检索结果-内蒙古大学图书馆

Double-Row Cascade Labeling Algorithm for Hyper-Scale Issue

Procedia Engineering 2011年 15卷 4047-4051页

作者： Yebin Fan Hualong Zhao Department of Computer Science Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multi-Spectral Information Processing Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

An efficient algorithm is presented to label the connected components in the case that the primary memory is smaller than the image data. Our algorithm uses only the memory of two image rows to label the huge image or any image larger than the available memory. The search path compression is a applied for improving the performance further. An extensive comparison with the state-of-art algorithms is proposed, both on random and real datasets. Our algorithm shows an impressive speedup, while the auxiliary memory is not required at all comparing with all competitors.

关键词： Connected components labeling page fault hyper-scalar union-find

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Preliminary Design of a Terrain recognition System

Preliminary Design of a Terrain Recognition System

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Fan Zhang Zheng Fang Ming Liu He Huang department of Electrical Computer and Biomedical Engineering University of Rhode Island National Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

ISBN: (纸本)9781424441211

This paper aims to design a wearable terrain recognition system, which might assist the control of powered artificial prosthetic legs. A laser distance sensor and inertial measurement unit (IMU) sensors were mounted on human body. These sensors were used to identify the movement state of the user, reconstruct the geometry of the terrain in front of the user while walking, and recognize the type of terrain before the user stepped on it. Different sensor configurations were investigated and compared. The designed system was evaluated on one healthy human subject when walking on an obstacle course in the laboratory environment. The results showed that the reconstructed terrain height demonstrated clearer pattern difference among studied terrains when the laser was placed on the waist than that when the laser was mounted on the shank. The designed system with the laser on the waist accurately recognized 157 out of 160 tested terrain transitions, 300ms-2870ms before the user switched the negotiated terrains. These promising results demonstrated the potential application of the designed terrain recognition system to further improve the control of powered artificial legs.

关键词： terrains preliminary design landscapes recognition system Laser design system Consumers Waist range sensor laboratory environment human subject walking

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Data classification on multiple manifolds

Data classification on multiple manifolds

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International Conference on pattern recognition

作者： Xiao, Rui Zhao, Qijun Zhang, David Shi, Pengfei Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China Department of Computer Science Hongkong Polytechnic University Hongkong Hong Kong

ISBN: (纸本)9780769541099

Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore, better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a class. To this end, a general framework for data classification on multiple manifolds is presented. The manifolds are firstly learned for each class separately, and a stochastic optimization algorithm is then employed to get the near optimal dimensionality of each manifold from the classification viewpoint. Then, classification is performed under a newly defined minimum reconstruction error based classifier. Our method could be easily extended by involving various manifold learning methods and searching strategies. Experiments on both synthetic data and databases of facial expression images show the effectiveness of the proposed multiple manifold based approach. © 2010 IEEE.

关键词： Classification (of information)

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Evaluation of a new point clouds registration method based on group averaging features

Evaluation of a new point clouds registration method based o...

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International Conference on pattern recognition

作者： Temerinac-Ott, Maja Keuper, Margret Burkhardt, Hans Department of Pattern Recognition and Image Processing Institute of Computer Science Albert-Ludwigs-University of Freiburg Germany Albert-Ludwigs-University of Freiburg Germany

ISBN: (纸本)9780769541099

Registration of point clouds is required in the processing of large biological data sets. The tradeoff between computation time and accuracy of the registration is the main challenge in this task. We present a novel method for registering point clouds in two and three dimensional space based on Group Averaging on the Euclidean transformation group. It is applied on a set of neighboring points whose size directly controls computing time and accuracy. The method is evaluated regarding dependencies of the computing time and the registration accuracy versus the point density assuming their random distribution. Results are verified in two biological applications on 2D and 3D images. © 2010 IEEE.

关键词： pattern recognition

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Laplacian MinMax discriminant projection and its applications

Laplacian MinMax discriminant projection and its application...

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作者： Zheng, Zhonglong Chang, Xueping Yang, Jie Department of Computer Science Zhejiang Normal University Jinhua China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

A new algorithm, Laplacian MinMax Discriminant Projection (LMMDP), is proposed in this paper for supervised dimensionality reduction. LMMDP aims at learning a discriminant linear transformation. Specifically, we define the within-class scatter and the between-class scatter using similarities which are based on pairwise distances in sample space. After the transformation, the considered pairwise samples within the same class are as close as possible, while those between classes are as far as possible. The structural information of classes is contained in the within-class and the between-class Laplacian matrices. Therefore, the discriminant projection subspace can be derived by controlling the structural evolution of Laplacian matrices. The performance on several data sets demonstrates the competence of the proposed algorithm. Copyright © 2010, Australian computer Society Inc.

关键词： Discriminant analysis

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Kernel ICA applied to feature extraction for image annotation

Kernel ICA applied to feature extraction for image annotatio...

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2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

作者： Xu, Bingxin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9781424481354

In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel mapping component, is adopted to extract low-level features from the original image data. Then these feature vectors are mapped to high-level semantic words using SVM to annotate images with labels in a given semantic label set. Comparative studies have done for the performance of KICA with traditional color histogram and discrete cosine transform features. The experimental results show that the proposed method is capable of extracting the components of images as key features, and with these features to map into semantic categories, higher accuracy is achieved. © 2010 IEEE.

关键词： Support vector machines

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Feature data optimization with LVQ technique in semantic image annotation

Feature data optimization with LVQ technique in semantic ima...

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2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

作者： Jiang, Ziheng He, Jing Guo, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9781424481354

In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods with and without feature data optimization when SVM is applied to semantic image annotation. Experiment results show that the proposed method has a better performance than that without using LVQ technique. © 2010 IEEE.

关键词： Support vector machines

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Spatial direction estimation for multiple sound sources in reverberation environment

Spatial direction estimation for multiple sound sources in r...

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2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010

作者： Li, Huakang Zhao, Qunfei Saji, Akira Tanno, Keita Huang, Jie School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu 965-8580 Japan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424493173

In this paper, we propose spatial localization of multiple sound sources using a spherical robot head equipped with four microphones. We obtain arrival time differences using phase difference candidates. Based on the model of precedence effect, arrival temporal disparities obtained from the zero-crossing point are used to calculate time differences and suppress the influence of echoes in a reverberant environment. To integrate spatial cues of different microphone pairs, we use a mapping method from the correlation between different microphone pairs to a 3D map corresponding to azimuth and elevation of sound sources directions. Experiments indicate that the system provides the distribution of sound sources in azimuth-elevation localization, with the EA model even concurrently in reverberant environments. © 2010 IEEE.

关键词： Reverberation

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Spatial localization of concurrent multiple sound sources

Spatial localization of concurrent multiple sound sources

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2010 2nd International Symposium on Aware Computing, ISAC 2010

作者： Li, Huakang Saji, Akira Tanno, Keita Ma, Jun Huang, Jie Zhao, Qunfei School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu 965-8580 Japan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424483143

Based on the human auditory system for spatial localization theory, we proposed a spatial localization of multiple sound sources using a spherical robot head. Space sound vectors recorded by a microphone array with spatial configuration, are used to estimate the histograms of spatial arrival time difference vectors by solving the simultaneous equations in different frequency bands. The echo avoidance model based on precedence effect is used to reduce the interference of environment reverberations which provide the strong interference for phase vectors especially in small indoor environments. To integrate spatial cues of different microphone pairs, we propose a mapping method from the correlation between different microphone pairs to a 3D map corresponding to azimuth and elevation of sound sources directions. Experiments indicate that the system provides the distribution of sound source in azimuth-elevation localization, even concurrently in reverberant environments. ©2010 IEEE.

关键词： Reverberation

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3D deformable surfaces with locally self-adjusting parameters - A robust method to determine cell nucleus shapes

3D deformable surfaces with locally self-adjusting parameter...

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International Conference on pattern recognition

作者： Keuper, Margret Schmidt, Thorsten Padeken, Jan Heun, Patrick Palme, Klaus Burkhardt, Hans Ronneberger, Olaf Pattern Recognition and Image Processing Computer Science Department Freiburg University Germany Max-Planck Institute of Immunobiology Freiburg Germany University of Freiburg Germany Inst. of Biology II Freiburg Inst. for Advanced Studies - FRIAS University of Freiburg Germany

ISBN: (纸本)9780769541099

When using deformable models for the segmentation of biological data, the choice of the best weighting parameters for the internal and external forces is crucial. Especially when dealing with 3D fluorescence microscopic data and cells within dense tissue, object boundaries are sometimes not visible. In these cases, one weighting parameter set for the whole contour is not desirable. We are presenting a method for the dynamic adjustment of the weighting parameters, that is only depending on the underlying data and does not need any prior information. The method is especially apt to handle blurred, noisy, and deficient data, as it is often the case in biological microscopy. © 2010 IEEE.

关键词： Deformation

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