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Computer vision

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input to the retina in the human analog) into descriptions of world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, 3D point clouds from LiDaR sensors, or medical scanning devices. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems.

Sub-domains of computer vision include scene reconstruction, object detection, event detection, activity recognition, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration.

Adopting computer vision technology might be painstaking for organizations as there is no single-point solution for it. Very few companies provide a unified and distributed platform or Operating System where computer vision applications can be easily deployed and managed.

Definition
Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. "Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding." As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems. Machine vision refers to a systems engineering discipline, especially in the context of factory automation. In more recent times, the terms computer vision and machine vision have converged to a greater degree.

History
In the late 1960s, computer vision began at universities that were pioneering artificial intelligence. It was meant to mimic the human visual system as a stepping stone to endowing robots with intelligent behavior. In 1966, it was believed that this could be achieved through an undergraduate summer project, by attaching a camera to a computer and having it "describe what it saw".

What distinguished computer vision from the prevalent field of digital image processing at that time was a desire to extract three-dimensional structure from images with the goal of achieving full scene understanding. Studies in the 1970s formed the early foundations for many of the computer vision algorithms that exist today, including extraction of edges from images, labeling of lines, non-polyhedral and polyhedral modeling, representation of objects as interconnections of smaller structures, optical flow, and motion estimation.

The next decade saw studies based on more rigorous mathematical analysis and quantitative aspects of computer vision. These include the concept of scale-space, the inference of shape from various cues such as shading, texture and focus, and contour models known as snakes. Researchers also realized that many of these mathematical concepts could be treated within the same optimization framework as regularization and Markov random fields. By the 1990s, some of the previous research topics became more active than others. Research in projective 3-D reconstructions led to better understanding of camera calibration. With the advent of optimization methods for camera calibration, it was realized that a lot of the ideas were already explored in bundle adjustment theory from the field of photogrammetry. This led to methods for sparse 3-D reconstructions of scenes from multiple images. Progress was made on the dense stereo correspondence problem and further multi-view stereo techniques. At the same time, variations of graph cut were used to solve image segmentation. This decade also marked the first time statistical learning techniques were used in practice to recognize faces in images (see Eigenface). Toward the end of the 1990s, a significant change came about with the increased interaction between the fields of computer graphics and computer vision. This included image-based rendering, image morphing, view interpolation, panoramic image stitching and early light-field rendering.

Recent work has seen the resurgence of feature-based methods used in conjunction with machine learning techniques and complex optimization frameworks. The advancement of Deep Learning techniques has brought further life to the field of computer vision. The accuracy of deep learning algorithms on several benchmark computer vision data sets for tasks ranging from classification, segmentation and optical flow has surpassed prior methods.

Solid-state physics
Solid-state physics is another field that is closely related to computer vision. Most computer vision systems rely on image sensors, which detect electromagnetic radiation, which is typically in the form of either visible, infrared or ultraviolet light. The sensors are designed using quantum physics. The process by which light interacts with surfaces is explained using physics. Physics explains the behavior of optics which are a core part of most imaging systems. Sophisticated image sensors even require quantum mechanics to provide a complete understanding of the image formation process. Also, various measurement problems in physics can be addressed using computer vision, for example, motion in fluids.

Neurobiology
Neurobiology has greatly influenced the development of computer vision algorithms. Over the last century, there has been an extensive study of eyes, neurons, and brain structures devoted to the processing of visual stimuli in both humans and various animals. This has led to a coarse yet convoluted description of how natural vision systems operate in order to solve certain vision-related tasks. These results have led to a sub-field within computer vision where artificial systems are designed to mimic the processing and behavior of biological systems at different levels of complexity. Also, some of the learning-based methods developed within computer vision (e.g. neural net and deep learning based image and feature analysis and classification) have their background in neurobiology. The Neocognitron, a neural network developed in the 1970s by Kunihiko Fukushima, is an early example of computer vision taking direct inspiration from neurobiology, specifically the primary visual cortex.

Some strands of computer vision research are closely related to the study of biological vision—indeed, just as many strands of AI research are closely tied with research into human intelligence and the use of stored knowledge to interpret, integrate, and utilize visual information. The field of biological vision studies and models the physiological processes behind visual perception in humans and other animals. Computer vision, on the other hand, develops and describes the algorithms implemented in software and hardware behind artificial vision systems. An interdisciplinary exchange between biological and computer vision has proven fruitful for both fields.

Signal processing
Yet another field related to computer vision is signal processing. Many methods for processing one-variable signals, typically temporal signals, can be extended in a natural way to the processing of two-variable signals or multi-variable signals in computer vision. However, because of the specific nature of images, there are many methods developed within computer vision that have no counterpart in the processing of one-variable signals. Together with the multi-dimensionality of the signal, this defines a subfield in signal processing as a part of computer vision.

Robotic navigation
Robot navigation sometimes deals with autonomous path planning or deliberation for robotic systems to navigate through an environment. A detailed understanding of these environments is required to navigate through them. Information about the environment could be provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot.

Other fields
Besides the above-mentioned views on computer vision, many of the related research topics can also be studied from a purely mathematical point of view. For example, many methods in computer vision are based on statistics, optimization or geometry. Finally, a significant part of the field is devoted to the implementation aspect of computer vision; how existing methods can be realized in various combinations of software and hardware, or how these methods can be modified in order to gain processing speed without losing too much performance. Computer vision is also used in fashion eCommerce, inventory management, patent search, furniture, and the beauty industry.

Distinctions
The fields most closely related to computer vision are image processing, image analysis and machine vision. There is a significant overlap in the range of techniques and applications that these cover. This implies that the basic techniques that are used and developed in these fields are similar, something which can be interpreted as there is only one field with different names. On the other hand, it appears to be necessary for research groups, scientific journals, conferences, and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented. In image processing, the input is an image and the output is an image as well, whereas in computer vision, an image or a video is taken as an input and the output could be an enhanced image, an understanding of the content of an image or even behavior of a computer system based on such understanding.

Computer graphics produces image data from 3D models, and computer vision often produces 3D models from image data. There is also a trend towards a combination of the two disciplines, e.g., as explored in augmented reality.

The following characterizations appear relevant but should not be taken as universally accepted:

 * Image processing and image analysis tend to focus on 2D images, how to transform one image to another, e.g., by pixel-wise operations such as contrast enhancement, local operations such as edge extraction or noise removal, or geometrical transformations such as rotating the image. This characterization implies that image processing/analysis neither requires assumptions nor produces interpretations about the image content.
 * Computer vision includes 3D analysis from 2D images. This analyzes the 3D scene projected onto one or several images, e.g., how to reconstruct structure or other information about the 3D scene from one or several images. Computer vision often relies on more or less complex assumptions about the scene depicted in an image.
 * Machine vision is the process of applying a range of technologies and methods to provide imaging-based automatic inspection, process control, and robot guidance in industrial applications. Machine vision tends to focus on applications, mainly in manufacturing, e.g., vision-based robots and systems for vision-based inspection, measurement, or picking (such as bin picking ). This implies that image sensor technologies and control theory often are integrated with the processing of image data to control a robot and that real-time processing is emphasized by means of efficient implementations in hardware and software. It also implies that external conditions such as lighting can be and are often more controlled in machine vision than they are in general computer vision, which can enable the use of different algorithms.
 * There is also a field called imaging which primarily focuses on the process of producing images, but sometimes also deals with the processing and analysis of images. For example, medical imaging includes substantial work on the analysis of image data in medical applications. Progress in convolutional neural networks (CNNs) has improved the accurate detection of disease in medical images, particularly in cardiology, pathology, dermatology, and radiology.
 * Finally, pattern recognition is a field that uses various methods to extract information from signals in general, mainly based on statistical approaches and artificial neural networks. A significant part of this field is devoted to applying these methods to image data.

Photogrammetry also overlaps with computer vision, e.g., stereophotogrammetry vs. computer stereo vision.

Applications
Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications. In many computer-vision applications, computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. Examples of applications of computer vision include systems for:

 * Automatic inspection, e.g., in manufacturing applications;
 * Assisting humans in identification tasks, e.g., a species identification system;
 * Controlling processes, e.g., an industrial robot;
 * Detecting events, e.g., for visual surveillance or people counting, e.g., in the restaurant industry;
 * Interaction, e.g., as the input to a device for computer-human interaction;
 * Modeling objects or environments, e.g., medical image analysis or topographical modeling;
 * Navigation, e.g., by an autonomous vehicle or mobile robot;
 * Organizing information, e.g., for indexing databases of images and image sequences.
 * Tracking surfaces or planes in 3D coordinates for allowing Augmented Reality experiences.

Medicine
One of the most prominent application fields is medical computer vision, or medical image processing, characterized by the extraction of information from image data to diagnose a patient. An example of this is the detection of tumours, arteriosclerosis or other malign changes, and a variety of dental pathologies; measurements of organ dimensions, blood flow, etc. are another example. It also supports medical research by providing new information: e.g., about the structure of the brain or the quality of medical treatments. Applications of computer vision in the medical area also include enhancement of images interpreted by humans—ultrasonic images or X-ray images, for example—to reduce the influence of noise.

Machine vision
A second application area in computer vision is in industry, sometimes called machine vision, where information is extracted for the purpose of supporting a production process. One example is quality control where details or final products are being automatically inspected in order to find defects. One of the most prevalent fields for such inspection is the Wafer industry in which every single Wafer is being measured and inspected for inaccuracies or defects to prevent a computer chip from coming to market in an unusable manner. Another example is a measurement of the position and orientation of details to be picked up by a robot arm. Machine vision is also heavily used in the agricultural processes to remove undesirable foodstuff from bulk material, a process called optical sorting.

Military
Military applications are probably one of the largest areas of computer vision. The obvious examples are the detection of enemy soldiers or vehicles and missile guidance. More advanced systems for missile guidance send the missile to an area rather than a specific target, and target selection is made when the missile reaches the area based on locally acquired image data. Modern military concepts, such as "battlefield awareness", imply that various sensors, including image sensors, provide a rich set of information about a combat scene that can be used to support strategic decisions. In this case, automatic processing of the data is used to reduce complexity and to fuse information from multiple sensors to increase reliability.

Autonomous vehicles
One of the newer application areas is autonomous vehicles, which include submersibles, land-based vehicles (small robots with wheels, cars, or trucks), aerial vehicles, and unmanned aerial vehicles (UAV). The level of autonomy ranges from fully autonomous (unmanned) vehicles to vehicles where computer-vision-based systems support a driver or a pilot in various situations. Fully autonomous vehicles typically use computer vision for navigation, e.g., for knowing where they are or mapping their environment (SLAM), for detecting obstacles. It can also be used for detecting certain task-specific events, e.g., a UAV looking for forest fires. Examples of supporting systems are obstacle warning systems in cars, cameras and LiDAR sensors in vehicles, and systems for autonomous landing of aircraft. Several car manufacturers have demonstrated systems for autonomous driving of cars. There are ample examples of military autonomous vehicles ranging from advanced missiles to UAVs for recon missions or missile guidance. Space exploration is already being made with autonomous vehicles using computer vision, e.g., NASA's Curiosity and CNSA's Yutu-2 rover.

Tactile feedback
Materials such as rubber and silicon are being used to create sensors that allow for applications such as detecting microundulations and calibrating robotic hands. Rubber can be used in order to create a mold that can be placed over a finger, inside of this mold would be multiple strain gauges. The finger mold and sensors could then be placed on top of a small sheet of rubber containing an array of rubber pins. A user can then wear the finger mold and trace a surface. A computer can then read the data from the strain gauges and measure if one or more of the pins are being pushed upward. If a pin is being pushed upward then the computer can recognize this as an imperfection in the surface. This sort of technology is useful in order to receive accurate data on imperfections on a very large surface. Another variation of this finger mold sensor are sensors that contain a camera suspended in silicon. The silicon forms a dome around the outside of the camera and embedded in the silicon are point markers that are equally spaced. These cameras can then be placed on devices such as robotic hands in order to allow the computer to receive highly accurate tactile data.

Other application areas include:

 * Support of visual effects creation for cinema and broadcast, e.g., camera tracking (match moving).
 * Surveillance.
 * Driver drowsiness detection
 * Tracking and counting organisms in the biological sciences

Typical tasks
Each of the application areas described above employ a range of computer vision tasks; more or less well-defined measurement problems or processing problems, which can be solved using a variety of methods. Some examples of typical computer vision tasks are presented below.

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

Recognition
The classical problem in computer vision, image processing, and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity. Different varieties of recognition problem are described in the literature.

 * Object recognition (also called object classification) – one or several pre-specified or learned objects or object classes can be recognized, usually together with their 2D positions in the image or 3D poses in the scene. Blippar, Google Goggles, and LikeThat provide stand-alone programs that illustrate this functionality.
 * Identification – an individual instance of an object is recognized. Examples include identification of a specific person's face or fingerprint, identification of handwritten digits, or the identification of a specific vehicle.
 * Detection – the image data are scanned for specific objects along with their locations. Examples include the detection of an obstacle in the car's field of view and possible abnormal cells or tissues in medical images or the detection of a vehicle in an automatic road toll system. Detection based on relatively simple and fast computations is sometimes used for finding smaller regions of interesting image data which can be further analyzed by more computationally demanding techniques to produce a correct interpretation.

Currently, the best algorithms for such tasks are based on convolutional neural networks. An illustration of their capabilities is given by the ImageNet Large Scale Visual Recognition Challenge; this is a benchmark in object classification and detection, with millions of images and 1000 object classes used in the competition. Performance of convolutional neural networks on the ImageNet tests is now close to that of humans. The best algorithms still struggle with objects that are small or thin, such as a small ant on the stem of a flower or a person holding a quill in their hand. They also have trouble with images that have been distorted with filters (an increasingly common phenomenon with modern digital cameras). By contrast, those kinds of images rarely trouble humans. Humans, however, tend to have trouble with other issues. For example, they are not good at classifying objects into fine-grained classes, such as the particular breed of dog or species of bird, whereas convolutional neural networks handle this with ease.

Several specialized tasks based on recognition exist, such as:

 * Content-based image retrieval – finding all images in a larger set of images which have a specific content. The content can be specified in different ways, for example in terms of similarity relative to a target image (give me all images similar to image X) by utilizing reverse image search techniques, or in terms of high-level search criteria given as text input (give me all images which contain many houses, are taken during winter and have no cars in them).
 * Pose estimation – estimating the position or orientation of a specific object relative to the camera. An example application for this technique would be assisting a robot arm in retrieving objects from a conveyor belt in an assembly line situation or picking parts from a bin.
 * Optical character recognition (OCR) – identifying characters in images of printed or handwritten text, usually with a view to encoding the text in a format more amenable to editing or indexing (e.g. ASCII). A related task is reading of 2D codes such as data matrix and QR codes.
 * Facial recognition –  a technology that enables the matching of faces in digital images or video frames to a face database, which is now widely used for mobile phone facelock, smart door locking, etc.
 * Emotion recognition – a subset of facial recognition, emotion recognition refers to the process of classifying human emotions. Psychologists caution, however, that internal emotions cannot be reliably detected from faces.
 * Shape Recognition Technology (SRT) in people counter systems differentiating human beings (head and shoulder patterns) from objects.
 * Human activity recognition - deals with recognizing the activity from a series of video frames, such as, if the person is picking up an object or walking.

Motion analysis
Several tasks relate to motion estimation, where an image sequence is processed to produce an estimate of the velocity either at each points in the image or in the 3D scene or even of the camera that produces the images. Examples of such tasks are:

 * Egomotion – determining the 3D rigid motion (rotation and translation) of the camera from an image sequence produced by the camera.
 * Tracking – following the movements of a (usually) smaller set of interest points or objects (e.g., vehicles, objects, humans or other organisms ) in the image sequence. This has vast industry applications as most high-running machinery can be monitored in this way.
 * Optical flow – to determine, for each point in the image, how that point is moving relative to the image plane, i.e., its apparent motion. This motion is a result of both how the corresponding 3D point is moving in the scene and how the camera is moving relative to the scene.

Scene reconstruction
Given one or (typically) more images of a scene, or a video, scene reconstruction aims at computing a 3D model of the scene. In the simplest case, the model can be a set of 3D points. More sophisticated methods produce a complete 3D surface model. The advent of 3D imaging not requiring motion or scanning, and related processing algorithms is enabling rapid advances in this field. Grid-based 3D sensing can be used to acquire 3D images from multiple angles. Algorithms are now available to stitch multiple 3D images together into point clouds and 3D models.

Image restoration
Image restoration comes into the picture when the original image is degraded or damaged due to some external factors like lens wrong positioning, transmission interference, low lighting or motion blurs, etc., which is referred to as noise. When the images are degraded or damaged, the information to be extracted from them also gets damaged. Therefore we need to recover or restore the image as it was intended to be. The aim of image restoration is the removal of noise (sensor noise, motion blur, etc.) from images. The simplest possible approach for noise removal is various types of filters, such as low-pass filters or median filters. More sophisticated methods assume a model of how the local image structures look to distinguish them from noise. By first analyzing the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis step, a better level of noise removal is usually obtained compared to the simpler approaches.

An example in this field is inpainting.

System methods
The organization of a computer vision system is highly application-dependent. Some systems are stand-alone applications that solve a specific measurement or detection problem, while others constitute a sub-system of a larger design which, for example, also contains sub-systems for control of mechanical actuators, planning, information databases, man-machine interfaces, etc. The specific implementation of a computer vision system also depends on whether its functionality is pre-specified or if some part of it can be learned or modified during operation. Many functions are unique to the application. There are, however, typical functions that are found in many computer vision systems.

 * Image acquisition – A digital image is produced by one or several image sensors, which, besides various types of light-sensitive cameras, include range sensors, tomography devices, radar, ultra-sonic cameras, etc. Depending on the type of sensor, the resulting image data is an ordinary 2D image, a 3D volume, or an image sequence. The pixel values typically correspond to light intensity in one or several spectral bands (gray images or colour images) but can also be related to various physical measures, such as depth, absorption or reflectance of sonic or electromagnetic waves, or magnetic resonance imaging.
 * Pre-processing – Before a computer vision method can be applied to image data in order to extract some specific piece of information, it is usually necessary to process the data in order to ensure that it satisfies certain assumptions implied by the method. Examples are:
 * Re-sampling to ensure that the image coordinate system is correct.
 * Noise reduction to ensure that sensor noise does not introduce false information.
 * Contrast enhancement to ensure that relevant information can be detected.
 * Scale space representation to enhance image structures at locally appropriate scales.
 * Feature extraction – Image features at various levels of complexity are extracted from the image data. Typical examples of such features are:
 * Lines, edges and ridges.
 * Localized interest points such as corners, blobs or points.
 * More complex features may be related to texture, shape, or motion.

 * Detection/segmentation – At some point in the processing, a decision is made about which image points or regions of the image are relevant for further processing. Examples are:
 * Selection of a specific set of interest points.
 * Segmentation of one or multiple image regions that contain a specific object of interest.
 * Segmentation of image into nested scene architecture comprising foreground, object groups, single objects or salient object parts (also referred to as spatial-taxon scene hierarchy), while the visual salience is often implemented as spatial and temporal attention.
 * Segmentation or co-segmentation of one or multiple videos into a series of per-frame foreground masks while maintaining its temporal semantic continuity.
 * High-level processing – At this step, the input is typically a small set of data, for example, a set of points or an image region, which is assumed to contain a specific object. The remaining processing deals with, for example:
 * Verification that the data satisfies model-based and application-specific assumptions.
 * Estimation of application-specific parameters, such as object pose or object size.
 * Image recognition – classifying a detected object into different categories.
 * Image registration – comparing and combining two different views of the same object.
 * Decision making Making the final decision required for the application, for example:
 * Pass/fail on automatic inspection applications.
 * Match/no-match in recognition applications.
 * Flag for further human review in medical, military, security and recognition applications.

Image-understanding systems
Image-understanding systems (IUS) include three levels of abstraction as follows: low level includes image primitives such as edges, texture elements, or regions; intermediate level includes boundaries, surfaces and volumes; and high level includes objects, scenes, or events. Many of these requirements are entirely topics for further research.

The representational requirements in the designing of IUS for these levels are: representation of prototypical concepts, concept organization, spatial knowledge, temporal knowledge, scaling, and description by comparison and differentiation.

While inference refers to the process of deriving new, not explicitly represented facts from currently known facts, control refers to the process that selects which of the many inference, search, and matching techniques should be applied at a particular stage of processing. Inference and control requirements for IUS are: search and hypothesis activation, matching and hypothesis testing, generation and use of expectations, change and focus of attention, certainty and strength of belief, inference and goal satisfaction.

Hardware

There are many kinds of computer vision systems; however, all of them contain these basic elements: a power source, at least one image acquisition device (camera, ccd, etc.), a processor, and control and communication cables or some kind of wireless interconnection mechanism. In addition, a practical vision system contains software, as well as a display in order to monitor the system. Vision systems for inner spaces, as most industrial ones, contain an illumination system and may be placed in a controlled environment. Furthermore, a completed system includes many accessories, such as camera supports, cables, and connectors.

Most computer vision systems use visible-light cameras passively viewing a scene at frame rates of at most 60 frames per second (usually far slower).

A few computer vision systems use image-acquisition hardware with active illumination or something other than visible light or both, such as structured-light 3D scanners, thermographic cameras, hyperspectral imagers, radar imaging, lidar scanners, magnetic resonance images, side-scan sonar, synthetic aperture sonar, etc. Such hardware captures "images" that are then processed often using the same computer vision algorithms used to process visible-light images.

While traditional broadcast and consumer video systems operate at a rate of 30 frames per second, advances in digital signal processing and consumer graphics hardware has made high-speed image acquisition, processing, and display possible for real-time systems on the order of hundreds to thousands of frames per second. For applications in robotics, fast, real-time video systems are critically important and often can simplify the processing needed for certain algorithms. When combined with a high-speed projector, fast image acquisition allows 3D measurement and feature tracking to be realized.

Egocentric vision systems are composed of a wearable camera that automatically take pictures from a first-person perspective.

As of 2016, vision processing units are emerging as a new class of processors to complement CPUs and graphics processing units (GPUs) in this role.

Lists

 * Outline of computer vision
 * List of emerging technologies
 * Outline of artificial intelligence

title: Computer vision


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Curry

Curry is a dish with a sauce seasoned with spices, mainly associated with South Asian cuisine. It is not to be confused with leaves from the curry tree, though some curries do include curry leaves. Curry is also found in the native cuisines of many South East Asian and East Asian countries due to ancient contact with South Asia.

There are many varieties of curry. The choice of spices for each dish in traditional cuisine depends on regional cultural traditions and personal preferences. Such dishes have names that refer to their ingredients, spicing, and cooking methods. Outside the Indian subcontinent, a curry is a dish from Southeast Asia which uses coconut milk or spice pastes and is commonly eaten over rice. Curries may contain fish, meat, poultry, or shellfish, either alone or in combination with vegetables. Others are vegetarian. Dry curries are cooked using small amounts of liquid, which is allowed to evaporate, leaving the other ingredients coated with the spice mixture. Wet curries contain significant amounts of sauce or gravy based on broth, coconut cream or coconut milk, dairy cream or yogurt, or legume purée, sautéed crushed onion, or tomato purée.

Curry powder, a commercially prepared mixture of spices marketed in the West, was first exported to Britain in the 18th century when Indian merchants sold a concoction of spices, similar to garam masala, to the British East India Company returning to Britain.

Etymology

Curry is an anglicised form of the Tamil கறி  meaning 'sauce' or 'relish for rice' that uses the leaves of the curry tree (Murraya koenigii). The word kari is also used in other Dravidian languages, namely in Malayalam, Kannada and Kodava with the meaning of "vegetables (or meat) of any kind (raw or boiled), curry". Kaṟi is described in a mid-17th century Portuguese cookbook by members of the British East India Company, who were trading with Tamil merchants along the Coromandel Coast of southeast India, becoming known as a "spice blend ... called kari podi or curry powder". The first appearance in its anglicised form (spelled currey) was in Hannah Glasse's 1747 book The Art of Cookery Made Plain and Easy.

The word cury in the 1390s English cookbook, The Forme of Cury, is unrelated, coming from the Middle French word cuire, meaning 'to cook'.

History
Evidence has been found that Austronesian merchants in South East Asia traded spices along marine trade routes between South Asia (primarily the ports on the south eastern coast of India and Sri Lanka) and East Asia as far back at 5000 BCE. Archaeological evidence dating to 2600 BCE from Mohenjo-daro also suggests the use of mortar and pestle to pound spices including mustard, fennel, cumin, and tamarind pods with which they flavoured food. Black pepper is native to the Indian subcontinent and Southeast Asia and has been known to Indian cooking since at least 2000 BCE.

The three basic ingredients of the spicy stew were ginger, garlic, and turmeric. Using a method called "starch grain analysis", archaeologists identified the residue of these spices in both skeletons and pottery shards from excavations in India, finding that turmeric and ginger were present.

The establishment of the Mughal Empire, in the early 16th century, also influenced some curries, especially in the north. Another influence was the establishment of the Portuguese trading centre in Goa in 1510, resulting in the introduction of chili pepper, tomatoes and potatoes to India from the Americas, as a byproduct of the Columbian Exchange.

The British lumped all sauce-based dishes under the generic name 'curry'. It was introduced to English cuisine from Anglo-Indian cooking in the 17th century, as spicy sauces were added to plain boiled and cooked meats. Curry was first served in coffee houses in Britain from 1809, and has been increasingly popular in Great Britain, with major jumps in the 1940s and the 1970s. During the 19th century, curry was carried to the Caribbean by Indian indentured workers in the British sugar industry. Since the mid-20th century, curries of many national styles have become popular far from their origins, and increasingly become part of international fusion cuisine.

South Asia
India is the home of curry, and many Indian dishes are curry-based, prepared by adding different types of vegetables, lentils, or meats. The content of the curry and style of preparation vary by region. Most curries are water-based, with occasional use of dairy and coconut milk. Curry dishes are usually thick and spicy and are eaten along with steamed rice and a variety of Indian breads. The popular rogan josh, for example, from Kashmiri cuisine, is a wet curry of lamb with a red gravy coloured by Kashmiri chillies and an extract of the red flowers of the cockscomb plant (mawal). Goshtaba (large lamb meatballs cooked in yoghurt gravy) is another curry dish from the Wazwan tradition occasionally found in Western restaurants.

Curries in Bengali cuisine include seafood and fresh fish. Mustard seeds and mustard oil are added to many recipes, as are poppy seeds. Emigrants from the Sylhet district of Bangladesh founded the curry house industry in Britain, while in Sylhet some restaurants run by expatriates specialise in British-style Indian food.

Rice and curry is the staple dish of Sri Lanka.

East Asia
Curry spread to other regions of Asia. Although not an integral part of Chinese cuisine, curry powder is added to some dishes in the southern part of China. The curry powder sold in Chinese grocery stores is similar to Madras curry powder but with addition of star anise and cinnamon. The former Portuguese colony of Macau has its own culinary traditions and curry dishes, including Galinha à portuguesa and curry crab. Portuguese sauce is a sauce flavoured with curry and thickened with coconut milk.

Japanese curry is usually eaten as karē raisu – curry, rice, and often pickled vegetables, served on the same plate and eaten with a spoon, a common lunchtime canteen dish. It is less spicy and seasoned than Indian and Southeast Asian curries, being more of a thick stew than a curry. British people brought curry from the Indian colony back to Britain and introduced it to Japan during the Meiji period (1868 to 1912), after Japan ended its policy of national self-isolation (sakoku), and curry in Japan was categorised as a Western dish. Its spread across the country is attributed to its use in the Japanese Army and Navy which adopted it extensively as convenient field and naval canteen cooking, allowing even conscripts from the remotest countryside to experience the dish. The Japan Maritime Self-Defense Force traditionally have curry every Friday for lunch and many ships have their own recipes. The standard Japanese curry contains onions, carrots, potatoes, and sometimes celery, and a meat that is cooked in a large pot. Sometimes grated apples or honey are added for additional sweetness and other vegetables are sometimes used instead.

Curry was popularized in Korean cuisine when Ottogi entered the Korean food industry with a curry powder in 1969. Korean curry, usually served with rice, is characterized by the golden yellow colour of turmeric. Curry tteokbokki is made of tteok (rice cakes), eomuk (fish cakes), eggs, vegetables, and curry. Curry can be added to Korean dishes such as bokkeumbap (fried rice), sundubujjigae (silken tofu stew), fried chicken, vegetable stir-fries, and salads.

Southeast Asia

Indian Indonesian cuisine consists of adaptations of authentic dishes from India, as well as original creations inspired by the diverse food culture of Indonesia. Curry in Indonesian is kari and in Javanese is kare. In Indonesian cuisine especially in Bandung, there is a dish called lontong kari, a combined of lontong and beef yellow curry soup. In Javanese cuisine, kare rajungan, blue swimmer crab curry has become a delicacy of Tuban Regency, East Java. Rendang, the national dish of Indonesia, which is originated from Minang, is drier and contains mostly meat and more coconut milk than a conventional Malaysian curry; it was mentioned in Malay literature in the 1550s by Hikayat Amir Hamzah.

Malaysian cuisine may have initially incorporated curries via the Indian population, but it has become a staple among the Malay and Chinese populations there. Malaysian curries typically use turmeric-rich curry powders, coconut milk, shallots, ginger, belacan (shrimp paste), chili peppers, and garlic. Tamarind is also often used.

In Burmese cuisine, curries are broadly called hin. Burmese curries generally consist of protein that is simmered in a curry base of aromatics including shallots, onions, ginger, and garlic, alongside dried spices like turmeric, paprika, and garam masala. Burmese curries generally differ from other Southeast Asian curries in that dried spices are also used commonly to season the dishes, while coconut milk is only used sparingly for select dishes.

In the Philippines, two kinds of curry traditions are seen corresponding with the cultural divide between the Hispanicised north and Indianised/Islamised south. In the northern areas, a linear range of new curry recipes could be seen. The most common is a variant of the native ginataang manok (chicken cooked in coconut milk) dish with the addition of curry powder, known as the "Filipino chicken curry". This is the usual curry dish that northern Filipinos are familiar with. Similarly, other northern Filipino dishes that can be considered "curries" are usually ginataan (cooked with coconut milk) variants of other native meat or seafood dishes such as adobo, kaldereta, and mechado, that simply add curry powder or non-native Indian spices.

In Thai cuisine, curries are called kaeng, and usually consist of meat, fish or vegetables in a sauce based on a paste made from chilies, onions or shallots, garlic, and shrimp paste. Additional spices and herbs define the type of curry. Local ingredients, such as chili peppers, kaffir lime leaves, lemongrass, galangal are used and, in central and southern Thai cuisine, coconut milk. Northern and northeastern Thai curries generally do not contain coconut milk. Due to the use of sugar and coconut milk, Thai curries tend to be sweeter than Indian curries. In the West, some of the Thai curries are described by colour; red curries use red chilies while green curries use green chilies. Yellow curry—called kaeng kari (by various spellings) in Thai, of which a literal translation could be "curry soup"—is more similar to Indian curries, with the use of turmeric, cumin, and other dried spices. A few stir-fried Thai dishes also use an Indian style curry powder (Thai: phong kari).

In Vietnamese cuisine, it is known as cà ri and is made of ingredients such as coconut milk, potatoes, sweet potatoes, taro, and chicken, along with coriander and green onions. This dish is more like soup than Indian curry. Goat meat curry is also available, but only in a few special restaurants in Vietnam. Curry is often served with bread, vermicelli or rice. Curry is considered a dish in the south. The other ingredients of the curry are very diverse, depending on the meat ingredients, the main fruit for cooking curry as well as the chef's creativity. Vietnamese curries are also made with coconut milk, red cashew, onions, ginger, potatoes, sweet potatoes, carrots, radishes, vegetables, and various types of meat.

Africa
Consumption of curry spread to South Africa with the migration of people from the Indian subcontinent to the region in the colonial era. African curries, Cape Malay curries and Natal curries include the traditional Natal curry, the Durban curry, Bunny chow, and roti rolls. South African curries appear to have been founded in both KwaZulu-Natal and the Western Cape, while other curries developed across the country over the late 20th century and early 21st century to include ekasi, coloured, and Afrikaner curries. Durban has the largest population of Indians outside of India in the world. Bunny chow or a "set", a South African standard, consists of either lamb, chicken or bean curry poured into a tunnelled-out loaf of bread to be eaten with one's fingers by dipping pieces of the bread into it.

Europe

Curry is very popular in the United Kingdom, with a curry house in nearly every town. Such is the popularity of curry in the United Kingdom, that it has frequently been called its "adopted national dish". It was estimated that in 2016 there were 12,000 curry houses, employing 100,000 people and with annual combined sales of approximately £4.2 billion.

The food offered is Indian food cooked to British taste, but with increasing demand for authentic Indian styles. As of 2015, curry houses accounted for a fifth of the restaurant business in the UK, but, being historically a low wage sector, they were plagued by a shortage of labour. Established Indian immigrants from South Asia were moving on to other occupations; there were difficulties in training Europeans to cook curry; and immigration restrictions, which require payment of a high wage to skilled immigrants, had crimped the supply of new cooks.

Curry powder
"Curry powder", as available in certain western markets, is a commercial spice blend, and first sold by Indian merchants to European colonial traders. This resulted in the export of a derived version of Indian concoction of spices. and commercially available from the late 18th century, with brands such as Crosse & Blackwell and Sharwood's persisting to the present. British traders introduced the powder to Meiji Japan, in the mid-19th century, where it became known as Japanese curry.

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title: Curry


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Camel

A camel (from camelus and κάμηλος from Ancient Semitic: gāmāl  ) is an even-toed ungulate in the genus Camelus that bears distinctive fatty deposits known as "humps" on its back. Camels have long been domesticated and, as livestock, they provide food (camel milk and meat) and textiles (fiber and felt from camel hair). Camels are working animals especially suited to their desert habitat and are a vital means of transport for passengers and cargo. There are three surviving species of camel. The one-humped dromedary makes up 94% of the world's camel population, and the two-humped Bactrian camel makes up 6%. The wild Bactrian camel is a distinct species that is not ancestral to the domestic Bactrian camel, and is now critically endangered, with fewer than 1,000 individuals.

The word camel is also used informally in a wider sense, where the more correct term is "camelid", to include all seven species of the family Camelidae: the true camels (the above three species), along with the "New World" camelids: the llama, the alpaca, the guanaco, and the vicuña, which belong to the separate tribe Lamini. Camelids originated in North America during the Eocene, with the ancestor of modern camels, Paracamelus, migrating across the Bering land bridge into Asia during the late Miocene, around 6 million years ago.

Extant species
Three species are extant:

Biology
The average life expectancy of a camel is 40 to 50 years. A full-grown adult dromedary camel stands 1.85 m at the shoulder and 2.15 m at the hump. Bactrian camels can be a foot taller. Camels can run at up to 65 km/h in short bursts and sustain speeds of up to 40 km/h. Bactrian camels weigh 300 to 1000 kg and dromedaries 300 to 600 kg. The widening toes on a camel's hoof provide supplemental grip for varying soil sediments.

The male dromedary camel has an organ called a dulla in his throat, a large, inflatable sac that he extrudes from his mouth when in rut to assert dominance and attract females. It resembles a long, swollen, pink tongue hanging out of the side of the camel's mouth. Camels mate by having both male and female sitting on the ground, with the male mounting from behind. The male usually ejaculates three or four times within a single mating session. Camelids are the only ungulates to mate in a sitting position.

Ecological and behavioral adaptations
Camels do not directly store water in their humps; they are reservoirs of fatty tissue. When this tissue is metabolized, it yields a greater mass of water than that of the fat processed. This fat metabolization, while releasing energy, causes water to evaporate from the lungs during respiration (as oxygen is required for the metabolic process): overall, there is a net decrease in water.

Camels have a series of physiological adaptations that allow them to withstand long periods of time without any external source of water. The dromedary camel can drink as seldom as once every 10 days even under very hot conditions, and can lose up to 30% of its body mass due to dehydration. Unlike other mammals, camels' red blood cells are oval rather than circular in shape. This facilitates the flow of red blood cells during dehydration and makes them better at withstanding high osmotic variation without rupturing when drinking large amounts of water: a 600 kg camel can drink 200 L of water in three minutes.

Camels are able to withstand changes in body temperature and water consumption that would kill most other mammals. Their temperature ranges from 34 °C at dawn and steadily increases to 40 °C by sunset, before they cool off at night again. In general, to compare between camels and the other livestock, camels lose only 1.3 liters of fluid intake every day while the other livestock lose 20 to 40 liters per day. Maintaining the brain temperature within certain limits is critical for animals; to assist this, camels have a rete mirabile, a complex of arteries and veins lying very close to each other which utilizes countercurrent blood flow to cool blood flowing to the brain. Camels rarely sweat, even when ambient temperatures reach 49 °C. Any sweat that does occur evaporates at the skin level rather than at the surface of their coat; the heat of vaporization therefore comes from body heat rather than ambient heat. Camels can withstand losing 25% of their body weight in water, whereas most other mammals can withstand only about 12–14% dehydration before cardiac failure results from circulatory disturbance.

When the camel exhales, water vapor becomes trapped in their nostrils and is reabsorbed into the body as a means to conserve water. Camels eating green herbage can ingest sufficient moisture in milder conditions to maintain their bodies' hydrated state without the need for drinking.

The camel's thick coat insulates it from the intense heat radiated from desert sand; a shorn camel must sweat 50% more to avoid overheating. During the summer the coat becomes lighter in color, reflecting light as well as helping avoid sunburn. The camel's long legs help by keeping its body farther from the ground, which can heat up to 70 °C. Dromedaries have a pad of thick tissue over the sternum called the pedestal. When the animal lies down in a sternal recumbent position, the pedestal raises the body from the hot surface and allows cooling air to pass under the body.

Camels' mouths have a thick leathery lining, allowing them to chew thorny desert plants. Long eyelashes and ear hairs, together with nostrils that can close, form a barrier against sand. If sand gets lodged in their eyes, they can dislodge it using their translucent third eyelid (also known as the nictitating membrane). The camels' gait and widened feet help them move without sinking into the sand.

The kidneys and intestines of a camel are very efficient at reabsorbing water. Camels' kidneys have a 1:4 cortex to medulla ratio. Thus, the medullary part of a camel's kidney occupies twice as much area as a cow's kidney. Secondly, renal corpuscles have a smaller diameter, which reduces surface area for filtration. These two major anatomical characteristics enable camels to conserve water and limit the volume of urine in extreme desert conditions. Camel urine comes out as a thick syrup, and camel faeces are so dry that they do not require drying when used to fuel fires.

The camel immune system differs from those of other mammals. Normally, the Y-shaped antibody molecules consist of two heavy (or long) chains along the length of the Y, and two light (or short) chains at each tip of the Y. Camels, in addition to these, also have antibodies made of only two heavy chains, a trait that makes them smaller and more durable. These "heavy-chain-only" antibodies, discovered in 1993, are thought to have developed 50 million years ago, after camelids split from ruminants and pigs. Camels suffer from surra caused by Trypanosoma evansi wherever camels are domesticated in the world, and resultantly camels have evolved trypanolytic antibodies as with many mammals. In the future, nanobody/single-domain antibody therapy will surpass natural camel antibodies by reaching locations currently unreachable due to natural antibodies' larger size. Such therapies may also be suitable for other mammals. Tran et al. 2009 provides a new reference test for surra (T. evansi) of camel. They use recombinant (rISG75, an Invariant Surface Glycoprotein) and ELISA. The Tran test has high test specificity and appears likely to work just as well for T. evansi in other hosts, and for a pan-Trypanozoon test, which would also be useful for T. b. brucei, T. b. gambiense, T. b. rhodesiense, and T. equiperdum.

Genetics
The karyotypes of different camelid species have been studied earlier by many groups,     but no agreement on chromosome nomenclature of camelids has been reached. A 2007 study flow sorted camel chromosomes, building on the fact that camels have 37 pairs of chromosomes (2n=74), and found that the karyotype consisted of one metacentric, three submetacentric, and 32 acrocentric autosomes. The Y is a small metacentric chromosome, while the X is a large metacentric chromosome. The hybrid camel, a hybrid between Bactrian and dromedary camels, has one hump, though it has an indentation 4 – deep that divides the front from the back. The hybrid is 2.15 m at the shoulder and 2.32 m tall at the hump. It weighs an average of 650 kg and can carry around 400 to 450 kg, which is more than either the dromedary or Bactrian can.

According to molecular data, the wild Bactrian camel (C. ferus) separated from the domestic Bactrian camel (C. bactrianus) about 1 million years ago. New World and Old World camelids diverged about 11 million years ago. In spite of this, these species can hybridize and produce viable offspring. The cama is a camel-llama hybrid bred by scientists to see how closely related the parent species are. Scientists collected semen from a camel via an artificial vagina and inseminated a llama after stimulating ovulation with gonadotrophin injections. The cama is halfway in size between a camel and a llama and lacks a hump. It has ears intermediate between those of camels and llamas, longer legs than the llama, and partially cloven hooves. Like the mule, camas are sterile, despite both parents having the same number of chromosomes.

Evolution
The earliest known camel, called Protylopus, lived in North America 40 to 50 million years ago (during the Eocene). It was about the size of a rabbit and lived in the open woodlands of what is now South Dakota. By 35 million years ago, the Poebrotherium was the size of a goat and had many more traits similar to camels and llamas. The hoofed Stenomylus, which walked on the tips of its toes, also existed around this time, and the long-necked Aepycamelus evolved in the Miocene. The split between the tribes Camelini, which contains modern camels and Lamini, modern llamas, alpacas, vicuñas, and guanacos, is estimated to have occurred over 16 million years ago.

The ancestor of modern camels, Paracamelus, migrated into Eurasia from North America via Beringia during the late Miocene, between 7.5 and 6.5 million years ago. During the Pleistocene, around 3 to 1 million years ago, the North American Camelidae spread to South America as part of the Great American Interchange via the newly formed Isthmus of Panama, where they gave rise to guanacos and related animals. Populations of Paracamelus continued to exist in the North American Arctic into the Early Pleistocene. This creature is estimated to have stood around 9 ft tall. The Bactrian camel diverged from the dromedary about 1 million years ago, according to the fossil record.

The last camel native to North America was Camelops hesternus, which vanished along with horses, short-faced bears, mammoths and mastodons, ground sloths, sabertooth cats, and many other megafauna as part of the Quaternary extinction event, coinciding with the migration of humans from Asia at the end of the Pleistocene, around 13–11,000 years ago.

An extinct giant camel species, Camelus knoblochi roamed Asia during the Late Pleistocene, before becoming extinct around 20,000 years ago.

Domestication
Like horses, camels originated in North America and eventually spread across Beringia to Asia. They survived in the Old World, and eventually humans domesticated them and spread them globally. Along with many other megafauna in North America, the original wild camels were wiped out during the spread of the first indigenous peoples of the Americas from Asia into North America, 10 to 12,000 years ago; although fossils have never been associated with definitive evidence of hunting.

Most camels surviving today are domesticated. Although feral populations exist in Australia, India and Kazakhstan, wild camels survive only in the wild Bactrian camel population of the Gobi Desert.

History
When humans first domesticated camels is disputed. Dromedaries may have first been domesticated by humans in Somalia or South Arabia sometime during the 3rd millennium BC, the Bactrian in central Asia around 2,500 BC,  as at Shar-i Sokhta (also known as the Burnt City), Iran. A study from 2016, which genotyped and used world-wide sequencing of modern and ancient mitochondrial DNA (mtDNA), suggested that they were initially domesticated in the southeast Arabian Peninsula, with the Bactrian type later being domesticated around Central Asia.

Martin Heide's 2010 work on the domestication of the camel tentatively concludes that humans had domesticated the Bactrian camel by at least the middle of the third millennium somewhere east of the Zagros Mountains, with the practice then moving into Mesopotamia. Heide suggests that mentions of camels "in the patriarchal narratives may refer, at least in some places, to the Bactrian camel", while noting that the camel is not mentioned in relationship to Canaan. Heide and Joris Peters reasserted that conclusion in their 2021 study on the subject.

In 2009-2013, excavations in the Timna Valley by Lidar Sapir-Hen and Erez Ben-Yosef discovered what may be the earliest domestic camel bones yet found in Israel or even outside the Arabian Peninsula, dating to around 930 BC. This garnered considerable media coverage, as it is strong evidence that the stories of Abraham, Jacob, Esau, and Joseph were written after this time.

The existence of camels in Mesopotamia—but not in the eastern Mediterranean lands—is not a new idea. The historian Richard Bulliet did not think that the occasional mention of camels in the Bible meant that the domestic camels were common in the Holy Land at that time. The archaeologist William F. Albright, writing even earlier, saw camels in the Bible as an anachronism.

The official report by Sapir-Hen and Ben-Joseph says:

"The introduction of the dromedary camel (Camelus dromedarius) as a pack animal to the southern Levant ... substantially facilitated trade across the vast deserts of Arabia, promoting both economic and social change (e.g., Kohler 1984; Borowski 1998: 112–116; Jasmin 2005). This ... has generated extensive discussion regarding the date of the earliest domestic camel in the southern Levant (and beyond) (e.g., Albright 1949: 207; Epstein 1971: 558–584; Bulliet 1975; Zarins 1989; Köhler-Rollefson 1993; Uerpmann and Uerpmann 2002; Jasmin 2005; 2006; Heide 2010; Rosen and Saidel 2010; Grigson 2012). Most scholars today agree that the dromedary was exploited as a pack animal sometime in the early Iron Age (not before the 12th century [BC])"

and concludes:

"Current data from copper smelting sites of the Aravah Valley enable us to pinpoint the introduction of domestic camels to the southern Levant more precisely based on stratigraphic contexts associated with an extensive suite of radiocarbon dates. The data indicate that this event occurred not earlier than the last third of the 10th century [BC] and most probably during this time. The coincidence of this event with a major reorganization of the copper industry of the region—attributed to the results of the campaign of Pharaoh Shoshenq I—raises the possibility that the two were connected, and that camels were introduced as part of the efforts to improve efficiency by facilitating trade."

Textiles
Desert tribes and Mongolian nomads use camel hair for tents, yurts, clothing, bedding and accessories. Camels have outer guard hairs and soft inner down, and the fibers may also be sorted by color and age of the animal. The guard hairs can be felted for use as waterproof coats for the herdsmen, while the softer hair is used for premium goods. The fiber can be spun for use in weaving or made into yarns for hand knitting or crochet. Pure camel hair is recorded as being used for western garments from the 17th century onwards, and from the 19th century a mixture of wool and camel hair was used.

Military uses

By at least 1200 BC the first camel saddles had appeared, and Bactrian camels could be ridden. The first saddle was positioned to the back of the camel, and control of the Bactrian camel was exercised by means of a stick. However, between 500 and 100 BC, Bactrian camels came into military use. New saddles, which were inflexible and bent, were put over the humps and divided the rider's weight over the animal. In the seventh century BC the military Arabian saddle evolved, which again improved the saddle design slightly.

Military forces have used camel cavalries in wars throughout Africa, the Middle East, and into the modern-day Border Security Force (BSF) of India (though as of July 2012, the BSF planned the replacement of camels with ATVs). The first documented use of camel cavalries occurred in the Battle of Qarqar in 853 BC. Armies have also used camels as freight animals instead of horses and mules.

The East Roman Empire used auxiliary forces known as dromedarii, whom the Romans recruited in desert provinces. The camels were used mostly in combat because of their ability to scare off horses at close range (horses are afraid of the camels' scent), a quality famously employed by the Achaemenid Persians when fighting Lydia in the Battle of Thymbra (547 BC).

19th and 20th centuries

The United States Army established the U.S. Camel Corps, stationed in California, in the 19th century. One may still see stables at the Benicia Arsenal in Benicia, California, where they nowadays serve as the Benicia Historical Museum. Though the experimental use of camels was seen as a success (John B. Floyd, Secretary of War in 1858, recommended that funds be allocated towards obtaining a thousand more camels), the outbreak of the American Civil War in 1861 saw the end of the Camel Corps: Texas became part of the Confederacy, and most of the camels were left to wander away into the desert.

France created a méhariste camel corps in 1912 as part of the Armée d'Afrique in the Sahara in order to exercise greater control over the camel-riding Tuareg and Arab insurgents, as previous efforts to defeat them on foot had failed. The Free French Camel Corps fought during World War II, and camel-mounted units remained in service until the end of French rule over Algeria in 1962.

In 1916, the British created the Imperial Camel Corps. It was originally used to fight the Senussi, but was later used in the Sinai and Palestine Campaign in World War I. The Imperial Camel Corps comprised infantrymen mounted on camels for movement across desert, though they dismounted at battle sites and fought on foot. After July 1918, the Corps began to become run down, receiving no new reinforcements, and was formally disbanded in 1919.

In World War I, the British Army also created the Egyptian Camel Transport Corps, which consisted of a group of Egyptian camel drivers and their camels. The Corps supported British war operations in Sinai, Palestine, and Syria by transporting supplies to the troops.

The Somaliland Camel Corps was created by colonial authorities in British Somaliland in 1912; it was disbanded in 1944.

Bactrian camels were used by Romanian forces during World War II in the Caucasian region. At the same period the Soviet units operating around Astrakhan in 1942 adopted local camels as draft animals due to shortage of trucks and horses, and kept them even after moving out of the area. Despite severe losses, some of these camels ended up as far west as to Berlin itself.

The Bikaner Camel Corps of British India fought alongside the British Indian Army in World Wars I and II.

The Tropas Nómadas (Nomad Troops) were an auxiliary regiment of Sahrawi tribesmen serving in the colonial army in Spanish Sahara (today Western Sahara). Operational from the 1930s until the end of the Spanish presence in the territory in 1975, the Tropas Nómadas were equipped with small arms and led by Spanish officers. The unit guarded outposts and sometimes conducted patrols on camelback.

21st century competition
The annual King Abdulaziz Camel Festival is held in Saudi Arabia. In addition to camel racing and camel milk tasting, the festival holds a camel "beauty pageant" with prize money of $57m (£40m). In 2018, 12 camels were disqualified from the beauty contest after their owners were found to have injected them with botox. In a similar incident in 2021, over 40 camels were disqualified.

Food uses
Camel meat and milk are foods that are found in many cuisines, typically in Middle Eastern, North African and some Australian cuisines.

Dairy

Camel milk is a staple food of desert nomad tribes and is sometimes considered a meal itself; a nomad can live on only camel milk for almost a month.

Camel milk can readily be made into yogurt, but can only be made into butter if it is soured first, churned, and a clarifying agent is then added. Until recently, camel milk could not be made into camel cheese because rennet was unable to coagulate the milk proteins to allow the collection of curds. Developing less wasteful uses of the milk, the FAO commissioned Professor J.P. Ramet of the École Nationale Supérieure d'Agronomie et des Industries Alimentaires, who was able to produce curdling by the addition of calcium phosphate and vegetable rennet in the 1990s. The cheese produced from this process has low levels of cholesterol and is easy to digest, even for the lactose intolerant.

Camel milk can also be made into ice cream.

Meat
Camels provide food in the form of meat and milk. Approximately 3.3 million camels and camelids are slaughtered each year for meat worldwide. A camel carcass can provide a substantial amount of meat. The male dromedary carcass can weigh 300 –, while the carcass of a male Bactrian can weigh up to 650 kg. The carcass of a female dromedary weighs less than the male, ranging between 250 and 350 kg. The brisket, ribs and loin are among the preferred parts, and the hump is considered a delicacy. The hump contains "white and sickly fat", which can be used to make the khli (preserved meat) of mutton, beef, or camel. On the other hand, camel milk and meat are rich in protein, vitamins, glycogen, and other nutrients making them essential in the diet of many people. From chemical composition to meat quality, the dromedary camel is the preferred breed for meat production. It does well even in arid areas due to its unusual physiological behaviors and characteristics, which include tolerance to extreme temperatures, radiation from the sun, water paucity, rugged landscape and low vegetation. Camel meat is reported to taste like coarse beef, but older camels can prove to be very tough, although camel meat becomes tenderer the more it is cooked.

Camel is one of the animals that can be ritually slaughtered and divided into three portions (one for the home, one for extended family/social networks, and one for those who cannot afford to slaughter an animal themselves) for the qurban of Eid al-Adha.

The Abu Dhabi Officers' Club serves a camel burger mixed with beef or lamb fat in order to improve the texture and taste. In Karachi, Pakistan, some restaurants prepare nihari from camel meat. Specialist camel butchers provide expert cuts, with the hump considered the most popular.

Camel meat has been eaten for centuries. It has been recorded by ancient Greek writers as an available dish at banquets in ancient Persia, usually roasted whole. The Roman emperor Heliogabalus enjoyed camel's heel. Camel meat is mainly eaten in certain regions, including Eritrea, Somalia, Djibouti, Saudi Arabia, Egypt, Syria, Libya, Sudan, Ethiopia, Kazakhstan, and other arid regions where alternative forms of protein may be limited or where camel meat has had a long cultural history. Camel blood is also consumable, as is the case among pastoralists in northern Kenya, where camel blood is drunk with milk and acts as a key source of iron, vitamin D, salts and minerals.

A 2005 report issued jointly by the Saudi Ministry of Health and the United States Centers for Disease Control and Prevention details four cases of human bubonic plague resulting from the ingestion of raw camel liver.

Camel meat is also occasionally found in Australian cuisine: for example, a camel lasagna is available in Alice Springs. Australia has exported camel meat, primarily to the Middle East but also to Europe and the US, for many years. The meat is very popular among East African Australians, such as Somalis, and other Australians have also been buying it. The feral nature of the animals means they produce a different type of meat to farmed camels in other parts of the world, and it is sought after because it is disease-free, and a unique genetic group. Demand is outstripping supply, and governments are being urged not to cull the camels, but redirect the cost of the cull into developing the market. Australia has seven camel dairies, which produce milk, cheese and skincare products in addition to meat.

Islam
Muslims consider camel meat halal (حلال, 'allowed'). However, according to some Islamic schools of thought, a state of impurity is brought on by the consumption of it. Consequently, these schools hold that Muslims must perform wudhu (ablution) before the next time they pray after eating camel meat. Also, some Islamic schools of thought consider it haram (حرام, 'forbidden') for a Muslim to perform Salat in places where camels lie, as it is said to be a dwelling place of the Shaytan (شيطان, 'Devil'). According to Abu Yusuf (d.798), the urine of camel may be used for medical treatment if necessary, but according to Abū Ḥanīfah, the drinking of camel urine is discouraged.

Islamic texts contain several stories featuring camels. In the story of the people of Thamud, the prophet Salih miraculously brings forth a naqat (ناقة, 'milch-camel') out of a rock. After Muhammad migrated from Mecca to Medina (the Hijrah), he allowed his she-camel to roam there; the location where the camel stopped to rest determined the location where he would build his house in Medina.

Judaism
According to Jewish tradition, camel meat and milk are not kosher. Camels possess only one of the two kosher criteria; although they chew their cud, they do not have cloven hooves: "But these you shall not eat among those that bring up the cud and those that have a cloven hoof: the camel, because it brings up its cud, but does not have a [completely] cloven hoof; it is unclean for you."

The Palestinian Muslim Makhamara clan in Yatta, who claim descent from Jews, reportedly avoid eating camel meat, a practice cited as evidence of their Jewish origins.

Cultural depictions
What may be the oldest carvings of camels were discovered in 2018 in Saudi Arabia. They were analysed by researchers from several scientific disciplines and, in 2021, were estimated to be 7,000 to 8,000 years old. The dating of rock art is made difficult by the lack of organic material in the carvings that may be tested, so the researchers attempting to date them tested animal bones found associated with the carvings, assessed erosion patterns, and analysed tool marks in order to determine a correct date for the creation of the sculptures. This Neolithic dating would make the carvings significantly older than Stonehenge (5,000 years old) and the Egyptian pyramids at Giza (4,500 years old) and it predates estimates for the domestication of camels.

Distribution and numbers

There are approximately 14 million camels alive, with 90% being dromedaries. Dromedaries alive today are domesticated animals (mostly living in the Horn of Africa, the Sahel, Maghreb, Middle East and South Asia). The Horn region alone has the largest concentration of camels in the world, where the dromedaries constitute an important part of local nomadic life. They provide nomadic people in Somalia and Ethiopia with milk, food, and transportation.

Over one million dromedary camels are estimated to be feral in Australia, descended from those introduced as a method of transport in the 19th and early 20th centuries. This population is growing about 8% per year; it was estimated at around 700,000 in 2008. Representatives of the Australian government have culled more than 100,000 of the animals in part because the camels use too much of the limited resources needed by sheep farmers.

A small population of introduced camels, dromedaries and Bactrians, wandered through Southwestern United States after having been imported in the 19th century as part of the U.S. Camel Corps experiment. When the project ended, they were used as draft animals in mines and escaped or were released. Twenty-five U.S. camels were bought and exported to Canada during the Cariboo Gold Rush.

The Bactrian camel is,, reduced to an estimated 1.4 million animals, most of which are domesticated. The Wild Bactrian camel is the only truly wild (as opposed to feral) camel in the world. It is a distinct species that is not ancestral to the domestic Bactrian camel. The wild camels are critically endangered and number approximately 950, inhabiting the Gobi and Taklamakan Deserts in China and Mongolia.

title: Camel