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al heritage special effects in movies and television and ludology visual computing also includes digital art and digital media studies other fields edit 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 25 distinctions edit 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 and output are both images whereas in computer vision the input is an image or video and the output could be an enhanced image an analysis of the image s content or even a system s behavior based on that analysis computer graphics produces image data from 3d models and computer vision often produces 3d models from image data 26 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 27 in industrial applications 22 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 28 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 29 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 30 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 edit 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 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 26 automatic inspection e g in manufacturing applications assisting humans in identification tasks e g a species identification system 31 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 mediapipe an open source framework from google for ai edge device computing e g face detection image classification object detection monitoring agricultural crops e g an open source vision transformers model 32 has been developed to help farmers automatically detect strawberry diseases with 98 4 accuracy 33 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 analyzing the condition of facilities in industry or construction automatic real time lip reading for devices and apps to assist people with disabilities 34 for 2024 the leading areas of computer vision were industry market size us 5 22 billion 35 medicine market size us 2 6 billion 36 military market size us 996 2 million 37 medicine edit darpa s visual media reasoning concept video 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 38 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 edit 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 inspecti 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 39 military edit 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 edit 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 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 edit 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 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 40 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 41 other applications edit other application areas include support of visual effects creation for cinema and broadcast e g camera tracking match moving surveillance driver drowsiness detection 42 43 44 tracking and counting organisms in the biological sciences 45 for example in leaf counting for food crops 46 typical tasks edit 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 1 2 3 4 understanding in this context means the transformation of visual images 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 47 recognition edit 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 48 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 49 performance of convolutional neural networks on the im...