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International Conference on pattern recognition

作者： U. Bhattacharya B.B. Chaudhuri S.K. Parui Computer Vision & Pattern Recognition Unit Indian Statistical Institute Calcutta India

In this paper we describe a texture segmentation approach without feature computation based on a multilayer perceptron network (MLP). Thus, the users need not bother about the selection and then computation of feature set and hence real-time segmentation may be possible. The basic motivation of the work is the fact that human vision does not consciously compute features for distinguishing different textures in a scene. A single hidden layer MLP network has been found to be most suitable with heuristically chosen input and hidden layer sizes. A method has been used to speedup the learning of the MLP network. The result of segmentation by a trained network usually results in misclassification in the form of speckles. For the removal of such noise an edge-preserving-noise-smoothing technique is proposed. The final segmentation accuracy is well comparable with that of other existing techniques.

关键词： computer vision Image segmentation Humans Layout pattern recognition Electronic mail computer networks Surface texture User-generated content Artificial neural networks

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Semi-automatic segmentation of tissue cells from confocal microscope images

Semi-automatic segmentation of tissue cells from confocal mi...

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International Conference on pattern recognition

作者： P.S.Umesh Adiga B.B. Chaudhuri K. Rodenacker Computer Vision & Pattern Recognition Unit Indian Statistical Institute Calcutta India Institute of Pathology GSF Munich Germany

We present a semi-automatic method for extracting the 3D boundary of the cells in a compact tissue cross-section photographed by a confocal microscope. The confocal microscope provides pictures at different depths of the cells which can be considered as the image slices of the tissue section. Segmentation of cell boundary from different image slices and combining them to obtain 3D surface automatically is a difficult task. We have developed an approach where given one segmented image slice, the other image slices can be automatically segmented in a layered approach. The idea is to use the information of the previous segmented image slice for segmenting the current image slice.

关键词： Image segmentation Microscopy Laser beams Image processing computer vision pattern recognition Pathology Lighting Hardware Laser noise

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Skeletal shape extraction from dot patterns by self-organization

Skeletal shape extraction from dot patterns by self-organiza...

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International Conference on pattern recognition

作者： A. Datta S.M. Parui B.B. Chaudhuri Computer and Statistical Service Centre Indian Statistical Institute Calcutta India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Calcutta India

Extraction of skeletal shape from a 2D dot pattern is discussed. We use a self-organizing neural network model to get a piecewise linear approximation of a skeleton of the pattern. It is found that even without a proper definition of a skeleton, the proposed algorithm is able to produce skeletons that are quite close to what we intuitively feel it should be. In Kohonen's self-organizing model, the set of processors and their neighbourhoods are fixed. We suggest here some modifications of it in which the set of processors and their neighbourhoods change adaptively.

关键词： Shape US Department of Transportation Skeleton Neural networks Network topology pattern recognition Piecewise linear approximation pattern analysis computer networks

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An efficient algorithm for detection of road-like structures in satellite images

An efficient algorithm for detection of road-like structures...

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International Conference on pattern recognition

作者： A. Mukherjee S.K. Parui D. Chaudhuri B.B. Chaudhuri R. Krishnan Indian Statistical Institute Computer Vision and Pattern Recognition Unit Calcutta India Advanced Data Processing Research Institute Secunderabad India

Road networks are important features of satellite imagery. The main contribution of the present road detection method consists of an effective enhancement technique and an efficient segmentation technique that removes non-road pixels step by step from the image where parameters involved: in each step images are determined by the sensor characteristics (like spatial resolution and spectral range) of the satellite. Also, the segmentation process depends not only on the road contrast but also on the road length. Thus, a low contrast but long road segment does not get removed. We have tested the algorithm on a number of images from IRS and SPOT satellites and the results are satisfactory.

关键词： Satellites Roads Image segmentation Pixel Sensor phenomena and characterization Spatial resolution Joining processes Intelligent networks computer vision pattern recognition

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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

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.

关键词： Stereo vision computer vision Noise robustness Information resources Correlation pattern recognition Image processing Layout Cameras Laboratories

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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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computer recognition OF PRINTED BANGLA SCRIPT

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 1995年第11期26卷 2107-2123页

作者： PAL, U CHAUDHURI, BB Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta 700 035 203 B. T. Road India

This paper considers optical character recognition (OCR) of Bangla, the second most popular script in the Indian subcontinent. A complete OCR system is described for documents of single Bangla font, where more than three hundred character shapes are recognized by a combination of template and feature-matching approach. Here the document image captured by a flatbed scanner is subject to tilt correction, line, word and character segmentation, simple and compound character separation, feature extraction and finally character recognition. Some character occurrence statistics have been computed to aid the recognition process. The simple character recognition is done by a feature-based tree classifier, and the compound character recognition involves a template matching approach preceded by a feature-based grouping. At present, recognition accuracy of about 96% is obtained by the system.

关键词： Optical character recognition

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Self-adaptive learning rates in backpropagation algorithm improve its function approximation performance

Self-adaptive learning rates in backpropagation algorithm im...

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International Conference on Neural Networks

作者： U. Bhattacharya S.K. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India

The backpropagation algorithm helps a multilayer perceptron to learn to map a set of inputs to a set of outputs. But often its function approximation performance is not impressive. In this paper the authors demonstrate that self-adaptation of the learning rate of the backpropagation algorithm helps in improving the approximation of a function. The modified backpropagation algorithm with self-adaptive learning rates is based on a combination of two updating rules-one for updating the connection weights and the other for updating the learning rate. The method for learning rate updating implements the gradient descent principle on the error surface. Simulation results with astrophysical data are presented.

关键词： Backpropagation algorithms Function approximation Multilayer perceptrons Approximation algorithms computer vision pattern recognition Neural networks Joining processes computer errors Computational modeling

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Motion constraint patterns

Motion constraint patterns

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1993 IEEE Workshop on Qualitative vision, WQV 1993

作者： Fermüller, Cornelia Computer Vision Laboratory Center for Automation Research University of Maryland College ParkMD20742-3275 United States Department for Pattern Recognition and Image Processing Institute for Automation Technical University Vienna Treitlstraße 3 ViennaA-1040 Austria

ISBN: (纸本)0818636920

The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements. © 1993 IEEE.

关键词： Flow measurement

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Motion constraint patterns

Motion constraint patterns

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IEEE Workshop on Qualitative vision

作者： C. Fermuller Department for Pattern Recognition and Image Processing Institute for Automation Technical University of of Vienna Vienna Austria Computer Vision Laboratory Center for Automation Research University of Maryland College Park MD USA

关键词： Motion estimation computer vision Automation Image motion analysis Fluid flow measurement Motion measurement Rotation measurement Laboratories Educational institutions Geometrical optics

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