a particular task, but methods based on learning are now becoming increasingly common. examples of applications of computer vision include systems for: learning 3d shapes has been a challenging task in computer vision. recent advances in deep learning have enabled researchers to build models that are able to generate and reconstruct 3d shapes from single or multi-view depth maps or silhouettes seamlessly and efficiently. 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 food stuff 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 artist's concept of curiosity, an example of an uncrewed land-based vehicle. the stereo camera is mounted on top of the rover. 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 and/or automatically ensuring navigational safety. 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 and systems for autonomous landing of aircraft. several car manufacturers have demonstrated systems for autonomous driving of cars, but this technology has still not reached a level where it can be put on the market. 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 rubber artificial skin layer with the flexible structure for the shape estimation of micro-undulation surfaces above is a silicon mold with a camera inside containing many different point markers. when this sensor is pressed against the surface the silicon deforms and the position of the point markers shifts. a computer can then take this data and determine how exactly the mold is pressed against the surface. this can be used to calibrate robotic hands in order to make sure they can grasp objects effectively. materials such as rubber and silicon are being used to create sensors that allow for applications such as detecting micro undulations 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 is 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 identification of a specific vehicle. detection – the image data are scanned for a specific condition. examples include the detection of 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 a 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). computer vision for people counter purposes in public places, malls, shopping centers 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). 2d code reading – 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. shape recognition technology (srt) in people counter systems differentiating human beings (head and shoulder patterns) from objects 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 of 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 both of 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 picture when the original image is degraded