

In 1997, during the Mexican Wolf Arizona Reintroduction, controversy arose when a captive pack at Carlsbad designated for release was found by Roy McBride to be largely composed of wolf-dogs, who had captured many wolves for the recovery programme in the 1970s. Though staff initially argued that the animals' odd appearance was due to captivity and diet, it was later decided to euthanise them.

In 2018, a study compared the sequences of 61,000 single-nucleotide polymorphisms (mutations) taken from across the genome of grey wolves. The study indicated that there exists individual wolves of dog-wolf ancestry in most of the wolf populations of Eurasia, but less so in North America. The admixture has been occurring across different time scales and was not a recent event. Low-level admixture did not reduce the wolf distinctiveness.

In 2019, in the Osogovo mountainous region along the border between Bulgaria and North Macedonia a putative grey wolf was recorded by camera to be living with a pack of 10 feral dogs, and by its behaviour and phenotype was assumed to be a wolf-dog hybrid.

Breed-specific legislation
The wolfdog has been the center of controversy for much of its history, and most breed-specific legislation is either the result of the animal's perceived danger or its categorization as protected native wildlife. The Humane Society of the United States, the RSPCA, Ottawa Humane Society, the Dogs Trust, and the Wolf Specialist Group of the IUCN Species Survival Commission consider wolfdogs to be wild animals and therefore unsuitable as pets, and support an international ban on the private possession, breeding, and sale of wolfdogs.

According to the National Wolfdog Alliance, 40 U.S. states effectively forbid the ownership, breeding, and importation of wolfdogs, while others impose some form of regulation upon ownership. In Canada, the provinces of Alberta, Manitoba, Newfoundland, and Prince Edward Island prohibit wolfdogs as pets. Most European nations have either outlawed the animal entirely or put restrictions on ownership. Wolfdogs were among the breeds banned from the U.S. Marine Corps base at Camp Pendleton and elsewhere after a fatal dog attack by a pit bull on a child.

Description

The physical characteristics of an animal created by breeding a wolf to a dog are not predictable, similar to that of mixed-breed dogs. In many cases the resulting adult wolfdog may be larger than either of its parents due to the genetic phenomenon of heterosis (commonly known as hybrid vigor). Breeding experiments in Germany with poodles and wolves, and later on with the resulting wolfdogs showed unrestricted fertility, mating via free choice and no significant problems of communication (even after a few generations). However, the offspring of poodles with either coyotes and jackals, all showed a decrease in fertility, significant communication problems, and an increase of genetic diseases after three generations of interbreeding between the hybrids. The researchers therefore concluded that domestic dogs and wolves are the same species.

Wolfdogs display a wide variety of appearances, ranging from a resemblance to dogs without wolf blood to animals that are often mistaken for full-blooded wolves. A lengthy study by DEFRA and the RSPCA found several examples of misrepresentation by breeders and indeterminate levels of actual wolf pedigree in many animals sold as wolfdogs. The report noted that uneducated citizens misidentify dogs with wolf-like appearance as wolfdogs. Wolfdogs tend to have somewhat smaller heads than pure wolves, with larger, pointier ears that lack the dense fur commonly seen in those of wolves. Fur markings also tend to be very distinctive and not well blended. Black-colored wolfdogs tend to retain black pigment longer as they age, compared to black wolves. In some cases, the presence of dewclaws on the hind feet is considered a useful, but not absolute, indicator of dog gene contamination in wild wolves. Dewclaws are the vestigial first toes, which are common on the hind legs of domestic dogs but thought absent from pure wolves, which only have four hind toes.

Observations on wild wolfdogs in the former Soviet Union indicate that in a wild state these may form larger packs than pure wolves, and have greater endurance when chasing prey. High wolf-content wolfdogs typically have longer canine teeth than dogs of comparable size, with some officers in the South African Defence Force commenting that the animals are capable of biting through the toughest padding "like a knife through butter".

Tests undertaken in the Perm Institute of Interior Forces in Russia demonstrated that high wolf-content wolfdogs took 15–20 seconds to track down a target in training sessions, whereas ordinary police dogs took 3–4 minutes. The scientific evidence to support the claims by wolfdog researchers is minimal, and more research has been called for.

Health
Wolfdogs are generally said to be naturally healthy animals, and are affected by fewer inherited diseases than most breeds of dog. Wolfdogs are usually healthier than either parent due to heterosis. There is some controversy over the effectiveness of the standard dog/cat rabies vaccine on a wolfdog. The USDA has not to date approved any rabies vaccine for use in wolfdogs, though they do recommend an off-label use of the vaccine. Wolfdog owners and breeders purport that the lack of official approval is a political move to prevent condoning wolfdog ownership.

Temperament and behavior
Wolfdogs are a mixture of genetic traits, which results in less predictable behavior patterns compared to either the wolf or dog. The adult behavior of wolfdog pups also cannot be predicted with comparable certainty to dog pups, even in third-generation pups produced by wolfdog mating with dogs or from the behavior of the parent animals. Thus, though the behavior of a single individual wolfdog may be predictable, the behavior of the type as a whole is not.

Due to the variability inherent to their admixture, whether a wolf–dog cross should be considered more dangerous than a dog depends on behavior specific to the individual alone rather than to wolfdogs as a group.

The view that aggressive characteristics are inherently a part of wolfdog temperament has been contested in recent years by wolfdog breeders and other advocates of wolfdogs as pets.

In popular culture

 * Jed was a Canadian timber wolf-Alaskan Malamute and animal actor, known for his roles in such movies as White Fang (1991), White Fang 2: Myth of the White Wolf (1994), The Journey of Natty Gann (1985), and The Thing (1982); he was born in 1977 and died in June 1995, aged 18.
 * Balto, Aleu and Kodi are fictitious wolfdogs in the animated films Balto (1995), Balto II: Wolf Quest (2002) and Balto III: Wings of Change (2004), respectively. The actual Balto was not a wolfdog but instead a Siberian Husky.
 * White Fang is the titular character of Jack London's eponymous 1906 novel, first serialized in Outing magazine, that details the wild wolfdog's journey to domestication in the Yukon Territory and the Northwest Territories during the 1890s Klondike Gold Rush.
 * The Wolf Dog (1933) is an American Pre-Code Mascot film serial starring Frankie Darro and Rin Tin Tin Jr.
 * Wolf Dog (1958), also known as A Boy and His Dog, is a Northwestern movie, directed and produced by Sam Newfield, and produced by Regal Films
 * Wolfdogs Magazine self-describes as a progressive "community based publication for wolfdog enthusiasts".

title: Wolfdog


text:
Talk:Chipping Barnet

Barnet etymology
§ 1 in the History section states:

The town's name derives from an ancient settlement, recorded as Barneto c. 1070, Barnet 1197, La Barnette 1248, that is 'the land cleared by burning', from Old English bærnet, referring to the clearing of this once densely forested area in early times.

§ 5 in the same section states:

The name of the town appears in early deeds as 'Bergnet' – the Saxon word 'Bergnet'[4] meant a little hill (monticulus). Barnet's elevated position is also indicated in one of its alternative names ('High Barnet')

So which is it? A burnt clearing, or a little hill? Redpaul1 (talk) 09:33, 16 March 2017 (UTC)

 * I've deleted the fanciful 18th-century derivation from 'Bergnet' and cited the Oxford Dictionary of London Place-Names for 'land cleared by burning' - but had to rewrite the sentence since someone had copied it direct from the Oxford Dictionary without citing the source, which was plagiarism according to Wikipedia policy! John O'London (talk) 11:13, 9 March 2020 (UTC)

title: Talk:Chipping Barnet


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Charlotte spiral

The Charlotte (pronounced shar-lot) spiral, also known as the candle stick or fadeout, is a figure skating spiral. The skater bends forward and glides on one leg with the other one lifted into the air. The skater's torso is upright, but during the Charlotte, the skater's torso is as close to the grounded foot as possible. When performed well, the skater's legs are almost in a straight vertical split position. The Charlotte requires great flexibility and balance.

A Charlotte can be performed either forward or backward. It is usually performed backwards, although some skaters have performed it forwards in competition.

The Charlotte is named for German skater Charlotte Oelschlägel, who first performed the move in the early 1900s. Sonia Henie performed the move in some of her films, as well as her Olympic program in 1936. Michelle Kwan and Sasha Cohen picked up the move several decades later.

The position is rarely performed by men, notable exceptions being John Curry, Rohene Ward, Michael Christian Martinez and a few others.

title: Charlotte spiral


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Claude Jutra

Claude Jutra (March 11, 1930 – November 5, 1986) was a Canadian actor, film director, and screenwriter.

The Prix Jutra, and the Academy of Canadian Cinema and Television's Claude Jutra Award, were named in his honour because of his importance in Quebec cinema history. The awards were renamed in 2016 following the publication of allegations that he had sexually abused children during his lifetime, as were streets named for him.

Life and career
Jutra was born and raised in Montreal, Quebec as Claude Jutras. His father, Albert Jutras, was a radiologist and a director of the Collège des médecins du Québec. He made the short films Dément du lac Jean-Jeunes and Perpetual Movement (Mouvement perpétuel) before graduating from the Université de Montréal with a degree in medicine, but turned to filmmaking instead of medical practice after completing his degree. He studied theatre in Montréal (1952–53) and wrote his first original Quebec television play (L'Ecole de la peur) in 1953, and a television series, Images en boite, in 1954.

He went to work at the National Film Board of Canada in 1956 where he trained in all facets of filmmaking, although his first film for the NFB, Trio-Brio, was permanently lost when the organization moved its head office from Ottawa to Montreal. As a filmmaker, he dropped the s from his surname, a common Québécois surname, because the Jutra spelling was more distinctive. In 1958 he went to France and Africa to work with noted French filmmaker, Jean Rouch.

Claude Jutra's career in film, in a certain sense, paralleled Quebec cinema itself. Beginning as an amateur at a time when there was no Quebec cinema, he participated in (and sometime led) several of the principal developments in Quebec: traditional documentaries and docudramas at the NFB; the germinal period of direct cinema; the first steps in the early 1960s toward independent film production; and later trend toward large-budget features, such as Kamouraska, a box office failure now revealed to be a major work in the canon of Canadian cinema. Overall, his work had a consistent thematic pattern: young people and the (often traumatic) passage from innocence to knowledge, a theme that has nostalgic overtones.

With financing and production provided by the NFB, Jutra co-wrote and directed the 1971 film Mon oncle Antoine, which until very recently has been ranked as the best Canadian movie ever made. As well as directing several cinema vérité shorts such as Wrestling and The Devil's Toy (1966 film), he also co-directed with Norman McLaren and starred in the innovative pixilation Academy Award-nominated short A Chairy Tale.

He was offered the Order of Canada in 1972 but declined because he was a Quebec separatist. In 1984, he was awarded the Prix Albert-Tessier, given to individuals for an outstanding career in Québec cinema.

Death
Jutra was diagnosed with early-onset Alzheimer's disease in the early 1980s. He was reported missing on November 5, 1986. His body was found in the St. Lawrence River in April 1987, with a note in his pocket reading "Je m'appelle Claude Jutra" ("My name is Claude Jutra"); an autopsy later confirmed drowning as his cause of death.

Controversy
In 2016, 30 years after Jutra's death, journalist Yves Lever wrote in the book Claude Jutra, biographie and claimed that Jutra was a pederast. Lever said that "one of Jutra's victims was under 14 years old." He also maintained that Jutra's proclivities were known by many people in the industry, "but nobody made a big deal out of it." Lever's allegations were not officially proven, as no victims publicly came forward; however, in the wake of the allegations, Québec Cinéma held an emergency meeting to discuss changing the name of the Prix Jutra.

On February 17, 2016, La Presse published an interview with an alleged victim of Jutra, who requested to remain anonymous, relating sexual contact ranging from embrace to oral sex from the time the victim was 6 to 16. On the same day and based on the information in the same article, the Minister of Culture of Quebec Hélène David asked Cinéma Québec to remove the name Jutra from its prizes recognizing cinematic achievements in Quebec, which they did. She also mandated the Commission de toponymie (Quebec Toponymy Commission), a sub-agency of Office québécois de la langue française which reports to the Minister of Culture, to assemble a list of all streets and public places in the province bearing the name Jutra, as the first step in a process of renaming them. On the same day, Montreal mayor Denis Coderre announced that the city would remove Jutra's name from streets and parks in its jurisdiction.

Of the controversy, The Globe and Mail wrote: "Few legendary figures have fallen so quickly and so completely. Merely 24 hours after the official publication of the first explosive allegation of child abuse against the Canadian cinematic pioneer, the film industry and governments started scrubbing the name Claude Jutra from every trophy, park and street."

As actor

 * A Chairy Tale - 1957
 * À tout prendre - 1964
 * The Rape of a Sweet Young Girl (Le viol d'une jeune fille douce) - 1968
 * Préambule - 1969
 * Act of the Heart - 1970
 * We Are Far from the Sun (On est loin du soleil) - 1971
 * Mon oncle Antoine -1971
 * Love on the Nose - 1974 (TV)
 * Pour le meilleur et pour le pire - 1975
 * La fleur aux dents - 1976
 * Arts Cuba - 1977 (voice)
 * Two Solitudes - 1978
 * Riel - 1979 (TV)
 * Till Death Do Us Part - 1982 (TV)
 * The Tin Flute (Bonheur d'occasion) - 1983

As director
Jutra made his debut as a director with Le dément du lac Jean-Jeunes - it explored themes that remained throughout his work, a nostalgia for childhood, madness, and troubled waters.

His collaboration with Michel Brault began at this early period. Mouvement perpétuel was influenced by Jean Cocteau's Le Sang d'un poète. L'École de la peur (1953) was the first television film made in Quebec. Toward the end of the 1950s, he moved to France, and François Truffaut, who became a friend, asked him to direct Anna la Bonne (1959), a Cocteau scenario. In 1960, Jutra returned to Canada.

Fiction

 * Le dément du lac Jean-Jeunes - 1948, short film
 * Perpetual Movement (Mouvement perpétuel) - 1949, short film
 * L'école de la peur - 1953, TV movie
 * Pierrot des bois - 1956, short film
 * A Chairy Tale - 1957 short animated film co-directed with Norman McLaren
 * Les mains nettes - 1958
 * Anna la bonne - 1959, short film from a scenario by Jean Cocteau
 * À tout prendre - 1963
 * Marie-Christine - 1970, short film
 * Mon oncle Antoine - 1971
 * Kamouraska - 1973
 * For Better or For Worse (Pour le meilleur et pour le pire) - 1975
 * Ada - 1977, TV movie created for anthology series For the Record
 * Dreamspeaker - 1977, TV movie created for anthology series For the Record
 * Seer Was Here - 1977, TV movie created for anthology series For the Record
 * The Patriarch - 1978, episode of The Beachcombers
 * The Wordsmith - 1979, TV movie
 * Surfacing - 1980
 * By Design - 1981
 * Un petit bonhomme de chemin - 1982, unfinished film
 * The Dame in Colour (La dame en couleurs) - 1985
 * My Father, My Rival - 1985, TV movie

Documentaries

 * Au service de l'esprit troublé (Short film Co-Directed with Stanley Jackson, 1955)
 * Chantons maintenant (Short film, 1956)
 * Jeunesses musicales (Short film, 1956)
 * Rondo de Mozart (Short film, 1957)
 * Félix Leclerc, troubadour (Short film, 1958)
 * Fred Barry, comédien (Short film, 1959)
 * Le Niger, jeune république (Short film, 1961)
 * La Lutte (Wrestling) (Short film Co-Directed with Marcel Carrière, Claude Fournier and Michel Brault, 1961)
 * Québec-U.S.A. ou l'invasion pacifique (Short film Co-Directed with Michel Brault, 1962)
 * Petit discours de la méthode (Short film Co-Directed with Pierre Patry, 1963)
 * Ciné boum (Short film Co-Directed with Robert Russell, 1964)
 * Comment savoir... (1966)
 * The Devil's Toy (Short film, 1966)
 * Au coeur de la ville (Short film, 1969)
 * Wow (1969)
 * Québec fête juin '75 (Co-Directed with Jean-Claude Labrecque, 1976)
 * Arts Cuba (Short film, 1977)

Awards and nominations
Canadian Film Awards

 * (1950) Canadian Film Award, Amateur for Perpetual Movement (Mouvement perpétuel)
 * (1958) Canadian Film Award, Arts and Experimental for A Chairy Tale (shared with Norman McLaren)
 * (1964) Canadian Film Award, Feature Film for À tout prendre (shared with Robert Hershorn)
 * (1971) Canadian Film Award, Direction (Feature) for Mon Oncle Antoine
 * (1977) Canadian Film Award, Direction (Non-Feature) for Dreamspeaker

Genie Awards

 * (1986) Genie Award, Best Achievement in Direction for The Dame in Colour (La Dame en couleurs)
 * (1986) Genie Award, Best Screenplay for The Dame in Colour (La Dame en couleurs)

Film about Claude Jutra
Jutra's close friend, filmmaker Paule Baillargeon, directed the feature documentary Claude Jutra: An Unfinished Story in 2002.

Marie-Josée Saint-Pierre used a mix of archival footage of Jutra with animation to create the 2014 short documentary film Jutra.

Legacy
George Lucas stated in an interview that he shot the first Star Wars film like a Jutra documentary.

Besides the film awards (Claude Jutra Award and Jutra Award), a number of places bear or bore Jutra's name, all found in Quebec:

 * Claude-Jutra Crescent, Montreal
 * Claude-Jutra Park, Montreal
 * Place Claude-Jutra, Repentigny, Quebec
 * Claude-Jutra Street, Blainville, Quebec
 * Claude-Jutra Street, Saint-Bruno-de-Montarville
 * Claude-Jutra Street, Quebec City
 * Claude-Jutra Street, Levis, Quebec
 * Jutra Street, Candiac

Multiple parks and streets later were renamed or scheduled to be renamed after the pederasty controversy in 2016.

title: Claude Jutra