PASM, a multimicroprocessor system being designed at Purdue Univeristy for imageprocessing.and patternrecognition, is described. This system can be dynamically reconfigured to operate as one or more independent SIMD...
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PASM, a multimicroprocessor system being designed at Purdue Univeristy for imageprocessing.and patternrecognition, is described. This system can be dynamically reconfigured to operate as one or more independent SIMD and/or MIMD machines. The functions that the PASM operating system will perform are discussed, demonstrating how it will handle a variety of types of imageprocessing.tasks. Examples of how PASM will improve computational speeds in comparison to conventional computers are presented. In particular, smoothing, histogram, and two-dimensional FFT algorithms are analyzed.
A method is described that generates 3D descriptions of arbitrarily-curved surfaces of physical objects by imageprocessing.and photogrammetric techniques. A surface that is to be described is illuminated by a pattern...
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A method is described that generates 3D descriptions of arbitrarily-curved surfaces of physical objects by imageprocessing.and photogrammetric techniques. A surface that is to be described is illuminated by a pattern of lines. The 3D shape of the surface causes the pattern to be distorted. The illuminated parts of the surface are photographed from two or more vantage points and the resulting images are digitized. The 2D perspective projection of the surface-distorted line pattern is extracted from each digitized image. Camera calibration marks are also extracted and the position of the camera is determined for each image. Two or more projections of the line pattern are then matched and the line pattern projected on the surface is reconstructed into 3D.
In video understanding, the spatial patterns formed by local space-time interest points hold discriminative information. We encode these spatial regularities using a word2vec neural network, a recently proposed tool i...
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ISBN:
(纸本)9781479943098
In video understanding, the spatial patterns formed by local space-time interest points hold discriminative information. We encode these spatial regularities using a word2vec neural network, a recently proposed tool in the field of text processing. Then, building upon recent accumulator based image representation solutions, input videos are represented in a hybrid manner: the appearance of local space time interest points is used to collect and associate the learned descriptors, which capture the spatial patterns. Promising results are shown on recent action recognition benchmarks, using well established methods as the underlying appearance descriptors.
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.
It is proposed that the curves commonly encountered in picture processing.can be described by a sequence of arcs linked at critical points. A statistical model is developed which assumes that the locations of the crit...
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It is proposed that the curves commonly encountered in picture processing.can be described by a sequence of arcs linked at critical points. A statistical model is developed which assumes that the locations of the critical points are statistically independent. The model is used to develop and analyze algorithms for determining such critical points.
This work explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of alg...
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This work explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose error is, as we formally show, close to the best possible. This approach can be approximated in a very fast segmentation algorithm for processing.images described using most common numerical feature spaces. Simple modifications of the algorithm allow to cope with occlusions and/or hard noise levels. Experiments on grey-level and color images, obtained with a short C-code, display the quality of the segmentations obtained.
The objective of computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. The system consists of three different types of units, two of which are associative dat...
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The objective of computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. The system consists of three different types of units, two of which are associative data memories implemented as relational databases. The short term memory (STM) contains the raw color picture data and the most current interpretations and deductions about the original scene. The long term memory (LTM) contains a detailed model of the scene under consideration. Results obtained with this system are presented.
Many applications in picture processing.require accurate slope estimation. A brief survey of these applications is followed by discussion of the Sobel edge detection operator. The Sobel operator is very fast and simpl...
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Many applications in picture processing.require accurate slope estimation. A brief survey of these applications is followed by discussion of the Sobel edge detection operator. The Sobel operator is very fast and simple;however, it is inherently biased as a function of true slope value and displacement of the true edge from center of the processing.window. An iterative version of the operator is presented which reduces this bias with only a moderate increase in required processing.
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.
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.
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