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Advances in Intelligent Systems and Computing 2013年 212卷 985-993页

作者： Chen, Ying Tang, Jin Luo, Bin School of Computer Science and Technology Anhui University Hefei 230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei 230039 China

ISBN: (纸本)9783642375019

image structure representation is a vital technique in the image recognition. A novel image representation and recognition method based on directed complex network is proposed in this paper. Firstly, the key points are extracted from an image as the nodes to construct an initial complete undirected complex network. Then, the k-nearest neighbor evolution method is designed to form a series of directed networks. At last, the feature descriptor of the image is constructed by concatenating the structure features of each directed network to finally achieve image recognition. Experimental results demonstrate that the proposed method outperforms the traditional methods in image recognition and can describe the structure of images more effectively. © Springer-Verlag Berlin Heidelberg 2013.

关键词： image recognition

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DUAL-CHANNEL NOISE REDUCTION BASED ON A MIXTURE OF CIRCULAR-SYMMETRIC COMPLEX GAUSSIANS ON UNIT HYPERSPHERE

DUAL-CHANNEL NOISE REDUCTION BASED ON A MIXTURE OF CIRCULAR-...

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IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Jalal Taghia Rainer Martin Jalil Taghia Arne Leijon Institute of Communication Acoustics Ruhr-Universit?t Bochum Bochum Germany Sound and Image Processing Lab KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781479903573

In this paper a model-based dual-channel noise reduction approach is presented which is an alternative to conventional noise reduction algorithms essentially due to its independence of the noise power spectral density estimation and of any prior knowledge about the spatial noise field characteristics. We use a mixture of circular-symmetric complex-Gaussian distributions projected on the unit hypersphere for modeling the complex discrete Fourier transform coefficients of noisy speech signals in the frequency domain. According to the derived mixture model, clustering of the noise and the target speech components is performed depending on their direction of arrival. A soft masking strategy is proposed for speech enhancement based on responsibilities assigned to the target speech class in each time-frequency bin. Our experimental results show that the proposed approach is more robust than conventional dual-channel noise reduction systems based on the single- and dual-channel noise power spectral density estimators.

关键词： Speech enhancement dual-channel noise reduction soft masking mixture of Gaussians Speech enhancement Noise reduction noise power spectral density noise reduction algorithm Noise Frequency-domain analysis noise field Unit Device Component Speech Responsibility Prior Learning Noise reduction systems

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Video Summarization by Robust Low-Rank Subspace Segmentation

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2013年 212卷 929-937页

作者： Tu, Zhengzheng Sun, Dengdi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei 230039 China

ISBN: (纸本)9783642375019

Video summarization provides condensed and succinct representations of the content of a video stream. A static storyboard summarization approach based on robust low-rank subspace segmentation is proposed in this paper. Firstly, video frames are represented as multi-dimensional vectors, and then embedded into a group of affine subspaces using low-rank representation according to the content similarity of the frames in the same subspace. Secondly, a series of subspaces are segmented based on the Normalized Cuts algorithm. The video summary is finally generated by choosing key frames from the significant subspaces and ranking these key frames in temporal order. The experimental results demonstrate that the proposed summarization algorithm can produce crucial key frames and effectively reduce the visual content redundancy in summary comparing with the conventional approaches. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Vectors

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A Local Elitism Based Membrane Evolutionary Algorithm for Point Pattern Matching

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2013年 212卷 873-882页

作者： Ding, Zhuanlian Tang, Jin Zhang, Xingyi Luo, Bin School of Computer Science and Technology Anhui University Hefei 230601 Anhui China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei 230039 Anhui China

ISBN: (纸本)9783642375019

Point pattern matching is a fundamental problem in computer vision and pattern recognition. Membrane computing is an emergent branch of bio-inspired computing, which provides a novel idea to solve computationally hard problems. In this paper, a new point pattern matching algorithm with local elitism strategy is proposed based on membrane computing models. Local elitism strategy is used to keep good correspondences of point pattern matching found during the search, so the matching rate and the convergence speed are improved. Five heuristic mutation rules are introduced to avoid the local optimum. Experiment results on both synthetic data and real world data illustrate that the proposed algorithm is of higher matching rate and better stability. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Membranes

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Design of M-channel linear-phase wavelet-like filter banks using FIR notch filters

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Journal of Computational Information Systems 2013年第15期9卷 6037-6046页

作者： Zhong, Wei Zhang, Qin Xie, Xuemei Shi, Guangming Peng, Bin Key Lab. of Media Audio and Video of Ministry of Education Communication University of China Beijing 100024 China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071 China

In this paper, we propose a new design method for M-channel wavelet-like filter banks with highly desired linear-phase (LP) property. It is based on the LP near-perfect-reconstruction (NPR) filter bank designed by the efficient modulation technique, with additional regularity condition. Since the FIR notch filter has desired zeros at notch frequencies, it is employed to achieve the regularity condition in the proposed wavelet-like filter bank. By imposing those notch filters on the obtained NPR filters, the original LP NPR bank is thus converted to the LP wavelet-like filter bank without optimization procedure, leading to a less design effort. Further the LP property as well as good system performance of the original NPR filter bank can be preserved in the resulting system even with the increase of the degree of regularity. Design example shows that with the proposed method, the good system performance can be achieved in the resulting LP wavelet-like filter bank with different degrees of regularity. © 2013 Binary Information Press.

关键词： Filter banks

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Stereo Matching based on Robust Likelihoods and MST Leveraged Smoothness Priors

Stereo Matching based on Robust Likelihoods and MST Leverage...

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2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： Tianliang Liu Liang Wang Xiuchang Zhu Jiangsu Provincial Key Lab of Image Processing and Image Communication College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781467321969

This paper proposes a global stereo correspondence using robust matching likelihoods and minimum spanning tree （MST） leveraged smooth priors in a probabilistic graphical model framework. The matching likelihoods of the stereo correspondence can be robustly constructed as data term by aggregating initial matching costs from Weber local descriptors using an unsymmetrical guided filtering in a linear model. The disparity priors are devised as smooth term to characterize the smoothness constraints leveraged by the MST structure. The presented stereo approach provides an effective and efficient way to reflect robust visual dissimilarity and resolve local and regional discontinuities. Experiments demonstrate that the proposed global stereo matching method can produce piecewise smooth, accurate and dense disparity map, while removing effectively the visual ambiguity of the stereo matching problem.

关键词： stereo vision Markov random field Weber descriptor minimun spanning tree QPBO optimization

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SAR image multiclass segmentation using a multiscale and multidirection triplet Markov fields model in nonsubsampled contourlet transform domain

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Information Fusion 2013年第4期14卷 441-449页

作者： Yan Wu Peng Zhang Ming Li Qiang Zhang Fan Wang Lu Jia Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University P.O. Box 140 Xi’an Shaanxi 710071 China National Key Lab. of Radar Signal Processing Xidian University Xi’an 710071 China

Triplet Markov fields (TMFs) model recently proposed is to deal with nonstationary image segmentation and has achieved promising results. In this paper, we propose a multiscale and multidirection TMF model for nonstationary synthetic aperture radar (SAR) image multiclass segmentation in nonsubsampled contourlet transform (NSCT) domain, named as NSCT-TMF model. NSCT-TMF model is capable of capturing the contextual information of image content in the spatial and scale spaces effectively by the construction of multiscale energy functions. And the derived multiscale and multidirection likelihoods of NSCT-TMF model can capture the dependencies of NSCT coefficients across scale and directions. In this way, the proposed model is able to achieve multiscale information fusion in terms of image configuration and features in underlying labeling process. Experimental results demonstrate that due to the effective propagation of the contextual information, NSCT-TMF model turns out to be more robust against speckle noise and improves the performance of nonstationary SAR image segmentation.

关键词： SAR image multiclass segmentation NSCT-TMF model NSCT-HMT NSCT-TMF energy function Multiscale information fusion

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Possibilistic similarity based image classification

Possibilistic similarity based image classification

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2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en sciences et Technologies de l'Information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UE Brest France

ISBN: (纸本)9789898565419

In this study, an approach for image classification based on possibilistic similarity is proposed. This approach, due to the use of possibilistic concepts, enables an important flexibility to integrate both contextual information and a priori knowledge. Possibility distributions are, first, obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability density functions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade's probability-possibility transformation. Several measures of similarity between classes were tested in order to improve the discrimination between classes. The classification is then performed based on the principle of possibilistic similarity. Synthetic and real images are used in order to evaluate the performances of the proposed model.

关键词： Probability density function

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Iterative possibility distributions refining in pixel-based images classification framework

Iterative possibility distributions refining in pixel-based ...

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2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013

作者： Alsahwa, B. Almouahed, S. Guériot, D. Solaiman, B. Image and Information Processing Dept. Telecom Bretagne Institut Mines-Télécom Brest France Lab-STICC UMR CNRS 3192 Laboratoire en Sciences et Technologies de l'Information de la Communication et de la Connaissance Institut Mines-Télécom-Télécom Bretagne-UEB Brest France

ISBN: (纸本)9789898565419

In this study, an incremental and iterative approach for possibility distributions estimation in pixel-based images classification context is proposed. This approach is based on the use of possibilistic reasoning in order to enrich a set of samples serving for the initial estimation of possibility distributions. The use of possibilistic concepts enables an important flexibility for the integration of a context-based additional semantic knowledge source formed by pixels belonging with high certainty to different semantic classes (called possibilistic seeds), into the available knowledge encoded by possibility distributions. Once possibilistic seeds are extracted, possibility distributions are incrementally updated and refined. Synthetic images composed of two thematic classes are generated in order to evaluate the performances of the proposed approach. Initial possibility distributions are, first, obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability distributions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade's probability-possibility transformation. The possibilistic seeds extraction process is conducted through the application of a possibilistic contextual rule using the confidence index used as an uncertainty measure.

关键词： Pixels

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Stereo matching using aggregated likelihood and multi-scale prior

Stereo matching using aggregated likelihood and multi-scale ...

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2012 3rd IEEE/IET International Conference on Audio, Language and image processing, ICALIP 2012

作者： Wang, Liang Liu, Tianliang Zhu, Xiuchang Jiangsu Provincial Key Lab. of Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

ISBN: (纸本)9781467301718

This paper proposes a novel global stereo matching method using aggregated likelihoods and multi-scale priors. The likelihoods of dense stereo correspondences as data term can be robustly expressed by aggregated matching costs based on Weber feature descriptors in an asymmetrical linear filtering model. The multi-scale priors on disparity surface are designed to capture scene smooth term from larger neighborhood besides 4-connected neighborhood. The presented stereo approach being relatively simple does not rely on image segmentation and any scene semantic analysis. Experiments demonstrate that the proposed stereo matching algorithm can produce the dense smooth disparity results comparable to those of excellent stereo matching techniques. © 2012 IEEE.

关键词： image segmentation

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