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13th International Conference on Pattern Recognition, ICPR 1996

作者： Menard, Christian Leonardis, Aleš Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 3A-1040 Vienna Austria University of Ljubljana Faculty of Computer and Information Science Computer Vision Laboratory Ljubljana Slovenia

ISBN: (纸本)081867282X

Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results that cannot be corrected in subsequent postprocessing stages. In this paper we present a modification of the standard area-based correlation approach so that it can tolerate a significant number of outliers. The approach exhibits a robust behavior not only in the presence of mismatches but also in the case of depth discontinuities. The confidence measure of the correlation and the number of outliers provide two complementary sources of information which, when implemented in a multiresolution framework, result in a robust and efficient method. We present the results of this approach on a number of synthetic and real images. © 1996 IEEE.

关键词： Stereo image processing

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3D segmentation and labeling using self-organizing Kohonen network for volumetric measurements on brain CT imaging to quantify TBI recovery

3D segmentation and labeling using self-organizing Kohonen n...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： M.N. Ahmed A.A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses. Segmentation is performed by assigning a feature pattern to each voxel, consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is input to Kohonen network for an unsupervised classification of the voxels into regions.

关键词： Labeling Volume measurement image segmentation Computed tomography Layout Electric variables measurement Engineering in Medicine and Biology Society Brain injuries Data mining Filters

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A new time series classification approach

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SIGNAL processing 1996年第2期54卷 191-199页

作者： Plataniotis, KN Androutsos, D Venetsanopoulos, AN Lainiotis, DG Department of Electrical and Computer Engineering Digital Signal & Image Processing Laboratory University of Toronto 10 King's College Rd. Toronto Ontario M5S 3G4 Canada Intelligent Systems Technology 3207 S. Hwy. A1A Melbourne Beach FL-32951 USA

A new, robust and computationally attractive approach to the problem of time series classification is discussed in this paper. Both the Bayesian as well as a new adaptive classification scheme for source selection are... 详细信息

关键词： time series classification neural networks logistic function

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Robust stereo on multiple resolutions

Robust stereo on multiple resolutions

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International Conference on Pattern Recognition

作者： C. Menard A. Leonardis Department for Pattern Recognition and Image Processing Technical University of of Vienna Vienna Austria Computer and InformationScience. Computer Vision Laboratory University of Ljubljana Ljubljana Slovenia

关键词： Stereo vision Computer vision Noise robustness Information resources Correlation Pattern recognition image processing Layout Cameras Laboratories

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Data-adaptive algorithms for signal detection in impulsive noise modeled as a subGaussian, alpha-stable process

Data-adaptive algorithms for signal detection in impulsive n...

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IEEE Workshop on Statistical Signal and Array processing

作者： G.A. Tsihrintzis C.L. Nikias Communication Systems Laboratory Department of Electrical Engineering University of Virginia Charlottesville VA USA Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

We address the problem of coherent detection of a signal embedded in heavy-tailed noise modeled as a subGaussian, alpha-stable process. We assume that the signal is a complex-valued vector of length L, known only within a multiplicative constant. The dependence structure of the noise, i.e., the underlying matrix of the sub Gaussian process, is not known. The intent is to implement a generalized likelihood ratio detector which employs robust estimates of the unknown noise underlying matrix and the unknown signal strength. The performance of the proposed adaptive detector is compared to that of an adaptive matched filter that uses Gaussian estimates of the noise underlying matrix and the signal strength and is found to be clearly superior. The proposed new algorithms an evaluated via Monte-Carlo simulation.

关键词： Signal detection Interference Signal processing Signal processing algorithms Detectors Gaussian noise Signal design Probability distribution image processing Noise robustness

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Robust recovery of eigenimages in the presence of outliers and occlusions

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Journal of Computing and Information Technology 1996年第1期4卷 25-38页

作者： Leonardis, Aleš Bischof, Horst Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 ViennaA-1040 Austria Computer Vision Laboratory Faculty of Computer and Information Science University of Ljubljana Tržaška 25 Ljubljana1000 Slovenia

The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way how the coefficients of the eigenimages are determined. Instead of computing the coefficients by a projection of the data onto the eigenimages. we extract them by a hvpothesize-and-test paradigm using subsets of image points. Competing hypotheses arc then subject to a selection procedure based on the Minimum Description Length principle. The approach enables us not only lo reject outliers and to deal with occlusions but also to simultaneously use multiple classes of eigenimages.

关键词： Principal component analysis

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Optical detection of breaks and short circuits on the layouts of printed circuit boards using CNN

Optical detection of breaks and short circuits on the layout...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： P. Szolgay K. Tomordi Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Image Processing and Neurocomputing Department Faculty of Engineering University of Veszprem Veszprem Hungary

The printed circuit board layout inspection methods are mostly based on local geometric information, therefore it is well suited to the cellular neural network (CNN) paradigm. Two layout errors are detected here namely, the breaks in the wires and some kind of short circuits. The designed analogic algorithms to solve the problems above were tested on real life examples using an experimental system based on our CNN-HAC1M digital multiprocessor add-on-board, with 1 million cell space and 2.0 /spl mu/s/cell/iteration speed.

关键词： Optical detectors Cellular neural networks Geometrical optics Optical computing Printed circuits Inspection Wires Algorithm design and analysis Circuit testing Life testing

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Thresholding methods considering the quantization error of an image

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systems AND COMPUTERS IN JAPAN 1996年第9期27卷 63-71页

作者： Sekita, I Kurita, T Otsu, N Abdelmalek, NN Electrotechnical Laboratory Tsukuba Japan 305 Ottawa Ontario Canada Nabih Nessim Abdelmalek:received his B.S. degree in Electrical Engineering in 1951 and his B.S. degree in Mathematics in 1954 both from Cairo University Egypt and his Ph.D. degree in Theoretical Nuclear Physics in 1958 from Manchester University U.K. After 7 years in the Department of Mathematics on the Faculty of Engineering Cairo University he moved to Canada and became a member of the scientific staff at Bell Northern Research in Ottawa. After a period of 2 years he joined the National Research Council of Canada in Ottawa. His current interest is the application of numerical analysis techniques to the problem of image processing particularly image enhancement and restoration data compression pattern classification parameter estimation and 3D robot vision.

Thresholding techniques are fundamental for region segmentation of a gray level image. It is often realistic to assume that gray pixel levels are subject to a mixture of normal distributions. This paper points out problems caused by the underestimation of variances from a frequency distribution and solves the problems by transforming a frequency distribution into a piecewise-continuous distribution based on the maximum entropy criterion. This transformation improves conventional thresholding techniques. Experimental results showed the effectiveness of the transformation.

关键词： threshold quantization mixture of normal distributions frequency distribution histogram

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Measuring the distance and orientation of a planar surface using nonstructured lighting

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systems AND COMPUTERS IN JAPAN 1996年第8期27卷 83-92页

作者： Tsukiyama, T Member Electrotechnical Laboratory Tsukuba Japan 305 Tosbifumi Tsukiyama:graduated in 1972 from the Biological Engineering Department Osaka University where he also received his Dr. of Eng. degree. He joined Electrotechnical Laboratory Ministry of Tread and Industry (Japan) in 1972. At present he is Senior Researcher at Computer Vision Section Intelligent Systems Division ETL. He was a Visiting Researcher at University of Illinois during 1984–1985. He has been interested in vision system for mobile robots and image processing architectures. He is a member of the Society of Instrumental and Control Engineers Japan Robotics Society of Japan and Information Processing Society of Japan.

This paper proposes a method of 3-dimensional measurement of a planar surface by using two fixed light sources and a TV camera. A set of two images is recorded by the camera switching on each light alternately. The peak of the luminance distribution in each image is detected, and the orientation and distance of the planar surface are calculated. The position of the peak of a luminance distribution can be estimated accurately by using an image processing. The light sources can be conventional apparatus with no particular structure. The method is simple and suitable for a vision system on an indoor mobile robot.

关键词： distance to surface orientation of planar surface point light source luminance distribution ring light

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Adaptability of K-L expansion technique for ambiguous face image retrieval

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ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE 1996年第10期79卷 35-46页

作者： Oda, M Akamatsu, S Fukamachi, H ATR Human Information Processing Laboratories Kyoto Japan 619-02 Shigeru Akamatsu received his B.E. M.E. and Dr. of Eng. degrees in Mathematical Enginwring and Instrumentation Physics in 1975 1977 and 1994 respectively from the University of Tokyo Japan. In 1977 he joined the Electrical Communications Laboratories Nippon Telegraph and Telephone Public Corporation Yokosuka Japan. From 1977 through 1985 he was engaged in the research and development of optical character recognition systems for handwritten Chmese characters. During the academic year 1985-86 he was a Visiting Researcher at the University of California Irvine California U.S.A. Until 1992 he was a Senior Research Engineer Supervisor at W Human Interface Laboratories and conducted research on human image recognition with a special interest in face recognition. Since 1992 he has been with ATR Human Information Processing Research Laboratories Kyoto Japan where he is currently Head of Department 2. His research interests include computational and cognitive studies on high-level vision with a special interest in facial information processing by man and computer. He is a member of the Institute of Electronics Information and Communication Engineers of Japan the Information Processing Society of Japan ACM and the IEEE Computer Society. Hidm Fukamachi received his B.E. degree in Image Technology from the Tokyo Institute of Polytechnics in 1990. He joined NTT Software Corp. in 1990 where he was engaged in the development of image processing software. Since 1993 he has been with ATR Human Information Processing Research Laboratories.

Some facial images are not necessarily clear images in the human brain. However, it can be easily judged whether a face matches the image in our mind;this is true even when the drawing or the expression of the target image is difficult to comprehend. This is because even if the image cannot be imagined like a picture, it does exist. The authors have proposed a system with which a retriever can retrieve an ambiguous target image from an image database. The system's retrieval efficiency and ease-of-retrieval were clarified by experiments using line drawn facial images. In this paper, the system is extended to photographs of faces. The most difficult problem in treating photographs is determining the retrieval key. One consideration is to add the subjective impressions or features of physical figures as keywords for each image data. Regardless of the method, however, significant manpower is necessary. The K-L expansion technique is used here, which has been verified as effective for face identification, and expect to need only a small amount of manpower. This paper investigates whether the technique can also be useful in ambiguous image retrieval.

关键词： ambiguous image image retrieval K-L expansion facial image subjective similarity

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