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Lucas–Kanade Method
In computer vision, the Lucas–Kanade method is a widely used differential method for optical flow estimation developed by Bruce D. Lucas and Takeo Kanade. It assumes that the flow is essentially constant in a local neighbourhood of the pixel under consideration, and solves the basic optical flow equations for all the pixels in that neighbourhood, by the least squares criterion.B. D. Lucas and T. Kanade (1981), An iterative image registration technique with an application to stereo vision.' Proceedings of Imaging Understanding Workshop, pages 121--130Bruce D. Lucas (1984) Generalized Image Matching by the Method of Differences' (doctoral dissertation) By combining information from several nearby pixels, the Lucas–Kanade method can often resolve the inherent ambiguity of the optical flow equation. It is also less sensitive to image noise than point-wise methods. On the other hand, since it is a purely local method, it cannot provide flow information in the interior of uniform reg ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Vision
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can 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. The scientific discipline of computer vision is concerned with the theory ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Aperture Problem
Motion perception is the process of inferring the speed and direction of elements in a scene based on visual, vestibular and proprioceptive inputs. Although this process appears straightforward to most observers, it has proven to be a difficult problem from a computational perspective, and difficult to explain in terms of neural processing. Motion perception is studied by many disciplines, including psychology (i.e. visual perception), neurology, neurophysiology, engineering, and computer science. Neuropsychology The inability to perceive motion is called akinetopsia and it may be caused by a lesion to cortical area V5 in the extrastriate cortex. Neuropsychological studies of a patient who could not see motion, seeing the world in a series of static "frames" instead, suggested that visual area V5 in humans is homologous to motion processing area V5/MT in primates. First-order motion perception Two or more stimuli that are switched on and off in alternation can produce two di ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kanade–Lucas–Tomasi Feature Tracker
In computer vision, the Kanade–Lucas–Tomasi (KLT) feature tracker is an approach to feature extraction. It is proposed mainly for the purpose of dealing with the problem that traditional image registration techniques are generally costly. KLT makes use of spatial intensity information to direct the search for the position that yields the best match. It is faster than traditional techniques for examining far fewer potential matches between the images. The registration problem The traditional image registration problem can be characterized as follows: Given two functions F(x) and G(x), representing pixel values at each location x in two images, respectively, where x is a vector. We wish to find the disparity vector h that minimizes some measure of the difference between F(x+h) and G(x), for x in some region of interest R. Some measures of the difference between F(x+h) and G(x): * L1 norm = \sum_\left\vert F(x+h)-G(x) \right\vert * L2 norm = \sqrt * Negative of normalized correla ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Shi–Tomasi Corner Detection Algorithm
Corner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, image registration, video tracking, image mosaicing, panorama stitching, 3D reconstruction and object recognition. Corner detection overlaps with the topic of interest point detection. Formalization A corner can be defined as the intersection of two edges. A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point. An interest point is a point in an image which has a well-defined position and can be robustly detected. This means that an interest point can be a corner but it can also be, for example, an isolated point of local intensity maximum or minimum, line endings, or a point on a curve where the curvature is locally maximal. In practice, most so-called corner detection methods detect interest ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Horn–Schunck Method
The Horn–Schunck method of estimating optical flow is a global method which introduces a global constraint of ''smoothness'' to solve the ''aperture problem'' (see Optical Flow for further description). Mathematical details The Horn-Schunck algorithm assumes smoothness in the flow over the whole image. Thus, it tries to minimize distortions in flow and prefers solutions which show more smoothness. The flow is formulated as a global energy functional which is then sought to be minimized. This function is given for two-dimensional image streams as: : E=\iint \left I_xu + I_yv + I_t)^2 + \alpha^2(\lVert\nabla u\rVert^2+\lVert\nabla v\rVert^2)\right where I_x, I_y and I_t are the derivatives of the image intensity values along the x, y and time dimensions respectively, \vec = (x,y),v(x,y)\top is the optical flow vector (which is to be solved ''for''), and the parameter \alpha is a regularization constant. Larger values of \alpha lead to a smoother flow. This functional can be mini ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Optical Flow
Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and a scene. Optical flow can also be defined as the distribution of apparent velocities of movement of brightness pattern in an image. The concept of optical flow was introduced by the American psychologist James J. Gibson in the 1940s to describe the visual stimulus provided to animals moving through the world. Gibson stressed the importance of optic flow for affordance perception, the ability to discern possibilities for action within the environment. Followers of Gibson and his ecological approach to psychology have further demonstrated the role of the optical flow stimulus for the perception of movement by the observer in the world; perception of the shape, distance and movement of objects in the world; and the control of locomotion. The term optical flow is also used by roboticists, encompassing related techniq ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Affine Mapping
In Euclidean geometry, an affine transformation or affinity (from the Latin, ''affinis'', "connected with") is a geometric transformation that preserves line (geometry), lines and parallelism (geometry), parallelism, but not necessarily Euclidean distances and angles. More generally, an affine transformation is an automorphism of an affine space (Euclidean spaces are specific affine spaces), that is, a Function (mathematics), function which Map (mathematics), maps an affine space onto itself while preserving both the dimension of any affine subspaces (meaning that it sends points to points, lines to lines, planes to planes, and so on) and the ratios of the lengths of Parallel (geometry), parallel line segments. Consequently, sets of parallel affine subspaces remain parallel after an affine transformation. An affine transformation does not necessarily preserve angles between lines or distances between points, though it does preserve ratios of distances between points lying on a str ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kanade–Lucas–Tomasi Feature Tracker
In computer vision, the Kanade–Lucas–Tomasi (KLT) feature tracker is an approach to feature extraction. It is proposed mainly for the purpose of dealing with the problem that traditional image registration techniques are generally costly. KLT makes use of spatial intensity information to direct the search for the position that yields the best match. It is faster than traditional techniques for examining far fewer potential matches between the images. The registration problem The traditional image registration problem can be characterized as follows: Given two functions F(x) and G(x), representing pixel values at each location x in two images, respectively, where x is a vector. We wish to find the disparity vector h that minimizes some measure of the difference between F(x+h) and G(x), for x in some region of interest R. Some measures of the difference between F(x+h) and G(x): * L1 norm = \sum_\left\vert F(x+h)-G(x) \right\vert * L2 norm = \sqrt * Negative of normalized correla ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Extrapolation
In mathematics, extrapolation is a type of estimation, beyond the original observation range, of the value of a variable on the basis of its relationship with another variable. It is similar to interpolation, which produces estimates between known observations, but extrapolation is subject to greater uncertainty and a higher risk of producing meaningless results. Extrapolation may also mean extension of a method, assuming similar methods will be applicable. Extrapolation may also apply to human experience to project, extend, or expand known experience into an area not known or previously experienced so as to arrive at a (usually conjectural) knowledge of the unknownExtrapolation entry at Merriam–Webster (e.g. a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Outlier
In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to a variability in the measurement, an indication of novel data, or it may be the result of experimental error; the latter are sometimes excluded from the data set. An outlier can be an indication of exciting possibility, but can also cause serious problems in statistical analyses. Outliers can occur by chance in any distribution, but they can indicate novel behaviour or structures in the data-set, measurement error, or that the population has a heavy-tailed distribution. In the case of measurement error, one wishes to discard them or use statistics that are robust to outliers, while in the case of heavy-tailed distributions, they indicate that the distribution has high skewness and that one should be very cautious in using tools or intuitions that assume a normal distribution. A frequent cause of outliers is a mixture of two distributions, which may be two dist ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Video Tracking
Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing. Video tracking can be a time-consuming process due to the amount of data that is contained in video. Adding further to the complexity is the possible need to use object recognition techniques for tracking, a challenging problem in its own right. Objective The objective of video tracking is to associate target objects in consecutive video frames. The association can be especially difficult when the objects are moving fast relative to the frame rate. Another situation that increases the complexity of the problem is when the tracked object changes orientation over time. For these situations video tracking systems usually employ a motion model which describes how the image o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Corner Detection
Corner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, image registration, video tracking, image mosaicing, panorama stitching, 3D reconstruction and object recognition. Corner detection overlaps with the topic of interest point detection. Formalization A corner can be defined as the intersection of two edges. A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point. An interest point is a point in an image which has a well-defined position and can be robustly detected. This means that an interest point can be a corner but it can also be, for example, an isolated point of local intensity maximum or minimum, line endings, or a point on a curve where the curvature is locally maximal. In practice, most so-called corner detection methods detect interest po ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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