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 transfor...
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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.
<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 tre...
<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.
作者:
Y. T. ChienTheodosios PavlidisGuest 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 patternrecognition (PR) and Artificial Intelligence (Al) held in Princeton, NJ, April 12-14, 1978. The Workshop wa...
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This Special Issue is composed of the papers selected from the 1978 IEEE computer Society Workshop on patternrecognition (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.
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 wi...
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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 track...
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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...
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.
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...
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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.
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 ...
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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 ...
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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.
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