رؤية حاسوبية

الرؤية الحاسوبية هي إحدى مجالات علم الحاسوب، تهدف إلى بناء تطبيقات ذكية قادرة على فهم محتوى الصور كما يفهمها الإنسان. حيث من الممكن أن تأخذ بيانات الصور عدة أشكال كالصور المتعاقبة (فيديو)، المشاهد من عدة كاميرات، بيانات ذات عدة أبعاد مأخوذ من جهاز تصوير طبي. بعض الأمثلة على تطبيقات الرؤية الحاسوبية:

• تطبيق قادر على التعرف على الأغراض أو الأشخاص ضمن صورة
• تطبيقات التحكم الآلي (الروبوتالصناعي، المركبات الآلية).
• بناء نماذج للأشياء أو للمحيط (الفحص الصناعي، تحليل الصورة الطبية).
• تطبيق قادر على متابعة غرض يتحرك ضمن صورة
• تطبيق قادر على معرفة البعد الثالث من صورة أو أكثر ثنائية البعد (أو من صورة وضوء ليزري متحرك)

من الممكن وصف الرؤية الحاسوبية باعتبارها مرادفاً(وليس بالضرورة عكساً) للرؤية الفيزيولوجية. فكما أن الرؤية الفيزيولوجية للإنسان والحيوانات المختلفة تتم دراستها للتعرف على خصائصها، فإن علم الرؤية الحاسوبية يدرس ويصف أنظمة الرؤية الصنعية التي يتم تنفيذها في البرامج أو الأجهزة. وقد أظهر التعاون بين مجالي دراسة الرؤية الفيزيولوجية والحاسوبية تطوراً في تعميق الفهم لكلا المجالين.

 مجالات متعلقة

Object detection in a photograph

 فيزياء الحالة الصلبة

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.

 علم الأحياء العصبي

Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict starfish and sea urchins, which are correlated with "nodes" that represent visual features. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring-textured sea urchin creates a weakly weighted association between them.

Subsequent run of the network on an input image (left): The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a shell that was not included in the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a false positive result for sea urchin.
In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.

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.

 معالجة الإشارات

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.

 ملاحة روبوتية

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

 مجالات أخرى

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.

 التمييز

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.

 التطبيقات

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.

• 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;
• monitoring agricultural crops, e.g. an open-source vision transformers model has been developed to help farmers automatically detect strawberry diseases with 98.4% accuracy.
• 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

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. 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

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

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. 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

 المهام الأساسية للرؤية الحاسوبية

كل واحد من التطبيقات المذكورة آنفاً يتضم العديد من مهام الرؤية الحاسوبية، بعضها مهام للقياس، وبعضها مهام حسابية تستخدم لحل العديد من المسائل. هذه بعض المهام الأساسية لعلم الرؤية الحاسوبية.

 التعرف

هي المهمة التقليدية في الرؤية الحاسوبية، وهي القيام بتحديد ما إذا كانت الصورة تحتوي أو لا تحتوي جسماً، معلماً، أو نشاطاً معيناً. هذه المهمة من الممكن حلها بباسطة وبدون أي جهد يذكر بواسطة الإنسان، لكن لا تزال هذه المسألة غير محلولة بشكل فعال ونهائي من قبل الحاسوب في شكلها العام. جميع الطرق الموجودة لحل هذه المسألة تقوم بإيجاد أفضل الحلول من أجل إيجاد أشكال معينة كالأشكال الهندسية، وجوه الأشخاص، الأحرف المطبوعة أو المكتوبة، أو السيارات، وفي حالات معينة فقط محددة على الغالب بظروف إضاءة محددة، خلفية ووضعية معينة للجسم بالنسبة للكاميرا. هناك أنواع مختلفة لمشكلة التعرف مشروحة في المراجع العلمية:

• التعرف Recognition : يتم التعرف على واحد أو البعض من الأجسام التي تم تعليمها مسبقاً للحاسوب، غالباً بأوضاعها المختلفة أو بزاويا مختلفة للكاميرا. Blippar, Google Goggles, and LikeThat provide stand-alone programs that illustrate this functionality.

• التحديد Identification: تحديد مطابق وحيد للجسم المعرف. مثلاً: تحديد وجه شخص معين أو التعرف على بصمة شخص معين أو سيارة من نوع معين. identification of handwritten digits, or the identification of a specific vehicle.

• التحري Detection:يتم البحث في بيانات الصورة لإيجاد جسم معين. مثال: تحري وجود خلايا مريضة في صورة طبية، التحري عن وجود سيارة على طريق سريع. الأمثلة تضم تمييز عقبة في مجال رؤية السيارة والخلايا الشاذة المحتملة في الصور الطبية أو تحري سيارة في نظام دفع تلقائي لرسم استخدام الطريق. 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).

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). 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.

 الحركة

يوجد العديد من المهام التي تتعلق بتقدير الحركة حيث تعالج فيها سلسلة من الصور المتعاقبة زمنياً من أجل حساب السرعة إما عند كل نقطة في الصورة أو في المشهد الثلاثي الأبعاد. بعض الأمثلة على هذه المهام هي:

• حركة الكاميرا Egomotion: تحديد الحركة الصلبة للكاميرا في الفضاء الثلاثي الأبعاد.

• التتبع Tracking: تتبع حركة الأجسام في الصورة المتعاقبة زمنياً (فيديو) مثل تتبع الأشخاص أو السيارات.

 بناء المشهد Scene reconstruction

بإعطاء صورة واحدة (بشكل عام أكثر من واحدة) لجسم معين أو صور متعاقبة، تهدف عملية بناء المشهد إلى حساب الموديل الثلاثي البعد للمشهد. وفي أبسط الحالات من الممكن إعادة بناء الجسم على شكل مجموعة من النقاط الثلاثية الأبعاد. وهناك طرق معقدة أكثر تقوم ببناء الموديل السطحي الكامل للأجسام.

 ترميم الصورة Image restoration

تهدف عملية ترميم الصور إلى إزالة التشويش (تشويش المستشعرات، تشويش الحركة...الخ) من الصور. تعتبر عمليات الفلترة (فلتر المتوسط - فلتر الوسيط..الخ) من أبسط عمليات إزالة التشويش من الصور. وهناك عمليات معقدة أكثر تفترض الشكل الذي تبدو عليه الصور مما يسمح لها بالتمييز بين الصورة والتشويش. يتم بشكل مبدئي التعرف على مكونات الصورة كالخطوط والمستقيمات ومن ثم التحكم بالفلتر بناء على المعلومات المحلية في جزء الصورة حيث يتم الحصول على نتائج أفضل من استخدام الفلاتر البسيطة.

 أنظمة الرؤية الحاسوبية

تختلف أنظمة الرؤية الحاسوبية بشكل كبير وتتوزع بين أنظمة كبيرة ومعقدة تؤدي مهمات عامة وشاملة، وبين أنظمة صغيرة تؤدي مهمات مخصصة وبسيطة. ولكن معظم أنظمة الرؤية الحاسوبية تشمل العناصر التالية بشكل أساسي:

• الحصول على الصورة: يتم الحصول على الصورة باستخدام واحد أو أكثر من مستشعرات الصور، وهذه تتضمن العديد من كاميرات مستشعرات الضوء، مستشعرات المسافات، أجهزة التصوير الشعاعي، الرادار، كاميرات الموجات الفوق صوتية..الخ. وتبعاً لنوع المستشعر فإن الصورة الناتجة تكون ثنائية البعد أو ثلاثية البعد أو سلسلة صور متعاقبة. تكون قيمة كل بكسل في الصورة تابعة لقيمة شدة الإشعاع الضوئي في واحد أو أكثر من الحزم الضوئية (الصور الرمادية، أو الصور الملونة) ولكن أيضاً من الممكن أن تشير إلى العديد من القياسات الفيزيائية كالبعد، الامتصاص، أو انعكاس الموجات الكهرومغناطيسية.
• العمليات المسبقة: قبل تطبيق خوارزمية الرؤية الحاسوبية على بيانات الصورة من أجل الحصول على معلومات مفيدة، فإنه من الضروري إجراء عمليات مسبقة على البيانات من أجل تأكيد أن البيانات تحقق افتراضات محددة تابعة للخوارزمية. بعض الأمثلة على هذه العمليات هي:

• • إعادة تحديد دقة الصورة من أجل تأكيد صحة نظام إحداثيات الصورة.
  • التقليل من التشويش من أجل التأكد أن المستشعر لا يقوم بتقديم أي معلومات خاطئة.
  • زيادة التباين من أجل التأكد من أن المعلومات المرغوبة سيكون من الممكن الحصول عليها.

• استحصال معالم الصورة Feature extraction: يتم الحصول على معالم الصورة على مستويات دقة مختلفة من بيانات الصورة ذاتها. تصنف هذه المعالم إلى:
  • معالم عامة global features مثل اللون و الشكل.
  • معالم محلية local features كالزوايا Harris corner ، والبقع و SIFT features .

من الممكن الحصول على معالم معقدة أكثر متعلقة بالألوان والأشكال في الصورة.

• التحري-التقسيم Detection/Segmentation

: يتم تحديد أي نقاط أو مناطق من الصورة هي المناطق الهامة من أجل العمليات اللاحقة. مثلاً:

• • اختيار مجموعة من نقاط العلام المميزة.
  • تقسيم ضورة أو أكثر تحتوي على المنطقة التي تحتوي الجسم المهتم به.

• العمليات عالية المستوى: عند هذه المرحلة تكون البيانات المدخلة هي مجموعة صغيرة من البيانات، على سبيل المثال مجموعة من النقاط أو منطقة من الصورة التي يشك أنها تحتوي الجسم موضوع الدراسة. والعمليات المتبقية تقوم بما يلي :
  • التأكد من أن البيانات التي تم الحصول عليها توافق افتراضات التطبيق المقترح.
  • تقدير قيم المعاملات المعينة للتطبيق، كاتجاه الجسم أو حجم الجسم.
  • تصنيف الأجسام التي تم التعرف عليها في عدة فئات.

 العتاد

A 2020 model iPad Pro with a LiDAR sensor

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.

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 قوائم

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

 للاستزادة

• James E. Dobson (2023). The Birth of Computer Vision. University of Minnesota Press. ISBN 978-1-5179-1421-9.
• David Marr (1982). Vision. W. H. Freeman and Company. ISBN 978-0-7167-1284-8.
• Azriel Rosenfeld; Avinash Kak (1982). Digital Picture Processing. Academic Press. ISBN 978-0-12-597301-4.
• Barghout, Lauren; Lawrence W. Lee (2003). Perceptual information processing system. U.S. Patent Application 10/618,543. ISBN 978-0-262-08159-7.
• Berthold K.P. Horn (1986). Robot Vision. MIT Press. ISBN 978-0-262-08159-7.
• Michael C. Fairhurst (1988). Computer Vision for robotic systems. Prentice Hall. ISBN 978-0-13-166919-2.
• Olivier Faugeras (1993). Three-Dimensional Computer Vision, A Geometric Viewpoint. MIT Press. ISBN 978-0-262-06158-2.
• Tony Lindeberg (1994). Scale-Space Theory in Computer Vision. Springer. ISBN 978-0-7923-9418-1.
• James L. Crowley; Henrik I. Christensen, eds. (1995). Vision as Process. Springer-Verlag. ISBN 978-3-540-58143-7.
• Gösta H. Granlund; Hans Knutsson (1995). Signal Processing for Computer Vision. Kluwer Academic Publisher. ISBN 978-0-7923-9530-0.
• Reinhard Klette; Karsten Schluens; Andreas Koschan (1998). Computer Vision – Three-Dimensional Data from Images. Springer, Singapore. ISBN 978-981-3083-71-4.
• Emanuele Trucco; Alessandro Verri (1998). Introductory Techniques for 3-D Computer Vision. Prentice Hall. ISBN 978-0-13-261108-4.
• Bernd Jähne (2002). Digital Image Processing. Springer. ISBN 978-3-540-67754-3.
• Richard Hartley and Andrew Zisserman (2003). Multiple View Geometry in Computer Vision. Cambridge University Press. ISBN 978-0-521-54051-3.
• Gérard Medioni; Sing Bing Kang (2004). Emerging Topics in Computer Vision. Prentice Hall. ISBN 978-0-13-101366-7.
• R. Fisher; K Dawson-Howe; A. Fitzgibbon; C. Robertson; E. Trucco (2005). Dictionary of Computer Vision and Image Processing. John Wiley. ISBN 978-0-470-01526-1.
• Nikos Paragios and Yunmei Chen and Olivier Faugeras (2005). Handbook of Mathematical Models in Computer Vision. Springer. ISBN 978-0-387-26371-7.
• Wilhelm Burger; Mark J. Burge (2007). Digital Image Processing: An Algorithmic Approach Using Java. Springer. ISBN 978-1-84628-379-6. Archived from the original on 2014-05-17. Retrieved 2007-06-13.
• Pedram Azad; Tilo Gockel; Rüdiger Dillmann (2008). Computer Vision – Principles and Practice. Elektor International Media BV. ISBN 978-0-905705-71-2.
• Richard Szeliski (2010). Computer Vision: Algorithms and Applications. Springer-Verlag. ISBN 978-1848829343.
• J. R. Parker (2011). Algorithms for Image Processing and Computer Vision (2nd ed.). Wiley. ISBN 978-0470643853.
• Richard J. Radke (2013). Computer Vision for Visual Effects. Cambridge University Press. ISBN 978-0-521-76687-6.
• Nixon, Mark; Aguado, Alberto (2019). Feature Extraction and Image Processing for Computer Vision (4th ed.). Academic Press. ISBN 978-0128149768.

 وصلات خارجية

• USC Iris computer vision conference list
• Computer vision papers on the web – a complete list of papers of the most relevant computer vision conferences.
• Computer Vision Online Archived 2011-11-30 at the Wayback Machine – news, source code, datasets and job offers related to computer vision
• CVonline – Bob Fisher's Compendium of Computer Vision.
• British Machine Vision Association – supporting computer vision research within the UK via the BMVC and MIUA conferences, Annals of the BMVA (open-source journal), BMVA Summer School and one-day meetings
• Computer Vision Container, Joe Hoeller GitHub: Widely adopted open-source container for GPU accelerated computer vision applications. Used by researchers, universities, private companies, as well as the U.S. Gov't.