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.
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Artificial intelligence and computer vision share other topics such as pattern recognition and learning techniques. Consequently, computer vision is sometimes seen as a part of the artificial intelligence field or the computer science field in general.
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 or infra-red 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. 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 behavior of biological systems, at different levels of complexity. Also, some of the learning-based methods developed within computer vision e. 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 consciousness, 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, studies and describes the processes implemented in software and hardware behind artificial vision systems. Interdisciplinary exchange between biological and computer vision has proven fruitful for both fields.
Yet another field related to computer vision is signal processing.
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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 processing of one-variable signals.
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Together with the multi-dimensionality of the signal, this 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.
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.
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Computer graphics produces image data from 3D models, computer vision often produces 3D models from image data . There is also a trend towards a combination of the two disciplines, e. Photogrammetry also overlaps with computer vision, e. 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, the 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:.
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 detection of tumours , arteriosclerosis or other malign changes; measurements of organ dimensions, blood flow, etc. It also supports medical research by providing new information: Applications of computer vision in the medical area also includes enhancement of images interpreted by humans—ultrasonic images or X-ray images for example—to reduce the influence of noise.
A second application area in computer vision is in industry, sometimes called machine vision , where 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.
Machine vision is also heavily used in agricultural process to remove undesirable food stuff from bulk material, a process called optical sorting. 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. It can also be used for detecting certain task specific events, e. 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. 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.
Different varieties of the recognition problem are described in the literature: 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 hundreds of object classes.
Performance of convolutional neural networks, on the ImageNet tests, is now close to that of humans. 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 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:. 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.
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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 . The aim of image restoration is the removal of noise sensor noise, motion blur, etc. 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. 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 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 if 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 which are found in many computer vision systems. As the model of this deformity behaviour is known, it is possible to recover the object's height. All the three dimensional computer vision techniques are described in detail as follows. The stereo vision system herein presented was designed for a robotic 'pick-and-place' application , as shown schematically in the scheme in Fig.
The stereo images grabbing process takes place in two steps, with the camera filming the scene from this top view. After the first image is grabbed, the camera is moved away to 0,5 to 1,5 cm range through a displacement mechanism driven by a step motor. After this displacement, the second image is grabbed. The development and tests of the algorithm have been carried out in four steps.
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At the beginning, the work was conducted with lower resolution images x pixels to make the routines development process faster due to the huge computational effort involved in this technique. As soon as the correlation algorithm parameters were optimised and settled to provide a good performance, the second step took place, where the real image, grabbed by the camera with x pixels and 64 grey levels resolution, replaced the synthetic images. The third step was the development of a process for calibrating intrinsic camera lens parameters. The most important parameter is the focal distance that is used to recover the object's height information.
The fourth step was the recovery of tridimensional data about the scene from 2D images. From the first image, which is always grabbed at an initial position, the information about objects in scene is achieved in pixels, such as length and width. To recover metric information about objects, it is necessary to find out the relationship between metric and pixels scales. The information about object height is calculated through a simple triangulation technique from geometric optics model of stereo images configuration, as shown in Fu The result search is a coarse to fine process, which ends when the iterative procedure reaches the required error.
These algorithms are associated to a ' energy ' function, quantifying the degree of violation of constraints, which is minimised as the process evolves. The stereo matching algorithm implemented in this paper has been proposed by Feris Camera model and camera calibration. The camera model adopted is the ' pinhole ' model, as shown in Fu and in Nalwa Camera calibration is the determination of all its inner geometric and optics features.
These inner features are called intrinsic parameters. Camera calibration also means the determination of its position and orientation relative to the world co-ordinate system. These features are called extrinsic parameters. Laudares presents in detail an extrinsic camera calibration process, which is quite suitable for the robotic application proposed in this work. The most important camera intrinsic parameter is the focal distance l , which is the distance between the centre of the lens and the image sensor plane.
The following conditions must be met for the model shown in the Fig 2: According to Fu , the depth information recovery Z co-ordinate is achieved by the following expression: Some improvements on Feris technique were included in order to increase the algorithm performance and ease the correlation process, as shown in Kabayama Further information about focal distance and scale factor processes procedures and calibration results can be found in Kabayama Table 1 shows the results of some objects height measures using 30mm baseline displacement.
Disparity is the difference between respective x co-ordinates in both images and matches established is the number of correlated points. The conception of the sensorial fusion technique for 3D-vision machine is shown in the Fig. The sensor provides an analogic tension output proportional to the distance to be measured. This proportional pattern can behave in a direct or in an inverse way, depending on how the sensor is programmed rising or falling modes.
The curves of this sensor relate output tension variation as a function of distance were determined using both proportional modes for different range programs. The results showed that the ultra sound sensor has a linear behaviour in all modes and this is an important and desirable feature. The respective static calibration coefficients for each curve were calculated and they are necessary to sensitivity to establish the relationship between the output tension and the distance measured and for evaluating the sensitivity of the programmed mode for noise and the resolution.
As for ultrasound beam characteristics, as shown in Fig. The distances shown in Table 2 refer to the object top. The determination of the ultrasound beam diameter in a given level was performed in an experimental way: An object was moved on this surface towards the place that the sensor was pointing at. As soon as the object was detected, the place where that happened was marked. This procedure was repeated until a complete ultrasound profile in this level was determined. This entire process was repeated for other levels, as shown in the Table 2.
From the knowledge about the sensor features, it is possible to estimate the minimum size of the object that can be manipulated using this technique. For example, at 40cm range, the object size must be 16cm at least. The size of the objects cannot be smaller than the diameter of ultrasound beam in the object top. Besides, the object material should not absorb the ultrasound waves and the object's top must be perpendicular to the direction that the ultrasound beam may reach it.
Two different lighting patterns were studied to evaluate accuracy and check if this technique is suitable for a pick-and-place robotic application. The first pattern studied was a source of laser light from a presentation pointer device. An external DC power source was adapted to the device in order to avoid the decreasing light intensity due to batteries flattering process.
The full line and the dotted line shown in Fig. The scene is filmed twice. At the first shot, represented by the dotted line, the object is out of scene and P1 is the position of the laser beam centre area where it touches the ground. At second shot, represented by the full line, the object is in scene and P2 is the position of the laser beam centre area where it touches the top of the object.
The laser beam area centre is determined by computer vision system in both situations. P3 is the P2 projection in the horizontal plane. The laser beam reaches the ground with a q angle and d is the distance, in pixels, between area centres P1 and P2. The object h height is determined by a simple trigonometric relation see Fig.
The first step was the determination of the conversion factor s.