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Titlebook: Image Analysis and Recognition; International Confer Aurélio Campilho,Mohamed Kamel Conference proceedings 2004 Springer-Verlag Berlin Heid

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书目名称Image Analysis and Recognition
副标题International Confer
编辑Aurélio Campilho,Mohamed Kamel
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Image Analysis and Recognition; International Confer Aurélio Campilho,Mohamed Kamel Conference proceedings 2004 Springer-Verlag Berlin Heid
描述ICIAR 2004, the International Conference on Image Analysis and Recognition, was the ?rst ICIAR conference, and was held in Porto, Portugal. ICIAR will be organized annually, and will alternate between Europe and North America. ICIAR 2005 will take place in Toronto, Ontario, Canada. The idea of o?ering these conferences came as a result of discussion between researchers in Portugal and Canada to encourage collaboration and exchange, mainly between these two countries, but also with the open participation of other countries, addressing recent advances in theory, methodology and applications. The response to the call for papers for ICIAR 2004 was very positive. From 316 full papers submitted, 210 were accepted (97 oral presentations, and 113 - sters). The review process was carried out by the Program Committee members and other reviewers; all are experts in various image analysis and recognition areas. Each paper was reviewed by at least two reviewing parties. The high q- lity of the papers in these proceedings is attributed ?rst to the authors, and second to the quality of the reviews provided by the experts. We would like to thank the authors for responding to our call, and we whole
出版日期Conference proceedings 2004
关键词Hidden Markov Model; Markov Model; action recognition; algorithms; classification; color analysis; compute
版次1
doihttps://doi.org/10.1007/b100437
isbn_softcover978-3-540-23223-0
isbn_ebook978-3-540-30125-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2004
The information of publication is updating

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Detecting Foreground Components in Grey Level Images for Shift Invariant and Topology Preserving Pyr positions of the partition grid used to build lower resolutions. Topology preservation is favoured by identifying on the highest resolution pyramid level all foreground components and, then, by forcing their preservation, compatibly with the resolution, through lower resolution pyramid levels.
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Image Segmentation by a Robust Clustering Algorithm Using Gaussian Estimatorod avoids producing coincident clusters, which occurs often in possibilistic clustering segmentation. Experiments on both the synthetic data and real image demonstrate the validity and power of the proposed algorithm.
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Hierarchical MCMC Samplingge estimation and segmentation, saw only limited improvements, we find reductions in computational complexity of two or more orders of magnitude, enabling the investigation of models at much greater sizes and resolutions.
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0302-9743 ICIAR will be organized annually, and will alternate between Europe and North America. ICIAR 2005 will take place in Toronto, Ontario, Canada. The idea of o?ering these conferences came as a result of discussion between researchers in Portugal and Canada to encourage collaboration and exchange, main
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Conference proceedings 2004 be organized annually, and will alternate between Europe and North America. ICIAR 2005 will take place in Toronto, Ontario, Canada. The idea of o?ering these conferences came as a result of discussion between researchers in Portugal and Canada to encourage collaboration and exchange, mainly between
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A New Approach to Unsupervised Image Segmentation Based on Wavelet-Domain Hidden Markov Tree Modelsm is converted into a self-supervised segmentation one. The simulation results on synthetic mosaics, aerial photo and synthetic aperture radar (SAR) image show that the new unsupervised image segmentation technique can obtain much better image segmentation performance than the approach based on K-means clustering.
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Spatial Discriminant Function with Minimum Error Rate for Image Segmentationn this paper. This method is derived by exploiting characteristics of a linear combination of random variables and its relation to the corresponding random vector. The suitable performance of the process is mathematically proved and the experimental results that support the effectiveness of the proposed method are provided.
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Pulling, Pushing, and Grouping for Image Segmentationithm based on the Hopfield neural model is developed for solving the optimisation process. Experimental results on various intensity, colour and texture images demonstrate the effectiveness of the new method.
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