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Maximizing intra-individual correlations for illumination-insensitive face recognition

Maximizing intra-individual correlations for illumination-in...

引用

IEEE International Conference on Image processing

作者： Hu Han Shiguang Shan Xilin Chen Wen Gao Chinese Academy of Sciences Beijing China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

Illumination variation has been one of the most intractable problems in face recognition and many approaches have been proposed to handle illumination problem in the last decades of years. The key problem is how to get stable similarity measurements between two face images of the same individual but captured under dramatically different lighting conditions. We propose a framework to optimize the illumination normalization for a pair of gallery and probe face images by maximizing a correlation (MAC) between them. The illumination normalization in the proposed framework tends to maximize the intra-individual correlations instead of both the inter- and intra-individual correlations. Experiments on Extended YaleB and CMU-PIE face databases show the effectiveness of our proposed approach in face recognition across varying lighting conditions.

关键词： Image processing Conferences

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International Conference on Bio-Inspired computing: Theories and Applications, BICTA

作者： Zhujin Zhang Shuo Wang Xingyi Zhang Zheng Zhang School of Computer and Information Science University of South Australia Adelaide SA Australia Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

Randić et al. proposed a significant graphical representation for DNA sequences, which is very compact and avoids loss of information. In this paper, we build a fast algorithm for this graphical representation with time complexity O(n 2 ), and find another important advantage in the representation: no degeneracy. Moreover, we propose a new method to do similarity analysis of DNA sequences based on the representation. The approach adopts four elements of covariance matrix as a descriptor, and is illustrated on the first exon of beta-globin genes from 11 different species.

关键词： Rabbits Educational institutions Bovine Humans Sun DNA Mice

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Non-rigid object reconstruction based on multiview images: A survey

Non-rigid object reconstruction based on multiview images: A...

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Congress on Image and Signal processing, CISP

作者： Zhi Liu Hongjun Wang Qingli Li Shi Jun Key Laboratory of Intelligent Computing & Information Processing Ministry of Education China School of Information Science and Engineering Shandong University Jinan China School of Information Science East China Jiao Tong University Shanghai China Shandong Institute of Light Industry Jinan China

In this paper we review the major approaches to nonrigid object reconstruction based on multi-view images. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context most of the paper is devoted to present all kinds of approaches for non-rigid object reconstruction based on multiview images. A number of references are provided that describe applications of non-rigid object reconstruction based on multiview images The paper ends by addressing some important issues and open questions that can be subject of future research.

关键词： Image reconstruction Three dimensional displays Shape Computational modeling Visualization Solid modeling Geometry

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A Sample Pre-mapping Method Enhancing Boosting for Object Detection

A Sample Pre-mapping Method Enhancing Boosting for Object De...

引用

International Conference on Pattern Recognition

作者： Haoyu Ren Xiaopeng Hong Cher-Keng Heng Luhong Liang Xilin Chen Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China School of Computer Science and Technology Harbin Institute of Technology China Panasonic Singapore Laboratories Private Limited Singapore

ISBN: (纸本)9781424475421

We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection. The key step of the proposed method is a sample pre-mapping on original space by referring to the selected `reference sample' before feeding into weak classifiers. The reference sample corresponds to an approximation of the optimal separating hyper-plane in an implicit high dimensional space, so that the resulting classifier could achieve the performance similar to kernel method, while spending the computation cost of linear classifier in both training and detection. We employ two different non-linear mappings to verify the proposed method under boosting framework. Experimental results show that the proposed approach achieves performance comparable with the common used methods on public datasets in both pedestrian detection and car detection.

关键词： Classification algorithms Training Boosting Kernel Support vector machine classification Accuracy Object detection

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Boosted Sigma Set for Pedestrian Detection

Boosted Sigma Set for Pedestrian Detection

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International Conference on Pattern Recognition

作者： Xiaopeng Hong Hong Chang Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Institute of Digital Media Peking University China

ISBN: (纸本)9781424475421

This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set. Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally demanding operations based on Riemannian manifold, Sigma set preserves similar robustness and discriminative power more efficiently because the classification on Sigma sets can be directly performed in vector space. Experimental results on the INRIA and the Daimler Chrysler pedestrian datasets show the effectiveness and efficiency of the proposed method.

关键词： Classification algorithms Manifolds Detectors Feature extraction Training Covariance matrix Support vector machine classification

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A Real-Time Categorization and Clustering Method for Text Data of Laws and Regulations

A Real-Time Categorization and Clustering Method for Text Da...

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International Conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： Bianping Su Rong Wang Yiping Wang School of Science Xi''an University of Architecture and Technology Xi'an China Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China School of Science Chang'an University Xi'an China

Taking the features of data in low and high frequency texts and the frequencies which such features emerge in a single text into consideration, the paper sets up a vector space model for part of texts of field. Then the paper also establishes a classifying and clustering method with features of classification and clustering by designing and constructing the two-dimensional analytic indexes of similarities and differences between field texts. This method is designed for field texts because it is quite suitable for effective machine learning and can extend the text data and textual categories dynamically in real-time. Meantime, it solves the single-label classification and multi-label classification issues at one time, overcoming the defects of previous text classifying methods which can only expand data instead of capacity. The general text clustering methods have many defects: they are not suitable for high-dimensional data sets or large data sets; they don't have the text data and category expanding function and they can not handle the outlier data problem well. On the contrary, this new method can offer solutions to these defects. According to this method, the corresponding algorithm has been established and the effectiveness of the method has been proven by the experiment on the data sets of laws and regulations of construction industry in Shaanxi Province in China.

关键词： Support vector machine classification Clustering methods Real time systems Clustering algorithms Text categorization Feature extraction

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Predicting RNA secondary structure with Tabu Search

Predicting RNA secondary structure with Tabu Search

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IEEE International Conference on cognitive Informatics

作者： Yongguo Liu Jianrui Hao Jun Peng Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan China School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China College of Electronic Information Engineering Chongqing University of Science and Technology Chongqing China

In this paper, a novel method for predicting RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS) is proposed. In RNATS, two search models, intensification search and diversification search, are designed to exploit the local regions around the current solution and explore the unvisited space, respectively. Simulation experiments are conducted for six RNA sequences to show that the proposed method is feasible and effective.

关键词： RNA Computer simulation Space exploration Neodymium Nickel Laboratories

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A HSC-based sample selection method for support vector machine

A HSC-based sample selection method for support vector machi...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qing He Ning Li Zhong-Zhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China Chinese Academy and Sciences China

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high.

关键词： Support vector machines Training Classification algorithms Machine learning Accuracy Testing Machine learning algorithms

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Solving NP-complete problems by spiking neural P systems with budding rules

Solving NP-complete problems by spiking neural P systems wit...

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10th International Workshop on Membrane computing, WMC 2009

作者： Ishdorj, Tseren-Onolt Leporati, Alberto Pan, Linqiang Wang, Jun Computational Biomodelling Laboratory Åbo Akademi University Department of Information Technologies 20520 Turku Finland Università degli Studi di Milano - Bicocca Dipartimento di Informatica Sistemistica e Comunicazione Viale Sarca 336/14 20126 Milano Italy Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China Research Group on Natural Computing Department of CS and AI University of Sevilla Avda Reina Mercedes s/n 41012 Sevilla Spain

ISBN: (纸本)3642114660

Inspired by the growth of dendritic trees in biological neurons, we introduce spiking neural P systems with budding rules. By applying these rules in a maximally parallel way, a spiking neural P system can exponentially increase the size of its synapse graph in a polynomial number of computation steps. Such a possibility can be exploited to efficiently solve computationally difficult problems in deterministic polynomial time, as it is shown in this paper for the NP-complete decision problem sat. © 2010 Springer-Verlag.

关键词： Computational complexity

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Adaptive generic learning for face recognition from a single sample per person

Adaptive generic learning for face recognition from a single...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yu Su Shiguang Shan Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Digital Media Research Center Institute of Computing Technology CAS China Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China Institute for Digital Media Peking University China

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.

关键词： Face recognition Principal component analysis Linear discriminant analysis Computer science Content addressable storage Laboratories Deductive databases information processing Scattering Image databases

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