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Workshops Bildverarbeitung fur die Medizin: Algorithmen Systeme Anwendungen, BVM 2006 - Workshop on Image Processing for Medicine: Algorithms - Systems - Applications, BVM 2006

作者： Brox, Thomas Kim, Yoo-Jin Weickert, Joachim Feiden, Wolfgang Computer Vision and Pattern Recognition Group University of Bonn Romerstr. 164 53117 Bonn Germany Institute of Neuropathology Saarland University 66421 Homburg/Saar Germany Math. Image Analysis Group Saarland University 66041 Saarbrücken Germany

ISBN: (纸本)9783540321361

This paper presents a new approach to automated muscle fiber analysis based on segmenting myofibers with combined region and edge-based active contours. It provides reliable and fully-automated processing, thus, enabling time-saving batch processing of the entire biopsy sample stemming from routinely HE-stained cryostat sections. The method combines color, texture, and edge cues in a level set based active contour model succeeded by a refinement with morphological filters. Falsepositive segmentations as compared to former methods are minimized. A quantitative comparison between manual and automated analysis of muscle fibers images did not reveal any significant differences. We gratefully acknowledge partial funding by the DFG.

关键词： Batch data processing

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recognition of Handwritten Kannada Numerals

Recognition of Handwritten Kannada Numerals

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International Conference on Information Technology (ICIT)

作者： N. Sharma U. Pal F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Mie Japan

This paper deals with a quadratic classifier based scheme for the recognition of off-line handwritten numerals of Kannada, an important Indian script. The features used in the classifier are obtained from the directional chain code information of the contour points of the characters. The bounding box of a character is segmented into blocks and the chain code histogram is computed in each of the blocks. Here we have used 64 dimensional and 100 dimensional features for a comparative study on the recognition accuracy of our proposed system. This chain code features are fed to the quadratic classifier for recognition. We tested our scheme on 2300 data samples and obtained 97.87% and 98.45% recognition accuracy using 64 dimensional and 100 dimensional features respectively, from the proposed scheme using five-fold cross-validation technique.

关键词： Handwriting recognition Character recognition pattern recognition Histograms Natural languages computer vision Testing Automation Neural networks Training data

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3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis

3D and Infrared Face Reconstruction from RGB data using Cano...

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International Conference on pattern recognition

作者： M. Reiter R. Donner G. Langs H. Bischof Pattern Recognition and Image Processing Group Vienna University of Technology Austria Graz University of Technology Institute for Computer Graphics and Vision Austria

In this paper, we apply a multiple regression method based on canonical correlation analysis (CCA) to face data modelling. CCA is a factor analysis method which exploits the correlation between two high dimensional signals. We first use CCA to perform 3D face reconstruction and in a separate application we predict near-infrared (NIR) face texture. In both cases, the input data are color (RGB) face images. Experiments show, that due to the correlation between input and output signal, only a small number of canonical factors are needed to describe the functional relation of RGB images to the respective output (NIR images and 3D depth maps) with reasonable accuracy

关键词： Shape Face detection Lighting Image reconstruction Image analysis Image databases pattern analysis Rendering (computer graphics) Image color analysis pattern recognition

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Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features

Car Plate Detection Using Cascaded Tree-Style Learner Based ...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Qiang Wu Huaifeng Zhang Wenjing Jia Xiangjian He Jie Yang Tom Hintz Computer Vision Research Group University of Technology Broadway NSW Australia Institute of Image Process. & Pattern Recognition Shanghai Jiaotong University China

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.

关键词： Object detection Licenses Detectors computer vision pattern recognition Lighting Interference Australia Image recognition Robustness

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Active Feature Models

Active Feature Models

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International Conference on pattern recognition

作者： G. Langs P. Peloschek R. Donner M. Reiter H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Department of Radiology Medical University of Vienna Austria

ISBN: (纸本)0769525210

In this paper active feature models are proposed. They utilize local texture features and a statistical shape model for the reliable localization of landmarks in images. They are related to active appearance models, but instead of modelling the entire texture of an object they represent image texture by means of local descriptors. The approach has advantages with complex image data like anatomical structures that exhibit high texture variation with limited relevance for the recognition of the object location. Experimental results and the comparison to AAMs on different data sets indicate that active feature models can improve search speed and result accuracy, considerably

关键词： Shape Active appearance model Biomedical imaging Image texture Image edge detection Image texture analysis computer graphics pattern recognition Image processing Radiology

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Automatic Surveying of Cutaneous Hemangiomas

Automatic Surveying of Cutaneous Hemangiomas

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International Conference on pattern recognition

作者： S. Zambanini G. Langs R. Sablatnig P. Donath H. Maier Pattern Recognition and Image Processing Group University of Technology Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria Division of Special and Environmental Dermatologyy Medical University of Vienna Austria

This paper presents a method for the fully automatic surveying of cutaneous hemangiomas by means of a hemangioma segmentation and a ruler visible in the images. The algorithm computes the spatial resolution of an image. Hemangioma segmentation is accomplished by a single-layer perceptron classification by means of pixel color features. The algorithm was evaluated on a set of 120 images. It achieves satisfactory results on images with clearly visible, saturated hemangiomas

关键词： Image segmentation Neoplasms pattern recognition Spatial resolution Lesions Area measurement Pixel Image processing computer graphics Biomedical imaging

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Processing of struck-out handwriting

Processing of struck-out handwriting

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Bhattacharya, U. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)0972741216

Struck-out words are often found in handwritten manuscripts. A realistic off-line handwriting recognition system should take care of this common aspect. A simple but efficient approach to this problem is to subject each handwritten word to a two-class classifier in the initial stage. This classifier should take decision to pass the word to a word recognition engine in the next step or discard it as a struck-out/wrong word. In the present work, we studied a stroke analysis based technique, which can classify an input word as a correct or wrong (struck-out) word. We simulated the proposed approach on a database of 466 words collected from handwritten manuscripts of different languages and the results are promising. Copyright © IICAI 2005.

关键词： Artificial intelligence

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A corpus for OCR research on mathematical expressions

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International Journal on Document Analysis and recognition 2005年第4期7卷 241-259页

作者： Garain, Utpal Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta-700 035 203 B.T. Road India

This paper is concerned with research on OCR (optical character recognition) of printed mathematical expressions. Construction of a representative corpus of technical and scientific documents containing expressions is discussed. A statistical investigation of the corpus is presented, and usefulness of this analysis is demonstrated in the related research problems, namely, (i) identification and segmentation of expression zones from the rest of the document, (ii) recognition of expression symbols, (iii) interpretation of expression structures, and (iv) performance evaluation of a mathematical expression recognition system. Moreover, a groundtruthing format has been proposed to facilitate automatic evaluation of expression recognition techniques. © Springer-Verlag Berlin/Heidelberg 2005.

关键词： Database Groundtruthing Mathematical expressions OCR Performance evaluation Statistical learning

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Two-stage recognition of handwritten bangla alphanumeric characters using neural classifiers

Two-stage recognition of handwritten bangla alphanumeric cha...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Bhattacharya, U. Parui, S.K. Sridhar, M. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Electrical and Computer Engineering University of Michigan-Dearborn United States Faculty of Engineering Mie University Japan

ISBN: (纸本)0972741216

No significant research work towards recognition of handwritten Bangla characters has yet been done. Only a few works in this area are found in the literature which are based on small databases col-lected in laboratory environments. Recently, a large and representative database of Bangla character images [1] has been developed. The first part of this article is devoted to shape analysis of samples of this database vis-a-vis shapes of the respective printed characters. The next part describes a two-stage scheme for recognition of handwritten Bangla alphanumeric characters. This is a 57 class problem (shapes of 3 numerals are not distinguishable from shapes of 3 basic characters among 10 numerals and 50 basic characters). In the first stage of this recognition scheme, an input character is identified in one of 11 smaller groups of characters. In the second stage, classification is performed within the particular smaller group as determined by the first stage. In both the stages of the proposed approach, local chain code histograms of character skeleton are used as the feature vector and multilayer perceptron (MLP) as the classifier. recognition accuracies obtained by the proposed approach on the training and test sets of the present handwritten Bangla alphanumeric character database are respectively 91.45% and 84.65%. Copyright © IICAI 2005.

关键词： Database systems

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Bangla handwritten character recognition

Bangla handwritten character recognition

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Roy, K. Pal, U. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Faculty of Engineering Mie University 1577 Kurimamachiya-cho Mie 514-8504 Japan

ISBN: (纸本)0972741216

recognition of handwritten characters is difficult because of variability involved in the writing style of different individuals. This paper deals with recognition of off-line Bangla handwritten characters using quadratic classifier based on the features obtained from directional chain code histogram. Here the bounding box of a character is segmented into blocks and chain code histogram is computed in each of the blocks. This chain code features are fed to the quadratic classifier for recognition. We tested the result on 14879 characters and we obtained 90.53% accuracy with 0.81% rejection and 8.66% error, when 50 classes are considered. From 35 classes we obtained 93.90% accuracy with 0.91% rejection and 5.19% error rate. Copyright © IICAI 2005.

关键词： Graphic methods

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