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2007 18th British Machine Vision Conference, BMVC 2007

作者： Donner, René Mičušsík, Branislav Langs, Georg Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group Vienna University of Technology Austria GALEN Group Laboratoire de Mathématiques Appliquées Aux Systèmes Ecole Centrale de Paris France

ISBN: (纸本)1901725340

image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Random Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps. Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX-SUM algorithm. The local image information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.

关键词： image segmentation

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Object recognition using local information content

Object recognition using local information content

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International Conference on Pattern Recognition

作者： G. Fritz L. Paletta H. Bischof Joanneum Research Institute of Digital Image Processing Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

Object identification from local information has recently been investigated with respect to its potential for robust recognition, e.g., in case of partial object occlusions, scale variation, noise, and background clutter in detection tasks. This work contributes to this research by a thorough analysis of the discriminative power of local appearance patterns and by proposing to exploit local information content for object representation and recognition. In a first processing stage, we localize discriminative regions in the object views from a posterior entropy measure, and then derive object models from selected discriminative local patterns. Object recognition is then applied to test patterns with associated low entropy using an efficient voting process. The method is evaluated by various degrees of partial occlusion and Gaussian image noise, resulting in highly robust recognition even in the presence of severe occlusion effects.

关键词： Object recognition Noise robustness Entropy Background noise Object detection Information analysis Pattern analysis Pattern recognition Testing Voting

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INCREASING THE FEATURE SIZE IN HANDWRITTEN NUMERAL RECOGNITION TO IMPROVE ACCURACY

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SYSTEMS AND computerS IN JAPAN 1995年第8期26卷 35-44页

作者： WAKABAYASHI, T TSURUOKA, S KIMURA, F MIYAKE, Y Member Faculty of Engineering Mie University Tsu Japan 514 Furnitaka Kimura:received his B.E. and Ph.D. degrees in 1973 and 1978 respectively from Nagoya University where he was appointed a Research Associate the same year. He has been an Associate Professor at Mie University since 1983. He had been a Visiting Associate Professor at the University of Michigan Dearborn from 1989 to 1991. He has engaged in research on pattern recognition image processing computer graphics. He is a member of Institute of Information Processing Japan Institute of Medical Engineering Japan and Institute of Artificial Intelligence Japan. Members Yasuji Miyab:received his B.E. and Ph.D. degrees in 1957 and 1965 respectively from Nagoya University where he was appointed a Research Associate in 1965. He was appointed to a Lecturer in 1968 and an Associate Professor in 1969. He has been a Professor at Mie University since 1978. He has engaged in research on machine recognition of handwritten characters computer graphics and medical image processing. He is a member of Institute of Information Processing Japan Institute of Artificial Intelligence Japan Institute of Robotics Japan I.E.E.E. Japan and Institute of Simulation Japan.

The relationship between the recognition rate of handwritten numerals and the normality of the distribution of their features has been investigated experimentally with a large amount of data in various combinations of quantized orientations and regions. The recognition method is based on the histogram of local orientation of contours of each numeral. To obtain a more accurate orientation quantization, the effectiveness of the orientation quantization using the gray-scale gradient has also been investigated. The results show that: (1) to increase the dimensionality of features, it is better to increase the number of quantized orientations, keeping the number of regions small (e.g., 4 x 4 or 5 x 5);(2) in the same dimensionality, the better the normality of a feature distribution, the higher the recognition rate;(3) a quantization of orientations using gray scales is effective for normalizing a feature distribution;and (4) the filter processing in reduction of the number of quantization scales improves the normality and recognition rate. The recognition of handwritten numerals collected from actual posts were carried out by using the gray-scale local-orientation histogram (400 dimensions). A correct recognition rate of 99.18 percent (mean value) has been obtained.

关键词： CHARACTER RECOGNITION PATTERN RECOGNITION ZIP CODE RECOGNITION FEATURE EXTRACTION GRADIENT NORMALITY

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Evaluating the similarity degree of digital watermarks

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Journal of Information and Computational Science 2010年第6期7卷 1417-1422页

作者： Li, Li Ruan, Liuzhao Zhang, David Institute of Computer Graphics and Image Processing Hangzhou Dianzi University Hangzhou 310018 China Centre for Multimedia Signal Processing Biometrics Research Centre Hong Kong Polytechnic University Hong Kong

The popular formulas currently used to evaluate the similarity of digital watermarks have serious drawbacks: when the similarity degree is 1, the watermarks are not unique. This paper firstly analyze the drawbacks in the popular formulas. Then proposes a new formula similarity degree, which eliminates the shortcomings of the existing formulas and overcome the lack of uniqueness in theory. From the experiment results the new formula not only does well in evaluating the watermark's similarity degree, also is in good accordance with visual perception. © 2010 Binary Information Press.

关键词： Digital watermarking

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A robust matching algorithm for active appearance models 1

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2005 - Workshop on image processing for Medicine: Algorithms - Systems - Applications, BVM 2005

作者： Beichel, Reinhard Bischof, Horst Langs, Georg Sonka, Milan Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/2 A-8010 Graz Austria Pattern Recognition and Image Processing Group Vienna University of Technology Favoritenstr. 9 A-1040 Vienna Austria Department of Electrical and Computer Engineering University of Iowa Iowa City IA 52242 United States

ISBN: (数字)9783540264316

ISBN: (纸本)3540250522

This paper proposes a method for robustly matching active appearance models (AAMs) on images with gross disturbances (outliers). The method consists of two steps. First, an initial residual is calculated by comparing model and image appearance, and modes of the residual are analyzed. Second, all possible mode combinations are tested by evaluating an objective function. The objective function allows the selection of an outlier-free mode combination. Experiments demonstrate the ability of the robust matching method to successfully cope with outliers - compared to standard AAM matching, no degeneration of the model during matching occurs.

关键词： Statistics

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image multilevel thresholding using EM and de based on generalized Gaussian mixture model

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ICIC Express Letters, Part B: Applications 2012年第2期3卷 305-312页

作者： Nie, Fangyan Zhang, Pingfeng Pan, Meisen Rong, Qiusheng Institute of Graphics and Image Processing Technology College of Computer Science and Technology Hunan University of Arts and Science No. 170 Dongting Road Changde 415000 China

A novel optimal multilevel thresholding for histogram-based image segmentation is presented in this paper. It is based on the estimation of the statistical parameters of different classes under the assumption that these classes follow generalized Gaussian distribution mixture model. To fit the generalized Gaussian mixture to the histogram of image, the expectation maximization (EM) and a modified differential evolution (DE) hybrid algorithm is developed to estimate the number of generalized Gaussian mixture of such histogram and their corresponding parameterization. The performance of the proposed algorithm is evaluated vis-à-vis Otsu method and EM based on Gaussian distribution method. The experimental results show the effectiveness of the proposed method. © 2012 ICIC International.

关键词： image segmentation

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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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Automatic diagnosis of disc herniation based on DenseNet fusion model 8

Automatic diagnosis of disc herniation based on DenseNet fus...

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8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： image segmentation

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Automated multi-contrast brain pathological area extraction from 2D MR images

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Journal of Applied Research and Technology 2015年第1期13卷 58-69页

作者： Dvořák, P. Bartušek, K. Kropatsch, W.G. Smékal, Z. Institute of Scientific Instruments of the ASCR V.v.i Academy if Sciences of the Czech Republic Brno Czech Republic Dept. of Telecommunications Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Faculty of Informatics Vienna University of Technology Vienna Austria

The aim of this work is to propose the fully automated pathological area extraction from multi-parametric 2D MR images of brain. The proposed method is based on multi-resolution symmetry analysis and automatic thresholding. The proposed algorithm first detects the presence of pathology and then starts its extraction. T2 images are used for the presence detection and the multi-contrast MRI is used for the extraction, concretely T2 and FLAIR images. The extraction is based on thresholding, where Otsu's algorithm is used for the automatic determination of the threshold. Since the method is based on symmetry, it works for both axial and coronal planes. In both these planes of healthy brain, the approximate left-right symmetry exists and it is used as the prior knowledge for searching the approximate pathology location. It is assumed that this area is not located symmetrically in both hemispheres, which is met in most cases. The detection algorithm was tested on 203 T2-weighted images and reached the true positive rate of 87.52% and true negative rate of 93.14%. The extraction algorithm was tested on 357 axial and 443 coronal real images from publicly available BRATS databases containing 3D volumes brain tumor patients. The results were evaluated by Dice Coefficient (axial: 0.85 ± 0.11, coronal 0.82 ± 0.18) and by Accuracy (axial: 0.96 ± 0.05, coronal 0.94 ± 0.09). © 2015 Universidad Nacional Autónoma de México.

关键词： Brain Pathology Brain Tumor MRI Multi-contrast MRI Symmetry Analysis

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A Novel Reversible Ternary Embedding Algorithm Based on Modified Full Context Prediction Errors

A Novel Reversible Ternary Embedding Algorithm Based on Modi...

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2016 IEEE International Conference on Signal and image processing

作者： Li Li Chin-Chen Chang K.Bharanitharan Yanjun Liu Institute of Computer Graphics and Image Processing Hangzhou Dianzi University Department of Information Engineering and Computer Science Feng Chia University The Australian National University ACT

We propose a high capacity reversible ternary embedding-watermarking algorithm based on a modification of full-context-prediction-errors(MFCPE) wherein the binary bit stream is converted to the ternary stream then error histogram shifting is utilized to embed the ternary *** the existing predictor methods, we provide a full context prediction with a modification of each pixel at most by 1, which significantly reduces *** results confirm that the proposed algorithm achieves high PSNR while providing a higher embedding ***, results indicate that MFCPE outperforms the existing methods in terms of payload and the watermarked image quality.

关键词： reversible watermarking ternary embedding histogram shifting full context prediction

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