作者:
M. ShawkyK.M. HouX.W. TuCNRS
URA 817 Heuristique & Diagnostic des Systèmes Complexes Universittaté de Technologie de Compiègne Compiegne France
Nowadays, autonomous vehicles are undergoing a distinct evolution, taking advantage of the recent technological progress in computer architectures. the development tools are more sophisticated, forwarding the trend to...
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Nowadays, autonomous vehicles are undergoing a distinct evolution, taking advantage of the recent technological progress in computer architectures. the development tools are more sophisticated, forwarding the trend to dedicated architectures. In the paper, the authors consider a parallel vision sub-system integrated in an overall architecture for a mobile robot (indoor autonomous vehicle). the system modules work in parallel, communicating through a hierarchical blackboard, an extension of the 'tuple space' from LINDA concepts, where they may exchange data or synchronization messages. the processing elements are of different skills, built around 50 MHz i860-XP Intel RISC processors for high level processing, and pipelined systolic array processors based on PLAs or FPGAs for low level imageprocessing.< >
the paper concerns computational models and languages for iterative cellular automata for imageprocessing applications. the authors present some formal models, with different computational resources, that can be usef...
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the paper concerns computational models and languages for iterative cellular automata for imageprocessing applications. the authors present some formal models, with different computational resources, that can be useful in the solution of certain algorithmic problems. they introduce some concepts such as memory splitting, conditional functions, dynamic neighborhood and supervisor automaton. the models defined lead to a parallel language structure that can express low-level imageprocessing algorithms in a clear and concise way. the language allows a transparent description of the algorithms and can be easily expandable to reflect the needs of people working in different branches of imageprocessing.< >
Rank order filters form an important class of low level image operations that have widespread applications in image smoothing, texture analysis, etc. In the paper, the authors study several ways of computing rank orde...
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Rank order filters form an important class of low level image operations that have widespread applications in image smoothing, texture analysis, etc. In the paper, the authors study several ways of computing rank order filters on processor array architectures. they also present a replicated data algorithm for efficient processing of small images on relatively large processor arrays. Results of implementing the algorithms on a Connection Machine CM-2 and a Mas-Par MP-1 are presented.< >
the primary task of intermediate level vision (ILV) is to take the output of low level vision, which is typically a subset of pixels from the original image array, and to generate a representation of image content whi...
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the primary task of intermediate level vision (ILV) is to take the output of low level vision, which is typically a subset of pixels from the original image array, and to generate a representation of image content which is appropriate for symbolic manipulations at a higher level. these tasks, e.g. boundary detection, various types of segmentation or the computation of attributes of image components, involve operations on individual pixels, sets of pixels with a common label or on entities extracted from the raw pixel data, such as orientation of lines or distance between pairs of parallel lines. A class of tasks which operate on individual pixels or sets of pixels is described, problems which are raised in parallel implementations of this class of tasks are considered, and solutions are suggested.< >
computervision and image understanding processes are not very robust; small changes in exposure parameters or in internal parameters of algorithms can lead to significantly different results. A combination (fusion) o...
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computervision and image understanding processes are not very robust; small changes in exposure parameters or in internal parameters of algorithms can lead to significantly different results. A combination (fusion) of these results is profitable. the authors introduce an extended fusion concept dealing with different sources of information at external (world, scene, image) and internal (image description, scene description) levels and define the process of fusion. Each level requires its own procedure of quality measure and information fusion in order to yield a combination of components from several sources. Related work in the field is reviewed. Examples from the authors' own work cover remote sensing (improvement of classification results by fusion at the image level), medical imageprocessing of ocular fundus images (automatic control point selection by fusion at the image description level) and the interpretation of Billard scenes (object identification by fusion at the scene description level).< >
Gives a brief description of the operation principles of LeaVis, a prototype industrial machine vision system aimed at processing and segmentation of the images of hides marked by lines and other symbols that show def...
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Gives a brief description of the operation principles of LeaVis, a prototype industrial machine vision system aimed at processing and segmentation of the images of hides marked by lines and other symbols that show defects and areas of different quality. A key feature of the vision system is its ability to correct line drawings by connecting broken lines and bridging gaps in line junctions. the goal of LeaVis is to provide a coherent visual input for a computer aided layout design system that creates trajectory descriptions by which the knife cutting the hides is controlled. the authors discuss the basic motivations of the vision system design, and present the ideas behind its core algorithms.< >
the interpretation of neural network behavior is of particular interest in neural network research. Visualization methods provide the necessary means to simultaneously analyze the huge amount of information hidden in ...
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the interpretation of neural network behavior is of particular interest in neural network research. Visualization methods provide the necessary means to simultaneously analyze the huge amount of information hidden in the network. the authors propose a framework for visualization methods suited for feed forward neural networks. the basic idea is to use the spatial information available outside the network to arrange the data to be visualized (weights, activations of units) in the spatial domain of the display. Several examples which illustrate the proposed framework are presented.< >
For pattern information processing like recognition or understanding, one must express the given pattern properly for later processing. Many ways have been developed to express the hierarchy of a pattern, but many of ...
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For pattern information processing like recognition or understanding, one must express the given pattern properly for later processing. Many ways have been developed to express the hierarchy of a pattern, but many of them use one dimensional or two dimensional patterns. It is important to find an expression reflecting the hierarchical structure of a spherical pattern. In this paper, the authors propose the scale space filtering on a spherical pattern to express its hierarchy.< >
Handwritten character recognition is typically classified as online or offline depending on the nature of the input data. Online data consists of a temporal sequence of instrument positions while offline data is in th...
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Handwritten character recognition is typically classified as online or offline depending on the nature of the input data. Online data consists of a temporal sequence of instrument positions while offline data is in the form of a 2D image of the writing sample. Online recognition techniques have been relatively successful but have the disadvantage of requiring the data to be gathered during the writing process. this paper presents work on the extraction of temporal information from static images of handwriting and its implications for character recognition.< >
In the imageprocessing literature many methods to segment 2D and 3D images have been presented. However, relatively little effort has been spent on the validation of the results of these methods. the goal of the pape...
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In the imageprocessing literature many methods to segment 2D and 3D images have been presented. However, relatively little effort has been spent on the validation of the results of these methods. the goal of the paper is to explore a validation methodology that is based on developing a task-directed quality norm that can be used as a constraint in cost analysis. In this methodology a segmentation method is evaluated by the cost reduction it provides relative to the cost of a full-interactive (manual) segmentation. this cost is constrained by a quality threshold, so that less-than-perfect segmentations are allowed. In this way segmentation methods can be compared, which are designed for the same task, but are different of nature.< >
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