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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

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On Achieving Near-Optimal "Anti-Bayesian" Order Statistics-Based Classification for Asymmetric Exponential Distributions

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15th international conference on Computer Analysis of Images and patterns (CAIP)

作者： thomas, Anu Oommen, B. John Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9783642402616

this paper considers the use of Order Statistics (OS) in the theory of pattern recognition (PR). the pioneering work on using OS for classification was presented in [1] for the Uniform distribution, where it was shown that optimal PR can be achieved in a counter-intuitive manner, diametrically opposed to the Bayesian paradigm, i.e., by comparing the testing sample to a few samples distant from the mean - which is distinct from the optimal Bayesian paradigm. In [2], we showed that the results could be extended for a few symmetric distributions within the exponential family. In this paper, we attempt to extend these results significantly by considering asymmetric distributions within the exponential family, for some of which even the closed form expressions of the cumulative distribution functions are not available. these distributions include the Rayleigh, Gamma and certain Beta distributions. As in [1] and [2], the new scheme, referred to as Classification by Moments of Order Statistics (CMOS), attains an accuracy very close to the optimal Bayes' bound, as has been shown both theoretically and by rigorous experimental testing.

关键词： Classification using Order Statistics (OS) Moments of OS

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Wavelet transforms for macro fiber composites transducers

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6th international conference on Management Science and Engineering Management, ICMSEM 2012

作者： De La Hermosa González-Carrato, Rául Ruiz Pedro García Márquez, Fausto Dimlaye, Vichaar Ingenium Research Group Universidad Castilla-La Mancha Ciudad Real Spain NDT Technology Group TWI-Technology Engineering Cambridge United Kingdom

ISBN: (纸本)9781447145998

this paper presents a novel pattern recognition approach for a Non Destructive Test (NDT) for pipes. NDT is based on Macro Fibre Composites Transducers (MFC). the signals are analysed employing Wavelet Transforms (WT). WT is an optimum methodology for the detection and diagnosis of particular behaviors that is very effective for maintenance management in NDT of pipes. WT analysis is a windowing technique that allows the use of time intervals when more precise information is required. It leads to WT to be a powerful tool that can reveal characteristics of the signals as trends, breakdown points or self-similarities. In this paper a real case study is presented, where all possible combinations between signals are considered in order to find pattern recognitions. To achieve the results, signals are broken down by mathematical software based on wavelets principles. Similarities are found with an associated energy percentage for each decomposed signal exchanging the waveforms, levels of decomposition and border distortions. © 2013 Springer-Verlag.

关键词： pattern recognition

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Code-level Information Fusion of Low-Resolution Iris Image Sequences for Personal Identification at a Distance

Code-level Information Fusion of Low-Resolution Iris Image S...

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IEEE 6th international conference on Biometrics - theory, Applications and Systems (BTAS)

作者： Liu, Jing Sun, Zhenan Tan, Tieniu Univ Sci & Technol China Dept Automat POB 2728 Beijing 100190 Peoples R China Chinese Acad Sci Ctr Res Intelligent Percept & Comp Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China

ISBN: (纸本)9781479905270

Iris images captured at a distance usually have low resolution (LR) iris texture regions, which may lose some detailed identity information. the existed approaches try to improve the similarity of these LR iris images to high resolution (HR) gallery samples through pixel-level or feature-level super-resolution. We argue that binary codes of iris feature templates are more directly relevant to iris recognition performance. this paper proposes a code-level scheme for heterogeneous matching of LR and HR iris images. the statistical relationship between a number of binary codes of LR iris images and a binary code corresponding to the latent HR iris image is established based on an adapted Markov network. Moreover, the cooccurence relationship between neighboring bits of HR iris code is also modeled through this Markov network. So that we can obtain an enhanced iris feature code from the probe set of LR iris image sequences. In addition, a weight mask can also be derived from the Markov model, which can be used to further improve iris recognition accuracy. Experimental results on Quality-Face/Iris Research Ensemble (Q-FIRE) database demonstrate that code-level information fusion performs significantly better than existed pixel-level, feature-level and score-level approaches for recognition of low resolution iris image sequences.

关键词： Image resolution Image sequences Iris Iris recognition Markov random fields Reliability

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the research of the strawberry disease identification based on image processing and pattern recognition

The research of the strawberry disease identification based ...

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6th international conference on Computer and Computing Technologies in Agriculture, CCTA 2012

作者： Ouyang, Changqi Li, Daoliang Wang, Jianlun Wang, Shuting Han, Yu College of Information and Electrical Engineering China Agricultural University Beijing 100083 China

ISBN: (纸本)9783642361234

In this paper, a synthesis segmentation algorithm is designed for the real-time online diseased strawberry images in greenhouse. First, preprocess images to eliminate the impact of uneven illumination through the "top-hat" transform, and remove noise interference by median filtering. After comprehensively applying the methods of gray morphology, logical operation, OTSU and mean shift segmentation, we can obtain the complete strawberry fruit area of the image. Normalize the extracted eigenvalues, and use eigenvectors of part of the samples for training the BP neural network and support vector machine, the remaining samples were tested in two kinds of disease strawberry recognition model. Results show that support vector machines have a higher recognition rate than the BP neural network. © 2013 IFIP international Federation for Information Processing.

关键词： Support vector machines

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Robust Matching of SIFT Keypoints via Adaptive Distance Ratio thresholding

Robust Matching of SIFT Keypoints via Adaptive Distance Rati...

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6th international conference on Machine Vision (ICMV)

作者： Mi, Liang Qiao, Yu Yang, Jie Bai, Li Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Minist Educ Syst Control & Informat Proc Key Lab Shanghai 200030 Peoples R China Univ Nottingham Sch Comp Sci Nottingham England

ISBN: (纸本)9780819499967

this paper presents a robust method to search for the correct SIFT keypoint matches with adaptive distance ratio threshold. Firstly, the reference image is analyzed by extracting some characteristics of its SIFT keypoints, such as their distance to the object boundary and the number of their neighborhood keypoints. the matching credit of each keypoint is evaluated based on its characteristics. Secondly, an adaptive distance ratio threshold for the keypoint is determined based on its matching credit to identify the correctness of its best match in the source image. the adaptive threshold loosens the matching conditions for keypoints of high matching credits and tightens the conditions for those of low matching credits. Our approach improves the scheme of SIFT keypoint matching by applying adaptive distance ratio threshold rather than global threshold that ignores different matching credits of various keypoints. the experiment results show that our algorithm outperforms the standard SIFT matching method in some complicated cases of object recognition, in which it discards more false matches as well as preserves more correct matches.

关键词： SIFT object recognition keypoint matching matching credit adaptive distance ratio threshold

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Identification of anomalies in processes of database alteration

Identification of anomalies in processes of database alterat...

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6th IEEE international conference on Software Testing, Verification and Validation (ICST)

作者： Mercaldo, Francesco Univ Sannio Dept Engn Benevento Italy

ISBN: (纸本)9780769549682

Data, especially in large item sets, hide a wealth of information on the processes that have created and modified them. Often, a data-field or a set of data-fields are not modified only through well-defined processes, but also through latent processes;without the knowledge of the second type of processes, testing cannot be considered exhaustive. As a matter of fact, changes in the data deriving from unknown processes can cause anomalies not detectable by testing, which focuses on known data variation rules. History of data variations can yield information about the nature of the changes. In my work I focus on the elicitation of an evolution profile of data: the values data may assume, the change frequencies, the temporal variation of a piece of data in relation to other data, or other constraints that are directly connected to the reference domain. the profile of evolution is then used to detect anomalies in the database state evolution. Detecting anomalies in the database state evolution could strengthen the quality of a system, since a data anomaly could be the signal of a defect in the applications populating the database.

关键词： anomaly detection outlier data mining intrusion detection pattern recognition

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Violence Detection Based on Histogram of Optical Flow Orientation

Violence Detection Based on Histogram of Optical Flow Orient...

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6th international conference on Machine Vision (ICMV)

作者： Yang, Zhijie Zhang, Tao Yang, Jie Wu, Qiang Bai, Li Yao, Lixiu Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China Univ Technol Sydney Sydney NSW 2007 Australia Univ Nottingham Nottingham NG7 2RD England

ISBN: (纸本)9780819499967

In this paper, we propose a novel approach for violence detection and localization in a public scene. Currently, violence detection is considerably under-researched compared with the common action recognition. Although existing methods can detect the presence of violence in a video, they cannot precisely locate the regions in the scene where violence is happening. this paper will tackle the challenge and propose a novel method to locate the violence location in the scene, which is important for public surveillance. the Gaussian Mixed Model is extended into the optical flow domain in order to detect candidate violence regions. In each region, a new descriptor, Histogram of Optical Flow Orientation (HOFO), is proposed to measure the spatial-temporal features. A linear SVM is trained based on the descriptor. the performance of the method is demonstrated on the publicly available data sets, BEHAVE and CAVIAR.

关键词： Violence detection video surveillance HOFO descriptor

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Kernel based weighted group sparse representation classifier

Kernel based weighted group sparse representation classifier

引用

15th international conference on Human-Computer Interaction, HCI international 2013

作者： Xu, Bingxin Guo, Ping Chen, C. L. Philip Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Faculty of Science and Technology University of Macau China

ISBN: (纸本)9783642393419

Sparse representation classification (SRC) is a new framework for classification and has been successfully applied to face recognition. However, SRC can not well classify the data when they are in the overlap feature space. In addition, SRC treats different samples equally and ignores the cooperation among samples belong to the same class. In this paper, a kernel based weighted group sparse classifier (KWGSC) is proposed. Kernel trick is not only used for mapping the original feature space into a high dimensional feature space, but also as a measure to select members of each group. the weight reflects the importance degree of training samples in different group. Substantial experiments on benchmark databases have been conducted to investigate the performance of proposed method in image classification. the experimental results demonstrate that the proposed KWGSC approach has a higher classification accuracy than that of SRC and other modified sparse representation classification. © 2013 Springer-Verlag.

关键词： Classification (of information)

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Classification based on LBP and SVM for human embryo microscope images

Classification based on LBP and SVM for human embryo microsc...

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15th international conference on Human-Computer Interaction, HCI international 2013

作者： Yin, Yabo Tian, Yun Wang, Weizhong Duan, Fuqing Wu, Zhongke Zhou, Mingquan College of Information Science and Technology Beijing Normal University Beijing China Reproductive Medicine Center Navy General Hospital PLA Beijing China

ISBN: (纸本)9783642393419

Embryo transfer is an extremely important step in the process of in- vitro fertilization and embryo transfer (IVF-ET). the identification of the embryo with the greatest potential for producing a child is a very big challenge faced by embryologists. Most current scoring systems of assessing embryo viability are based on doctors' subjective visual analysis of the embryos' morphological features. So it provides only a very rough guide to potential. A classifier as a computer-aided method which is based on pattern recognition can help to automatically and accurately select embryos. this paper presents a classifier based on the support vector machine (SVM) algorithm. Key characteristics are formulated by using the local binary pattern (LBP) algorithm, which can eliminate the inter-observer variation, thus adding objectivity to the selection process. the experiment is done with 185 embryo images, including 47 "good" and 138 "bad" embryo images. the result shows our proposed method is robust and accurate, and the accurate rate of classification can reach about 80.42%. © 2013 Springer-Verlag.

关键词： Local binary pattern

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Robust visual tracking based on online learning of joint sparse dictionary

Robust visual tracking based on online learning of joint spa...

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6th international conference on Machine Vision, ICMV 2013

作者： Li, Qiaozhe Qiao, Yu Yang, Jie Bai, Li Key Laboratory of Ministry of Education for System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China School of Computer Science University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9780819499967

In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. the joint sparse dictionary consists of positive and negative sub-dictionaries, which model foreground and background objects respectively. An online dictionary learning method is developed to update the joint sparse dictionary by selecting both positive and negative bases from bags of positive and negative image patches/templates during tracking. A linear classifier is trained with sparse coefficients of image patches in the current frame, which are calculated using the joint sparse dictionary. this classifier is then used to locate the target in the next frame. Experimental results show that our tracking method is robust against object variation, occlusion and illumination change. © 2013 SPIE.

关键词： E-learning

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