The following topics are dealt with: communication theory;imageprocessing and understanding;reflector antennas;educational technology;expert systems;artificial intelligence systems and models;electronic systems and d...
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ISBN:
(纸本)0818607173
The following topics are dealt with: communication theory;imageprocessing and understanding;reflector antennas;educational technology;expert systems;artificial intelligence systems and models;electronic systems and design;optical communications and systems;image analysis and classification;speech processing;signal detection systems and theory;computer communications networks;optimal and nonlinear control;linear systems analysis;time-varying systems;power systems analysis and dynamics, and the use of microcomputers;power distribution systems;robotics;digital control systems;computer architecture;filter design;and stochastic systems. 118 papers were presented, of which 106 are published in the present proceeding.
An efficient recursive algorithm is presented for convolving an image with the most successfully used filters in edge detection: the two-dimensional (2-D) Gaussian filter, its first directional derivatives, and its La...
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An efficient recursive algorithm is presented for convolving an image with the most successfully used filters in edge detection: the two-dimensional (2-D) Gaussian filter, its first directional derivatives, and its Laplacian. The key points of the approach are the use of the separability, the exact decomposition of the Laplacian of a 2-D Gaussian filter into a difference of two separable filters, and the use of recursive filtering. An approximation iterative algorithm is used to determine the coefficients of the one-dimensional IIR filter of order n that best approximates in a least-squares sense each separable filter. The technique results in a substantial saving in computational effort and a gain ratio of roughly (2 multiplied by sigma )/3, and makes the use of large filters practical. The performance of the approximation algorithm is illustrated and experimental results for multiscale edge detection on real images, using zero-crossings and nonmaxima suppression, are presented.
The solution of many estimation problems can be greatly enhanced by the incorporation of inexact knowledge or vague human reasoning. For such estimation problems, two distinct forms of problem knowledge can be identif...
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The solution of many estimation problems can be greatly enhanced by the incorporation of inexact knowledge or vague human reasoning. For such estimation problems, two distinct forms of problem knowledge can be identified: statistical (objective) knowledge and heuristic (subjective) knowledge. This paper discusses a systematic way of expressing and integrating these two forms of knowledge into the estimation process. This work can be interpreted as a fuzzification of standard constrained optimization. Fuzzy set theory is used to form a fuzzy constraint which represents the domain-specific knowledge of human expert. This work may also be interpreted as a systemization of the use of subjective priors by Bayesians. Although our work is of general applicability, we demonstrate the use of heuristically constrained estimation to the particular problem of seismic deconvolution. These results show that the incorporation of heuristic knowledge (albeit vague) yields better results than if such knowledge is ignored.
This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise en...
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This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
The transformation of imagesignals between the continuous domain of the real world and the discrete domain of modern data processing can have a significant effect on quality and efficiency. Analysis based on perceptu...
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The transformation of imagesignals between the continuous domain of the real world and the discrete domain of modern data processing can have a significant effect on quality and efficiency. Analysis based on perceptual rather than mathematical considerations has been carried out. A series of experiments based on the analysis has shown that substantial improvement over usual techniques can be achieved by the use of a cascade of a presharpening filter combined with Gaussian presampling and interpolation filters. The resulting ``sharpened Gaussian"" filter, although not exactly circularly symmetrical, gives a high degree of isotropy. Each element in the cascade is separable, so that computational efficiency is high. A favorable tradeoff is achieved among sharpness, smoothness, and the effects of aliasing. Subjective testing, in comparison with other commonly used filters, has shown the clear superiority of this filter.","doi":"10.1109/TPAMI.1985.4767642","publicationTitle":"ieee Transactions on Pattern Analysis and Machine intelligence","startPage":"178","endPage":"186","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&publication=0162-8828&title=Transformation+Between+Continuous+and+Discrete+Representations+of+images%3A+A+Perceptual+Approach&isbn=&publicationDate=March+1985&author=William+F.+Schreiber&ContentID=10.1109/TPAMI.1985.4767642&orderBeanReset=true&startPage=178&endPage=186&volumeNum=PAMI-7&issueNum=2","displayPublicationTitle":"ieee Transactions on Pattern Analysis and Machine intelligence","pdfPath":"/iel5/34/4767634/***","keywords":[{"type":"ieee Keywords","kwd":["Filters","Sampling methods","Interpolation","Frequency","image quality","image converters","Hydrogen","image sampling","signalprocessing","Gaussian processes"]}],"allowComments":false,"pubLink":"/xpl/***?punumber=34","issueLink":"/xpl/***?isnumber=4767634","standardTitle":"Transformation Between Continuous and Discrete Representations o
The co-occurrence matrix, a two-dimensional histogram of pairs of sample amplitudes, is explored as a representation of the digital speech waveform. Co-occurrence matrix representations support a hypothesis-testing ap...
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The co-occurrence matrix, a two-dimensional histogram of pairs of sample amplitudes, is explored as a representation of the digital speech waveform. Co-occurrence matrix representations support a hypothesis-testing approach to digital speech analysis. This approach is pursued in the formulation of a quantitative (chi-square) measure of sample amplitude dependence, based on co-occurrence matrices. This measure, which is higly sensitive to quasi-periodicity, is shown to lead to a good estimator of the pitch period of voiced speech. Co-occurrence matrix representations are employed in conjunction with pattern classification methods in experiments involving the voiced-unvoiced-silence analysis of speech, and in an experimental pitch extraction algorithm which is tested on continuous speech.
作者：
Lee, M.H.Kim, D.Y.Jeonbook Natl Univ
Dep of Electronics Engineering Jeonju South Korea Jeonbook Natl Univ Dep of Electronics Engineering Jeonju South Korea
The Hadamard matrix is a symmetric matrix made of plus and minus ones as entries. The use of Hadamard transforms in imageprocessing requires only real number operations and results in the computational advantages. Re...
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The Hadamard matrix is a symmetric matrix made of plus and minus ones as entries. The use of Hadamard transforms in imageprocessing requires only real number operations and results in the computational advantages. Recently, however, certain degradation aspects have been reported. We propose a W-H matrix which retains the main properties of the Hadamard matrix. The actual improvement of the image transmission in the inner part of the picture has been demonstrated by computer simulated image developments. Orthogonal transforms, such as Hadamard transforms, offer a useful facility in digital signalprocessing. As the size of the transmission block increases, however, the assignment of bits for each datum must increase exponentially. Thus the snr of the image tends to decline accordingly. As an attempt to increase the snr, we propose the W-H matrix whose elements are made of plus or minus 1, plus or minus 2, plus or minus 3, plus or minus 4, and the uniform of 8 multiplied by 8 matrix.
In order to describe the movement of the left ventricular wall, a method has been developed to assess the movement of its center of mass using ECG-gated Tl-201 scintigraphy of the left ventricle. Areas with Tl-201 upt...
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ISBN:
(纸本)0818605448
In order to describe the movement of the left ventricular wall, a method has been developed to assess the movement of its center of mass using ECG-gated Tl-201 scintigraphy of the left ventricle. Areas with Tl-201 uptake outside the left ventricle were rejected by delineating the object in scintigrams with extremely low signal-to-noise ratios and estimating a background count density. The DIODA-contour detection program demonstrated its worth even in these extreme circumstances.
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