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    Machine Vision & Computer Vision
    Experiments, Studies and Background Information


    • Machine Vision: Recognizing objects and scenes [View Experiment]
    • A Versatile Camera Calibration Techniaue for High-Accuracy 3D Machine Vision Metrology Using Off-the-shelf TV Cameras and Len [View Experiment]
    • Computer Vision: Thermal Imaging and Infrared [View Experiment]
    • Monitoring Creatures Great and Small: Computer Vision Systems for Looking at Grizzly Bears, Fish, and Grasshoppers [View Experiment]
    • Assessment of Post-Stroke Functioning using Machine Vision [View Experiment]
    • Applying Machine Vision to Verification and Testing [View Experiment]
    • Pedestrian Detectability: Predicting Human Perception Performance with Machine Vision [View Experiment]
    • Design and Implementation of an Embedded Vision System for Industrial Robots [View Experiment]
    Machine Vision & Computer Vision


    Computer vision is the science and technology of machines that see.

    Machine vision (MV) is the application of computer vision to industry and manufacturing.

    Whereas computer vision is mainly focused on machine-based image processing, machine vision most often requires also digital input/output devices and computer networks to control other manufacturing equipment such as robotic arms.

    Machine Vision

    See also Machine Vision Glossary

    Machine vision (MV) is the application of computer vision to industry and manufacturing. Whereas computer vision is mainly focused on machine-based image processing, machine vision most often requires also digital input/output devices and computer networks to control other manufacturing equipment such as robotic arms. Machine Vision is a subfield of engineering that encompasses computer science, optics, mechanical engineering, and industrial automation. One of the most common applications of Machine Vision is the inspection of manufactured goods such as semiconductor chips, automobiles, food and pharmaceuticals. Just as human inspectors working on assembly lines visually inspect parts to judge the quality of workmanship, so machine vision systems use digital cameras, smart cameras and image processing software to perform similar inspections.

    Machine vision systems are programmed to perform narrowly defined tasks such as counting objects on a conveyor, reading serial numbers, and searching for surface defects. Manufacturers favour machine vision systems for visual inspections. that require high-speed, high-magnification, 24-hour operation, and/or repeatability of measurements. Frequently these tasks extend roles traditionally occupied by human beings whose degree of failure is classically high through distraction, illness and circumstance. However, humans may display finer perception over the short period and greater flexibility in classification and adaptation to new defects and quality assurance policies.

    Computers do not 'see' in the same way that human beings are able to. Cameras are not equivalent to human optics and while people can rely on inference systems and assumptions, computing devices must 'see' by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features such as Pattern recognition engines. Although some machine vision algorithms have been developed to mimic human visual perception, a number of unique processing methods have been developed to process images and identify relevant image features in an effective and consistent manner. Machine vision and computer vision systems are capable of processing images consistently, but computer-based image processing systems are typically designed to perform single, repetitive tasks, and despite significant improvements in the field, no machine vision or computer vision system can yet match some capabilities of human vision in terms of image comprehension, tolerance to lighting variations and image degradation, parts' variability etc.

    Components of a machine vision system

    A typical machine vision system will consist of several among the following components:

    1. One or more digital or analog camera (black-and-white or colour) with suitable optics for acquiring images
    2. Camera interface for digitizing images (widely known as a "frame grabber")
    3. A processor (often a PC or embedded processor, such as a DSP)
    4. (In some cases, all of the above are combined within a single device, called a smart camera).
    5. Input/Output hardware (e.g. digital I/O) or communication links (e.g. network connection or RS-232) to report results
    6. Lenses to focus the desired field of view onto the image sensor.
    7. Suitable, often very specialized, light sources (LED illuminators, fluorescent or halogen lamps etc.)
    8. A program to process images and detect relevant features.
    9. A synchronizing sensor for part detection (often an optical or magnetic sensor) to trigger image acquisition and processing.
    10. Some form of actuators used to sort or reject defective parts.

    The sync sensor determines when a part (often moving on a conveyor) is in position to be inspected. The sensor triggers the camera to take a picture of the part as it passes beneath the camera and often synchronizes a lighting pulse to freeze a sharp image. The lighting used to illuminate the part is designed to highlight features of interest and obscure or minimize the appearance of features that are not of interest (such as shadows or reflections). LED panels of suitable sizes and arrangement are often used to this purpose.

    The camera's image is captured by the framegrabber. A framegrabber is a digitizing device (within a smart camera or as a separate computer card) that converts the output of the camera to digital format (typically a two dimensional array of numbers, corresponding to the luminous intensity level of the corresponding point in the field of view, called pixel) and places the image in computer memory so that it may be processed by the machine vision software.

    The software will typically take several steps to process an image. Often the image is first manipulated to reduce noise or to convert many shades of gray to a simple combination of black and white (binarization). Following the initial simplification, the software will count, measure, and/or identify objects, dimensions, defects or other features in the image. As a final step, the software passes or fails the part according to programmed criteria. If a part fails, the software may signal a mechanical device to reject the part; alternately, the system may stop the production line and warn a human worker to fix the problem that caused the failure.

    Though most machine vision systems rely on black-and-white cameras, the use of colour cameras is becoming more common. It is also increasingly common for Machine Vision systems to include digital camera equipment for direct connection rather than a camera and separate framegrabber, thus reducing signal degradation.

    "Smart" cameras with built-in embedded processors are capturing an increasing share of the machine vision market. The use of an embedded (and often very optimized) processor eliminates the need for a framegrabber card and external computer, thus reducing cost and complexity of the system while providing dedicated processing power to each camera. Smart cameras are typically less expensive than systems comprising a camera and a board and/or external computer, while the increasing power of embedded processors and DSPs is often providing comparable or higher performance and capabilities than conventional PC-based systems.

    Processing methods

    Commercial and open source machine vision software packages typically include a number of different image processing techniques such as the following:

    • Pixel counting: counts the number of light or dark pixels
    • Thresholding: converts an image with gray tones to simply black and white
    • Segmentation: used to locate and/or count parts
      • Blob discovery & manipulation: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks. These blobs frequently represent optical targets for machining, robotic capture, or manufacturing failure.
      • Recognition-by-components: extracting geons from visual input
      • Robust pattern recognition: location of an object that may be rotated, partially hidden by another object, or varying in size
    • Barcode reading: decoding of 1D and 2D codes designed to be read or scanned by machines
    • Optical character recognition: automated reading of text such as serial numbers
    • Gauging: measurement of object dimensions in inches or millimeters
    • Edge detection: finding object edges
    • Template matching: finding, matching, and/or counting specific patterns

    In most cases, a machine vision system will use a sequential combination of these processing techniques to perform a complete inspection. E.g. A system that reads a barcode may also check a surface for scratches or tampering and measure the length and width of a machined component.

    Applications of machine vision

    The applications of Machine Vision (MV) are diverse, covering areas of endeavour including, but not limited to:

    • Biometrics
    • Large-scale industrial manufacture
    • Short-run unique object manufacture
    • Safety systems in industrial environments
    • Inspection of pre-manufactured objects (e.g. quality control, failure investigation)
    • Visual stock control and management systems (counting, barcode reading, store interfaces for digital systems)
    • Control of Automated Guided Vehicles (AGVs)
    • Automated monitoring of sites for security and safety
    • Monitoring of agricultural production
    • Quality control and refinement of food products
    • Retail automation
    • Consumer equipment control
    • Medical imaging processes (e.g. Interventional Radiology)
    • Medical remote examination and procedures

    Machine vision systems are widely used in semiconductor fabrication; indeed, without machine vision, yields for computer chips would be significantly reduced. Machine vision systems inspect silicon wafers, processor chips, and subcomponents such as resistors and capacitors.

    In the automotive industry, machine vision systems are used to guide industrial robots, gauge the fit of stamped metal components, and inspect the surface of the painted vehicle for defects.

    Though machine vision techniques were developed for the visible spectrum, the same processing techniques may be applied to images captured using imagers sensitive to other forms of spectra such as infrared light or x-ray emissions.

    Related fields

    Machine vision is distinct from computer vision. Computer vision extends to topics related to autonomous robotics and machine representation of human vision. Machine Vision refers to automated imaging systems including a wide range of computing disciplines aggregated to form a complete solution to visual problems and can be considered a superset composed of Computer Vision and elements such as equipment control, databasing, network systems, interfacing and machine learning.

    Computer Vision

    Computer vision is the science and technology of machines that see.

    As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images. The image data can take many forms, such as a video sequence, views from multiple cameras, or multi-dimensional data from a medical scanner.

    As a technological discipline, computer vision seeks to apply the theories and models of computer vision to the construction of computer vision systems. Examples of applications of computer vision systems include systems for

    1. Controlling processes (e.g. an industrial robot or an autonomous vehicle).
    2. Detecting events (e.g. for visual surveillance or people counting)
    3. Organizing information (e.g. for indexing databases of images and image sequences),
    4. Modeling objects or environments (e.g. industrial inspection, medical image analysis or topographical modeling),
    5. Interaction (e.g. as the input to a device for computer-human interaction).

    Computer vision can also be described as a complement (but not necessarily the opposite) of biological vision. In biological vision, the visual perception of humans and various animals are studied, resulting in models of how these systems operate in terms of physiological processes. Computer vision, on the other hand, studies and describes artificial vision system that are implemented in software and/or hardware. Interdisciplinary exchange between biological and computer vision has proven increasingly fruitful for both fields.

    Sub-domains of computer vision include scene reconstruction, event detection, tracking, object recognition, learning, indexing, ego-motion and image restoration.

    State of the art

    The field of computer vision can be characterized as immature and diverse. Even though earlier work exists, it was not until the late 1970s that a more focused study of the field started when computers could manage the processing of large data sets such as images. However, these studies usually originated from various other fields, and consequently there is no standard formulation of "the computer vision problem". Also, and to an even larger extent, there is no standard formulation of how computer vision problems should be solved. Instead, there exists an abundance of methods for solving various well-defined computer vision tasks, where the methods often are very task specific and seldom can be generalized over a wide range of applications. Many of the methods and applications are still in the state of basic research, but more and more methods have found their way into commercial products, where they often constitute a part of a larger system which can solve complex tasks (e.g., in the area of medical images, or quality control and measurements in industrial processes). In most practical computer vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common.

    A significant part of artificial intelligence deals with autonomous planning or deliberation for system which can perform mechanical actions such as moving a robot through some environment. This type of processing typically needs input data provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot. Other parts which sometimes are described as belonging to artificial intelligence and which are used in relation to computer vision is pattern recognition and learning techniques. As a consequence, computer vision is sometimes seen as a part of the artificial intelligence field or the computer science field in general.

    Physics is another field that is strongly related to computer vision. A significant part of computer vision deals with methods which require a thorough understanding of the process in which electromagnetic radiation, typically in the visible or the infra-red range, is reflected by the surfaces of objects and finally is measured by the image sensor to produce the image data. This process is based on optics and solid state physics. More sophisticated image sensors even require quantum mechanics to provide a complete comprehension of the image formation process. Also, various measurement problems in physics can be addressed using computer vision, for example related to motion in fluids. Consequently, computer vision can also be seen as an extension of physics.

    A third field which plays an important role is neurobiology, specifically the study of the biological vision system. Over the last century, there has been an extensive study of eyes, neurons, and the brain structures devoted to processing of visual stimuli in both humans and various animals. This has led to a coarse, yet complicated, description of how "real" vision systems operate in order to solve certain vision related tasks. These results have led to a subfield within computer vision where artificial systems are designed to mimic the processing and behaviour of biological systems, at different levels of complexity. Also, some of the learning-based methods developed within computer vision have their background in biology.

    Yet another field related to computer vision is signal processing. Many methods for processing of one-variable signals, typically temporal signals, can be extended in a natural way to 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 which have no counterpart in the processing of one-variable signals. A distinct character of these methods is the fact that they are non-linear which, together with the multi-dimensionality of the signal, defines a subfield in signal processing as a part of computer vision.

    Beside 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.

    Related fields

    Computer vision, Image processing, Image analysis, Robot vision and Machine vision are closely related fields. If you look inside text books which have any of these names in the title there is a significant overlap in terms of what techniques and applications they cover. This implies that the basic techniques that are used and developed in these fields are more or less identical, 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. 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 require assumptions nor produce interpretations about the image content.

    Computer vision tends to focus on 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 tends to focus on applications, mainly in industry, e.g., vision based autonomous robots and systems for vision based inspection or measurement. 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.

    There is also a field called Imaging which primarily focus on the process of producing images, but sometimes also deals with processing and analysis of images. For example, Medical imaging contains lots of work on the analysis of image data in medical applications.

    Finally, pattern recognition is a field which uses various methods to extract information from signals in general, mainly based on statistical approaches. A significant part of this field is devoted to applying these methods to image data.

    A consequence of this state of affairs is that you can be working in a lab related to one of these fields, apply methods from a second field to solve a problem in a third field and present the result at a conference related to a fourth field!

    Examples of applications for computer vision

    One of the most prominent application fields is medical computer vision or medical image processing. This area is characterized by the extraction of information from image data for the purpose of making a medical diagnosis of a patient. Generally, image data is in the form of microscopy images, X-ray images, angiography images, ultrasonic images, and tomography images. An example of information which can be extracted from such image data is detection of tumours, arteriosclerosis or other malign changes. It can also be measurements of organ dimensions, blood flow, etc. This application area also supports medical research by providing new information, e.g., about the structure of the brain, or about the quality of medical treatments.

    A second application area in computer vision is in industry. Here, information is extracted for the purpose of supporting a manufacturing process. One example is quality control where details or final products are being automatically inspected in order to find defects. Another example is measurement of position and orientation of details to be picked up by a robot arm.

    Military applications are probably one of the largest areas for computer vision. The obvious examples are 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 which 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.

    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, i.e. for knowing where it is, or for producing a map of its environment (SLAM) and 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, 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 Mars Exploration Rover.

    Other application areas include:

    • Support of visual effects creation for cinema and broadcast, e.g., camera tracking (matchmoving).
    • Surveillance.

    Typical tasks of computer vision

    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.


    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. This task can normally be solved robustly and without effort by a human, but is still not satisfactorily solved in computer vision for the general case: arbitrary objects in arbitrary situations. The existing methods for dealing with this problem can at best solve it only for specific objects, such as simple geometric objects (e.g., polyhedrons), human faces, printed or hand-written characters, or vehicles, and in specific situations, typically described in terms of well-defined illumination, background, and pose of the object relative to the camera.

    Different varieties of the recognition problem are described in the literature:

    • Recognition: 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.
    • Identification: An individual instance of an object is recognized. Examples: identification of a specific person's face or fingerprint, or identification of a specific vehicle.
    • Detection: the image data is scanned for a specific condition. Examples: detection of possible abnormal cells or tissues in medical images or 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.

    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 a target image (give me all images similar to image X), or in terms of high-level search criteria given as text input (give me all images which contains 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.
    • Optical character recognition (or 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).


    Several tasks relate to motion estimation, in which an image sequence is processed to produce an estimate of the velocity either at each points in the image or in the 3D scene. Examples of such tasks are:

    • Egomotion: determining the 3D rigid motion of the camera.
    • Tracking: following the movements of objects (e.g. vehicles or humans).

    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.

    Image restoration

    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 like, a model which distinguishes them from the noise. By first analysing 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.

    Computer vision systems

    The organization of a computer vision system is highly application dependent. Some systems are stand-alone applications which solve a specific measurement or detection problem, while other 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 if its functionality is pre-specified or if some part of it can be learned or modified during operation. There are, however, typical functions which are found in many computer vision systems.

    • Image acquisition: A digital image is produced by one or several image sensor which, besides various types of light-sensitive cameras, includes 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 nuclear magnetic resonance.
    • 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 assure that it satisfies certain assumptions implied by the method. Examples are
      • Re-sampling in order to assure that the image coordinate system is correct.
      • Noise reduction in order to assure that sensor noise does not introduce false information.
      • Contrast enhancement to assure 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 which contain a specific object of interest.
    • 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 satisfy model-based and application specific assumptions.
      • Estimation of application specific parameters, such as object pose or object size.
      • Classifying a detected object into different categories.

    Source: Wikipedia (All text is available under the terms of the GNU Free Documentation License and Creative Commons Attribution-ShareAlike License.)

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