A discussion is presented of a facet model for image data which has the potential for fitting the form of the real idealized image, and for describing how the observed image differs from the idealized form. It is also...
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A discussion is presented of a facet model for image data which has the potential for fitting the form of the real idealized image, and for describing how the observed image differs from the idealized form. It is also an appropriate form for a variety of imageprocessing algorithms. Then the authors give a relaxation procedure, and prove its convergence, for determining an estimate of the ideal image from observed image data.
The focus of this study is to develop an understanding of the state of the art in visual motion perception by intelligent systems. The authors examine diverse theoretical and empirical approaches to visual motion anal...
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The focus of this study is to develop an understanding of the state of the art in visual motion perception by intelligent systems. The authors examine diverse theoretical and empirical approaches to visual motion analysis, perception, and understanding. Emphasis is placed on issues of observed object and image sequence description, representation, and perceptual control strategies. The author introduces two concepts in visual motion perception: motion vantage perspective and object motion coherence.
This study deals with an optimization technique applied to the problem of stochastic labeling. The authors propose a definition of a global criterion on a set of objects to be labeled that combines both ambiguity and ...
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This study deals with an optimization technique applied to the problem of stochastic labeling. The authors propose a definition of a global criterion on a set of objects to be labeled that combines both ambiguity and consistency with adjustable weights. A projected gradient algorithm is developed to minimize the criterion. Results are shown on a toy example and on the edge detection problem. Comparisons are made with relaxation labeling techniques.
The problem of texture discrimination is considered. Random walks are performed in a plane domain D bounded by an absorbing boundary GAMMA , and the absorption distribution is calculated. Measurements derived from suc...
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The problem of texture discrimination is considered. Random walks are performed in a plane domain D bounded by an absorbing boundary GAMMA , and the absorption distribution is calculated. Measurements derived from such distributions are the features used for discrimination. Experiments using the model are performed and results are shown.
Let a vector of probabilities be associated with every node of a graph. These probabilities define a random variable representing the possible labels of the node. Probabilities at neighboring nodes are used iterativel...
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Let a vector of probabilities be associated with every node of a graph. These probabilities define a random variable representing the possible labels of the node. Probabilities at neighboring nodes are used iteratively to update the probabilities at a given node based on statistical relations among node labels. The results are compared with previous work on probabilistic relaxation labeling, and examples are given from the image segmentation domain. References are also given to applications of the new scheme in text processing.
Consideration is given to a class of patterns which are subject to the action of a group of transformations. The author is particularly concerned with the existence of measurements or features which are invariant with...
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Consideration is given to a class of patterns which are subject to the action of a group of transformations. The author is particularly concerned with the existence of measurements or features which are invariant with respect to transformation. A concept of relative invariance is also introduced and explored in depth. In a very general sense, it is shown that every invariant (and relative invariant) is a suitable average over the relevant group of transformations. Finally, invariant means of bounded functions are used to explore existence of pattern invariants. Suggestions for further research are also given.
An examination is made of the fuzzy approach of E. H. Ruspini to the problem of pattern classification. The problem of classification as that of estimating a partition of the data to be classified is presented. An alg...
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An examination is made of the fuzzy approach of E. H. Ruspini to the problem of pattern classification. The problem of classification as that of estimating a partition of the data to be classified is presented. An algorithm is presented for classifying data issued from a Gaussian environment; the fundamental tool of this algorithm is the use of numerical filters for estimating a set of parameters which characterize each class. This algorithm has been applied to the recognition of the components of a mixture of Normal distributions.
Simulation results are presented for motion compensated hybrid transform/DPCM image coders using coefficient-recursive displacement estimation. computer simulations on two typical real-life sequences of frames show th...
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Simulation results are presented for motion compensated hybrid transform/DPCM image coders using coefficient-recursive displacement estimation. computer simulations on two typical real-life sequences of frames show that displacement based (motion compensated) coefficient prediction results in coder bit rates that are 20 to 40 percent lower than conventional interframe transform coders using 'frame difference of coefficients'.
The adaptive image filtering considered in this study includes a Kalman filter for noisy image enchancement and a generalized likelihood ratio technique to detect and estimate the jumps corresponding to object boundar...
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The adaptive image filtering considered in this study includes a Kalman filter for noisy image enchancement and a generalized likelihood ratio technique to detect and estimate the jumps corresponding to object boundaries. The filter is adjusted when the jump is detected. When the transition matrix of the filter is unknown, it is determined by a method of simultaneous on-line estimation of parameters and states. Both the mathematical analysis and computer results are presented in detail. The procedures involved are highly effective and flexible, and computationally efficient.
This study presents a suboptimal boundary estimation algorithm for noisy images which is based upon an optimal maximum likelihood problem formulation. Both the maximum likelihood formulation and the resulting algorith...
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This study presents a suboptimal boundary estimation algorithm for noisy images which is based upon an optimal maximum likelihood problem formulation. Both the maximum likelihood formulation and the resulting algorithm are described in detail, and computational results are given. In addition, the potential power of the likelihood formulation is demonstrated through the presentation of three simple but insightful analyses of algorithm performance.
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