A novel hardware architecture for extracting region boundaries in two raster scan passes through a binary image is presented. The first pass gathers statistics regarding the size of each object contour. In the second ...
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A novel hardware architecture for extracting region boundaries in two raster scan passes through a binary image is presented. The first pass gathers statistics regarding the size of each object contour. In the second raster pass the same architecture constructs lists of grid-joint codes to represent the perimeter pixels of each object. These codes, referred to variously as 'crack' codes or 'raster-chain' codes in the literature, are later decoded by the hardware to reproduce the ordered sequence of coordinates surrounding each object. This list of coordinates is useful for a variety of shape recognition and manipulation algorithms that utilize boundary information. We present results of software simulations of the VLSI architecture, along with measurements on the coding efficiency of the basic algorithm, and estimates of the overall complexity of a proposed VLSI chip.
It is widely recognized that effective imageprocessing and machinevision must involve the use of information at multiple scales, and that models of human vision must be multi-scale as well. The most commonly used im...
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ISBN:
(纸本)0819402389
It is widely recognized that effective imageprocessing and machinevision must involve the use of information at multiple scales, and that models of human vision must be multi-scale as well. The most commonly used image representations are linear transforms, in which an image is decomposed into a sum of elementary basis functions. Besides being well understood, linear transformations which can be expressed in terms of convolutions provide a useful model of early processing in the human visual system various properties which are valuable for linear transforms that are to be used in imageprocessing and vision modelling are defined and discussed.
A novel, massively parallel system is presented for image memorization, processing, and reconstruction based on the use of magnetic thin films and optomagnetic control. images in the form of pixel arrays are imposed o...
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A novel, massively parallel system is presented for image memorization, processing, and reconstruction based on the use of magnetic thin films and optomagnetic control. images in the form of pixel arrays are imposed on the film by locally heating it in the regions of high image intensity, using a laser with an external magnetic field. One of these stored images is recalled when a partial or corrupted version of that image is imposed on the film. The system can also be used for feature detection and image compression.
The Warwick Pyramid machine is a large parallel architecture for imageprocessing and computer visionapplications. It consists of several distinct processing layers, each well suited to a particular class of operatio...
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The Warwick Pyramid machine is a large parallel architecture for imageprocessing and computer visionapplications. It consists of several distinct processing layers, each well suited to a particular class of operations: a very fine grain array of numeric processing elements, one per pixel;an intermediate array of scalar processors, capable of providing independent local control of regions within the image;and a loosely coupled array of powerful, general purpose processors for symbolic manipulation. In this paper we describe our current implementation of this architecture, in the form of both software simulators and hardware prototypes combining the DAP and transputers, and show how the machine is suited to performing iconic to symbolic data transformation. A number of representative algorithms are described, including low-level signal processing, feature extraction, segmentation and classification, as well as applications in the field of graphics.
The authors present OLivE, an object-oriented language for machinevision and imageprocessing that is intended to make it easier to develop efficient, portable applications. The goal in defining OLivE is to provide a...
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ISBN:
(纸本)0818619848
The authors present OLivE, an object-oriented language for machinevision and imageprocessing that is intended to make it easier to develop efficient, portable applications. The goal in defining OLivE is to provide a well-factored, intuitive, portable tool for imageprocessing and analysis that insulates the user from details of the implementation while at the same time exploiting its capabilities (e.g., specialized hardware). The authors define OLivE's principal object types - images and locuses (abstractions of point sets and geometric entities) - and their corresponding operations, including the use of locuses as generalized indexes for images. They present several examples of OLivE for typical imageprocessing and machinevision tasks. They discuss issues in the implementation of OLivE, including a hardware architecture that simplifies the implementation while enhancing its performance.
Surface defects such as holes, dirt and scratches cause major problems for manufacturers, particularly when the production process includes a surface treatment stage. This paper defines a surface defect and reviews im...
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Surface defects such as holes, dirt and scratches cause major problems for manufacturers, particularly when the production process includes a surface treatment stage. This paper defines a surface defect and reviews imageprocessing algorithms for defect detection, severity measurement and classification. A number of new techniques which have been developed for use on a dedicated machinevision defect detection system are presented and their applications discussed.
This publication contains 158 workshop papers in summary form only. The following topics are dealt with: image understanding;biomedical radar, and geophysical applications;machinevision algorithms, architectures, and...
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This publication contains 158 workshop papers in summary form only. The following topics are dealt with: image understanding;biomedical radar, and geophysical applications;machinevision algorithms, architectures, and applications;matched field processing;multidimensional spectral estimation;mathematical models and multidimensional signal processing;sensor array processing;image enhancement and restoration;image reconstruction;and image coding and data compression.
An approach to segmenting a gray-level image is presented and used to reconstruct the three-dimensional shape of unconstrained objects. In analyzing a natural image, the authors cannot utilize heuristic rules that con...
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An approach to segmenting a gray-level image is presented and used to reconstruct the three-dimensional shape of unconstrained objects. In analyzing a natural image, the authors cannot utilize heuristic rules that constrain the degree of freedom of reconstruction of the scene. Therefore they use local feature-based clustering, which utilizes the local distribution of the features. This clustering is based on an image itself and is considered as object-oriented processing. The edge detection problem is discussed as an inverse problem to clustering. Clustering methods which utilize both lowest features (for all pixels) and the features a little higher up are discussed with respect to their ability for exact segmentation.
The problem in document analysis of the conversion of technical drawings into descriptions that are appropriate to be manipulated by CAD systems is addressed. Ideas are presented for the construction of 3-D-geometric ...
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The problem in document analysis of the conversion of technical drawings into descriptions that are appropriate to be manipulated by CAD systems is addressed. Ideas are presented for the construction of 3-D-geometric models from noisy and inaccurate images of simple workpiece drawings. The main ideas are the search for instances of primitive solids in the image, the incremental construction of partial descriptions based on these primitives, and their manipulation to achieve consistency with the image data. The implementation of the method is described, limitations are discussed, and the possible future development of the approach is outlined.
Two kinds of Fourier shape descriptors (FDs) are considered in this article: ZR (defined by Zahn and Roskies) and G (defined by Granlund). The first section of this article studies ZR descriptors. Four modifications o...
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Two kinds of Fourier shape descriptors (FDs) are considered in this article: ZR (defined by Zahn and Roskies) and G (defined by Granlund). The first section of this article studies ZR descriptors. Four modifications of ZR descriptors are proposed. These new descriptors are based on the step signature, smoothed signature, linearized smoothed signature, and the curvature function, respectively. The amplitudes of FDs are shown to be invariant under rotations, translations, changes in size, mirror reflections, and shifts in the starting point. In all cases the reconstruction accuracy in terms of the number of FDs is studied, resulting in approximation error bounds. An efficient reconstruction formula based on the composite rectangle integration method is proposed. This method also provides polygonal approximation for arbitrary contours. In the second section of this article, the theoretical results are illustrated with examples and verified in numerical experiments involving digitized patterns. In the same experiments the performances of ZR and G descriptors are compared.
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