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IEEE International Conference on image processing

作者： Xin Gao Xiaoguang Chen Jia Cao Zirui Deng Chongjin Liu Jufu Feng Key Laboratory of Machine Perception MOE Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing China Institute of Image Processing and Pattern Recognition North-China University of Technology Beijing China

In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.

关键词： Pixel Feature extraction image quality Fingerprint recognition Detectors Frequency estimation Software algorithms

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Combining Feature Extraction and Hidden Markov Modelling for Automatic Speech recognition

Combining Feature Extraction and Hidden Markov Modelling for...

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International Conference on Communication Technology(ICCT’94)

作者： WU Jianxiong CHEN Peifang CHAN Chorkin DENG Li Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Department of Computer Science University of Hong Kong Dcpartment of Electrical and Computer Engineering University of WaterlooWaterlooOntarioCanada N2L 3G1.

＜正＞Feature extraction is very important for the classifier design and the overall performance of *** recognition ***,due to the lack of theoretical guidances,feature extraction and classifier design are usually treated separately in current speech recognition *** *** proposes an approach to combine linear feature extraction with continuous density hidden Markov modeling（HMM） which is currently the most successful speech pattern classifier.A maximumlikelihood based algorithm is derived to iteratively train HMM parameters as well as the parameters of the feature *** algorithm is an exteusion of the Baum-Welcli parameter re-estimation algorithm for conventional HMMs and thus has a nice property of guara, nteed convergence.

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Preface [Special issue intro.]

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IEEE Transactions on pattern Analysis and Machine Intelligence 1979年第2期PAMI-1卷 125-126页

作者： Y. T. Chien Theodosios Pavlidis Guest Editor Professor and Head of the Department of Electrical Engineering and Computer Science. Guest Editor member of the Association for Computing Machinery and Sigma Xi member of the editorial committee of the IEEE TRANSACTIONS OF PATTERN ANALYSIS AND MACHINE INTELLIGENCE Associate Editor of the Bulletin of Mathematical Biology Computer Graphics and Image Processing and Pattern Recognition.

This Special Issue is composed of the papers selected from the 1978 IEEE computer Society Workshop on pattern recognition (PR) and Artificial Intelligence (Al) held in Princeton, NJ, April 12-14, 1978. The Workshop was sponsored by the Technical Committee on Machine Intelligence and pattern Analysis. Inevitably, the contributors to the Workshop determined, to a large degree, the tone and complexion of this Special Issue. For this reason, a brief account of the Workshop Proceedings, though now history, is given. About half of the papers presented at the Workshop were also submitted for the Special Issue, a total of 37. Those of high quality were far more than the number that could be accommodated within the available number of pages. We decided to choose three topics where the interaction between the methodologies of PR and Al was most prevelant: analysis of images, analysis of speech, and certain general algorithms. All the selected papers present either theoretical, or experimental results, or both. We felt that such results clearly demonstrate the progress achieved and can be seen as very impressive if measured against the difficult problem of emulating functions associated with human intelligence by machines. It is true that they often fall short from some of the earlier ambitious goals, but the time is probably ripe to reexamine such goals in view of the accumulated experience. The following is a brief scanning of the contents of this issue, especially as related to the integration and/or interaction of PR and Al methodologies.

关键词： Artificial intelligence pattern recognition image analysis Speech analysis Conferences Labeling Machine intelligence Algorithm design and analysis image edge detection computer Society

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A New Approach of Color image Quantization

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通讯和计算机（中英文版） 2005年第3期2卷 72-76页

作者： HU Xinrong WANG Tianzhen LI Dehua Institute for Pattern Recognition and Artificial Intelligence State Education Commission Laboratory for Image Processing and Intelligence Control HuaZhong Univ. of Science＆Technology WuHan 430074 China Dept. of Computer Science ＆Technology Wuhan Institute of Science＆Technology WuHan 430074 China Institute of linage Processing WuHan Univ. of Technology Wuhan 430070China

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Impulse noise removal using linear prediction model

Impulse noise removal using linear prediction model

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4th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services, ITELSIKS 1999

作者： Prudyus, Ivan Voloshynovskiy, Sviatoslav Rytsar, Yuriy Holotyak, Taras Radio Engineering Faculty State University Lvivska Polytechnika 12 S.Bandery Str. Lviv 290646 Ukraine Coordinated Science Laboratory University of Illinois College of Engineering 1308 West Main Street Urbana IL 61801-2307 United States Department of Image Processing and Pattern Recognition Institute of Physics and Mechanics National Academy of Science of Ukraine 5 Naukova str. Lviv Ukraine

ISBN: (纸本)078035768X

Noise removal is an important problem in many applications. In this paper a new two-step scheme of the decision-based impulse noise removal method by means of contaminated pixel detection is proposed and comparison with direct order statistic filtering is given. The proposed methods satisfy both objective and subjective image quality. © 1999 IEEE.

关键词： Impulse noise

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Robust real-time tracking for visual surveillance

Eurasip Journal on Advances in Signal Processing

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Eurasip Journal on Advances in Signal processing 2007年 2007卷

作者： Thirde, David Borg, Mark Aguilera, Josep Wildenauer, Horst Ferryman, James Kampel, Martin School of Systems Engineering Computational Vision Group University of Reading Reading RG6 6AY United Kingdom Computer Science Department Pattern Recognition and Image Processing Group Vienna University of Technology Vienna 1040 Austria

This paper describes a real-time multi-camera surveillance system that can be applied to a range of application domains. This integrated system is designed to observe crowded scenes and has mechanisms to improve tracking of objects that are in close proximity. The four component modules described in this paper are (i) motion detection using a layered background model, (ii)object tracking based on local appearance, (iii) hierarchical object recognition, and (iv) fused multisensor object tracking using multiple features and geometric constraints. This integrated approach to complex scene tracking is validated against a number of representative real-world scenarios to show that robust, real-time analysis can be performed. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.

关键词： Tracking (position)

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Variational Feature Disentanglement for Few-Shot Domain Adaptation

Variational Feature Disentanglement for Few-Shot Domain Adap...

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IEEE International Conference on image processing

作者： Weiduo Wang Yun Gu Jie Yang Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Institute of Medical Robotics Shanghai Jiao Tong University China Shanghai Center for Brain Science and Brain-Inspired Technology

In this paper, we focus on the few-shot domain adaptation problem. With limited training data in target domain, a new approach is emerging to acquire the transferable knowledge from the source domain. Previous methods aligned the embedding space between domains by reducing the pair-wise distance. However, these methods are reporting the misalignment and poor generalization. To solve this problem, we propose a variational feature disentanglement framework. The embedding features are explicitly disentangled into domaininvariant and domain-specific components. The distributions of domain-invariant variance are estimated and aligned by the variational inference. For further disentanglement, the domain-invariant and domain-specific components are separated by the orthogonal constraints of subspaces. The experiments on Digits dataset and VisDA-C dataset demonstrate that the proposed method can outperform the state-of-the-art methods.

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A unifying framework for spectral analysis based dimensionality reduction

A unifying framework for spectral analysis based dimensional...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tianhao Zhang Dacheng Tao Xuelong Li Tie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Biometrics Research Centre Department of Computing Hong Kong Polytechnic University Hong Kong China School of Computer Science and Information Systems University of London UK

Past decades, numerous spectral analysis based algorithms have been proposed for dimensionality reduction, which plays an important role in machine learning and artificial intelligence. However, most of these existing algorithms are developed intuitively and pragmatically, i.e., on the base of the experience and knowledge of experts for their own purposes. Therefore, it will be more informative to provide some a systematic framework for understanding the common properties and intrinsic differences in the algorithms. In this paper, we propose such a framework, i.e., ldquopatch alignmentrdquo, which consists of two stages: part optimization and whole alignment. With the proposed framework, various algorithms including the conventional linear algorithms and the manifold learning algorithms are reformulated into a unified form, which gives us some new understandings on these algorithms.

关键词： Algorithm design and analysis Optimization Spectral analysis Weight measurement Classification algorithms Artificial neural networks Level measurement

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Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model 40th

Decoupling Respiratory and Angular Variation in Rotational X...

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40th German Conference on pattern recognition, GCPR 2018

作者： Geimer, Tobias Keall, Paul Breininger, Katharina Caillet, Vincent Dunbar, Michelle Bert, Christoph Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany ACRF Image X Institute The University of Sydney Sydney Australia

ISBN: (纸本)9783030129385

Data-driven respiratory signal extraction from rotational X-ray scans is a challenge as angular effects overlap with respiration-induced change in the scene. In this paper, we use the linearity of the X-ray transform to propose a bilinear model based on a prior 4D scan to separate angular and respiratory variation. The bilinear estimation process is supported by a B-spline interpolation using prior knowledge about the trajectory angle. Consequently, extraction of respiratory features simplifies to a linear problem. Though the need for a prior 4D CT seems steep, our proposed use-case of driving a respiratory motion model in radiation therapy usually meets this requirement. We evaluate on DRRs of 5 patient 4D CTs in a leave-one-phase-out manner and achieve a mean estimation error of $$3.01\%$$ in the gray values for unseen viewing angles. We further demonstrate suitability of the extracted weights to drive a motion model for treatments with a continuously rotating gantry. © 2019, Springer Nature Switzerland AG.

关键词： Interpolation

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DSA image registration based on multiscale Gabor filters and mutual information

DSA image registration based on multiscale Gabor filters and...

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International Conference on Information and Automation (ICIA)

作者： Zhiguo Cao Xiaoxiao Liu Bo Peng Yiu-Sang Moon Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Chinese University of Hong Kong New Territories Hong Kong China

In clinical practice, digital subtraction angiography (DSA) is a powerful technique for the visualization of blood vessels in X-ray image sequences. Different with traditional DSA image registration processes, in our proposed image registration method, the control points are selected from the vessel centerlines using multiscale Gabor filters, and mutual information (MI) is then taken as the similarity criterion to find the correspondences. Experimental results demonstrate our algorithm efficiently yields satisfying registration result for DSA images.

关键词： image registration Gabor filters Mutual information Biomedical imaging Angiography Visualization Displacement control Blood vessels image sequences Pixel

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