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IEEE International Conference on image processing

作者： H.-Y. Chen N. Brandle H. Bischof H. Lapp Pattern Recognition & Image Processing Group University of Technology Vienna Vienna Austria Novartis Research Institute Vienna Genetics Vienna Austria

Addresses the problem of reliably fitting parametric and semi-parametric models to high density spot array images obtained in gene expression experiments. The goal is to measure the amount of genetic material at specific spot locations. Many spots can be modelled accurately by a Gaussian shape. In order to deal with highly overlapping spots the authors use robust M-estimators. When the parametric method fails, they use a novel, robust semi-parametric method which can handle spots of different shapes accurately. They present the results for real data and compare the complexity of the two methods.

关键词： Robustness Genetics Shape image analysis Gene expression pattern recognition image processing Lapping Safety Noise shaping

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Fuzzy c-means in an MDL-framework

Fuzzy c-means in an MDL-framework

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International Conference on pattern recognition

作者： A. Selb H. Bischof A. Leonardis Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)0769507506

In this paper we present a minimum description length (MDL) framework for fuzzy clustering algorithms. This framework enables us to find an optimal number of cluster centers. We applied our approach to the fuzzy c-means algorithm for which we designed a computationally efficient procedure. We report the results of our approach on a 2D clustering problem and on RGB color image segmentation.

关键词： Clustering algorithms Radial basis function networks Encoding Color image segmentation image recognition image processing Algorithm design and analysis Unsupervised learning pattern recognition

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Multiple eigenspaces by MDL

Multiple eigenspaces by MDL

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International Conference on pattern recognition

作者： A. Leonardis H. Bischof Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria

ISBN: (纸本)0769507506

We propose an approach to constructing multiple eigenspaces from a set of training images based on the minimum description length (MDL) principle. The main idea is to systematically build a redundant set of eigenspaces, which are treated as hypotheses that are then subject to a selection procedure. The selection procedure, based on the MDL principle, selects the final resulting set of eigenspaces as an optimal representation of the training set. We have tested the proposed method on a number of standard image sets, and the significance of the approach with respect to the recognition rate has been clearly demonstrated.

关键词： Testing image recognition Principal component analysis Information science image analysis image reconstruction pattern recognition image processing Robustness Robust control

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Gravel image Segmentation in Noisy Background Based on Partial Entropy Method

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中国地质大学学报(英文版) 2000年第1期11卷 92-94页

作者： Wang Guangjun Tian Jinwen Chen Zhenyu Liu Jian Wu Guoping State Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology WuHan 430074 Faculty of Information Engineering China University of Geosciences Wuhan 430074

Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult o segment gravel objects. In this paper, we develop a partial entropy method and succeed to realize gravel objects segmentation. We give entropy principles and fur calculation methods. Moreover, we use minimum entropy error automaticly to select a threshold to segment image. We introduce the filter method using mathematical morphology. The segment experiments are performed by using different window dimensions for a group of gravel image and demonstrates that this method has high segmentation rate and low noise sensitivity.

关键词： partial entropy segmentation mathematical morphology gravel threshold filter

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Content based image retrieval using interest points and texture features

Content based image retrieval using interest points and text...

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15th International Conference on pattern recognition, ICPR 2000

作者： Wolf, Christian Jolion, Jean-Michel Kropatsch, Walter Bischof, Horst Vienna University of Technology Pattern Recognition and Image Processing Group Favoritenstraße 9/1832 Wien1040 Austria INSA de Lyon Laboratoire Reconnaissance de Formes et Vision 3 Avenue Albert Einstein Villeurbanne Cedex69626 France

ISBN: (纸本)0769507506

Content based image retrieval is the task of searching images from a database, which are visually similar to a given example image. We present methods for content based image retrieval based on texture similarity using interest points and Gabor features. Interest point detectors are used in computer vision to detect image points with special properties, which can be geometric (corners) or non-geometric (contrast etc.). Gabor functions and Gabor filters are regarded as excellent tools for feature extraction and texture segmentation. The article combines these methods and generates a textural description of images. Special emphasis is devoted to distance measures on texture descriptions. Experimental results of a query system are given. © 2000 IEEE.

关键词： Gabor filters

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Nearness in digital images and proximity spaces

Nearness in digital images and proximity spaces

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9th International Conference on Discrete Geometry for Computer imagery, DGCI 2000

作者： Pták, Pavel Kropatsch, Walter G. CMP – Czech Technical University Center for Machine Perception Karlovo nám. 13 Praha 2121 35 Czech Republic Institute of Computer Aided Automation 183/2 Vienna University of Technology Pattern Recognition and Image Processing Group Favoritenstr.9 WienA-1040 Austria

ISBN: (纸本)3540413960

The concept of "nearness", which has been dealt with as soon as one started studying digital images, finds one of its rigorous forms in the notion of proximity space. It is this notion, together with "nearness preserving mappings", that we investigate in this paper. We first review basic examples as they naturally occur in digital topologies, making also brief comparison studies with other concepts in digital geometry. After this we characterize proximally continuous mappings in metric spaces. Finally, we show by example that the "proximite complexity" of a finite covering in a digital picture may be too high to be adequately depicted in a finite topological space. This combinatorial result may indicate another conceptual advantage of proximities over topologies. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Topology

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Background mosaic from egomotion

Background mosaic from egomotion

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International Conference on pattern recognition

作者： R. Megret C. Saraceno W. Kropatsch École Normale Supérieure de Lyon Lyon France Starlab NV Brussels Belgium Pattern Recognition and Image Processing group University of Technology Vienna Vienna Austria

ISBN: (纸本)0769507506

A framework is presented that produces the mosaic corresponding to the background object of an image sequence. It is based on the dominant motion assumption, which states that the background has a parametric motion and occupies the main part of the images. The foreground objects are localised by their different motion. This localisation is computed together with the background motion in an iterative method. The regions corresponding to the background are then pasted onto the mosaic using classic methods adapted to object elimination or a new mosaicking method based on a striping that takes the foreground objects localisation into account.

关键词： image segmentation Pixel Parametric statistics pattern recognition image processing image sequences Iterative methods Video sequences Buildings Motion segmentation

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Content based image retrieval using interest points and texture features

Content based image retrieval using interest points and text...

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International Conference on pattern recognition

作者： C. Wolf J.-M. Jolion W. Kropatsch H. Bischof Pattern Recognition and Image Processing Group University of Technology Vienna Wien Austria Laboratoire Reconnaissance de Formes et Vision INSA Lyon Villeurbanne France

ISBN: (纸本)0769507506

关键词： image retrieval Content based retrieval Information retrieval image databases Spatial databases Visual databases Detectors Computer vision Gabor filters Feature extraction

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On estimating the position of fragments on rotational symmetric pottery 2

On estimating the position of fragments on rotational symmet...

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2nd International Conference on 3-D Digital Imaging and Modeling, 3DIM 1999

作者： Sablatnig, Robert Menard, Christian Vienna University of Technology Institute of Computer Aided Automation Pattern Recognition and Image Processing Group Treitlstr. 3 183-2 WienA-1040 Austria

ISBN: (纸本)0769500625

The activation of ancient vessel-fragments is a time consuming but important task for archaeologists. The basis for classification and reconstruction is the profile which is the cross-section of the fragment in the direction of the rotational axis of symmetry. Hence the position of a fragment (orientation) on a vessel is important. In this work the estimation of the axis of rotation out of range data by using a Hough inspired method is proposed. In order to avoid outliers a robust method for estimation of the axis is used. Classification and reconstruction are performed in a bottom-up manner using a description language, which holds all features of the fragment as primitives and all properties among features as relations. Classification of newly found fragments of unknown type is performed by comparing the description of the new fragment with the description of already classified fragments by completing graph similarity. The sub-graph with the highest similarity is then used to reconstruct the complete vessel out of the fragment. © 1999 IEEE.

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Texture Segmentation Based on image Model and Neural Network

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Journal of Earth Science 1999年第4期18卷 333-335页

作者： Chen Zhenyu Sheng Wen Wang Guangjun Li Dehua(State Education Commission Laboratory for image processing and Intelligence Control, Institute for pattern recognition and Artificial Intelligence, Huazhong university of Science and technology, Wu han 430074) State Education Commission Laboratory for Image Processing and Intelligence Control Institute for Pattern Recognition and Artificial IntelligenceHuazhong University of Science and TechnologyWuhan 430074

This paper presents a texture segmentation approach which is based on the Markov random field model (MRF) and feed forward neural *** texture is modeled by the second order Gauss MRF model, and the least square error estimation is employed for the solution of model parameters. To perform texture segmentation, we introduced an improved BP algorithm to get faster learning speed. Experiment shows that better segmentation results can be obtained than the traditional Euclidean distance method.

关键词： texture analysis Markov random field neural network

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