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IEEE International Conference on Systems, Man and Cybernetics

作者： Ke Wang Ping Guo School of computer Science and Technology Beijing Institute of Technology Beijing P.R. China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing P.R. China

ISBN: (纸本)9781479938414

In this paper, we investigate the efficiency and scalability of Gaussian mixture model based learning algorithm for the detection of Near-Earth objects in large scale astronomy image data. We propose an effective scheme to reduce the computational complexity of current learning algorithm, this is achieved by adopting the perceptual image hashing method. Our proposed scheme is validated on raw astronomy image data. The experiment results illustrate that both efficiency and scalability are improved significantly in astronomical scenario and other scenario.

关键词： Astronomy Scalability Big data Data mining Gaussian distribution Hamming distance Gaussian mixture model

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Posterior distribution learning (PDL): A novel supervised learning framework

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8834卷 86-94页

作者： Tu, Enmei Yang, Jie Jia, Zhenghong Kasabov, Nicola Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China School of Information Science and Engineering Xinjiang University Urumqi830046 China The Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand

In order to obtain a robust supervised model with good generalization ability, traditional supervised learning method has to be trained with sufficient well labeled and uniformly distributed samples. However, in many real applications, the cost of labeled samples is generally very expensive. How to make use of ample easily available unlabeled samples to remedy the insufficiency of labeled samples to train a supervised model is of great interest and practical significance. In this paper we propose a new supervised learning framework, Posterior Distribution Learning (PDL), which could train a robust supervised model with very a few labeled samples by including those unlabeled samples into training stage. Experimental results on both synthetic and real world data sets are presented to demonstrate the effectiveness of the proposed framework. © Springer International Publishing Switzerland 2014.

关键词： Supervised learning

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Learning distance transform for boundary detection and deformable segmentation in CT prostate images

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8679卷 93-100页

作者： Gao, Yaozong Wang, Li Shao, Yeqin Shen, Dinggang Department of Radiology and BRIC University of North Carolina at Chapel Hill United States Department of Computer Science University of North Carolina at Chapel Hill United States Institution of Image Processing & Pattern Recognition Shanghai Jiao Tong University China

Segmenting the prostate from CT images is a critical step in the radiotherapy planning for prostate cancer. The segmentation accuracy could largely affect the efficacy of radiation treatment. However, due to the touching boundaries with the bladder and the rectum, the prostate boundary is often ambiguous and hard to recognize, which leads to inconsistent manual delineations across different clinicians. In this paper, we propose a learning-based approach for boundary detection and deformable segmentation of the prostate. Our proposed method aims to learn a boundary distance transform, which maps an intensity image into a boundary distance map. To enforce the spatial consistency on the learned distance transform, we combine our approach with the auto-context model for teratively refining the estimated distance map. After the refinement, the prostate boundaries can be readily detected by finding the valley in the distance map. In addition, the estimated distance map can also be used as a new external force for guiding the deformable segmentation. Specifically, to automatically segment the prostate, we integrate the estimated boundary distance map into a level set formulation. Experimental results on 73 CT planning images show that the proposed distance transform is more effective than the traditional classification-based method for driving the deformable segmentation. Also, our method can achieve more consistentsegmentations than human raters, and more accurate results than the existing methods under comparison. © Springer International Publishing Switzerland 2014.

关键词： Computerized tomography

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Direction-of-arrival estimation in the presence of nonuniform noise using acoustic vector sensor

Direction-of-arrival estimation in the presence of nonunifor...

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2013 IEEE International Conference on Signal processing, Communications and Computing, ICSPCC 2013

作者： Jin, Yong Li, Jing Li, Song Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475001 China

ISBN: (纸本)9781479910274

A simple method is proposed to estimate the DOA using acoustic vector sensor in the presence of nonuniform noise. Algorithms mentioned in earlier works are shown to be susceptible to choice of weighted parameter. The proposed Algorithms estimate the nonuniform noise-covariance matrix first. The received data for DOA estimation is then prewhitened using the estimated noise covariance. The proposed method is shown to approach the Cramer-Rao lower bound (CRLB) without choice of weighted parameter. © 2013 IEEE.

关键词： Covariance matrix

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Estimation of distribution algorithm for the Max-Cut problem

Estimation of distribution algorithm for the Max-Cut problem

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9th IAPR-TC-15 International Workshop on Graph-Based Representations in pattern recognition, GbRPR 2013

作者： De Sousa, Samuel Haxhimusa, Yll Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group Vienna Austria

ISBN: (纸本)9783642382208

In this paper, we investigate the Max-Cut problem and propose a probabilistic heuristic to address its classic and weighted version. Our approach is based on the Estimation of Distribution Algorithm (EDA) that creates a population of individuals capable of evolving at each generation towards the global solution. We have applied the Max-Cut problem for image segmentation and defined the edges' weights as a modified function of the L2 norm between the RGB values of nodes. The main goal of this paper is to introduce a heuristic for Max-Cut and additionally to investigate how it can be applied in the segmentation context. © 2013 Springer-Verlag.

关键词： image segmentation

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Structural cues in 2D tracking: Edge lengths vs. barycentric coordinates 1

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18th Iberoamerican Congress on pattern recognition, CIARP 2013

作者： Artner, Nicole M. Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group Vienna Austria

ISBN: (数字)9783642418273

ISBN: (纸本)9783642418266

Graph models offer high representational power and useful structural cues. Unfortunately, tracking objects by matching graphs over time is in general NP-hard. Simple appearance-based trackers are able to find temporal correspondences fast and efficient, but often fail to overcome challenging situations like occlusions, distractors and noise. This paper proposes an approach, where an attributed graph is used to represent the structure of the target object and multiple, simple trackers in combination with structural cues replace the costly graph matching. Thus, the strengths of both methodologies are combined to overcome their weaknesses. Experiments based on synthetic videos are used to evaluate two possible structural cues. Results show the superiority of the cue based on barycentric coordinates and the potential of the proposed tracking approach in challenging situations. © Springer-Verlag 2013.

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On the evaluation of graph centrality for shape matching

On the evaluation of graph centrality for shape matching

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9th IAPR-TC-15 International Workshop on Graph-Based Representations in pattern recognition, GbRPR 2013

作者： De Sousa, Samuel Artner, Nicole M. Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group Vienna Austria

ISBN: (纸本)9783642382208

Graph centrality has been extensively applied in Social Network Analysis to model the interaction of actors and the information flow inside a graph. In this paper, we investigate the usage of graph centralities in the Shape Matching task. We create a graph-based representation of a shape and describe this graph by using different centrality measures. We build a Naive Bayes classifier whose input feature vector consists of the measurements obtained by the centralities and evaluate the different performances for each centrality. © 2013 Springer-Verlag.

关键词： Graphic methods

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Non-rigid object matching using hypergraph method

Non-rigid object matching using hypergraph method

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2013 7th International Conference on image and Graphics, ICIG 2013

作者： Li, Deyuan Zhou, Yue Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9780769550503

We present a non-rigid object matching algorithm based on hyper graph method. Previous work has achieved good performance for rigid object matching. However, because of deformation and wrinkle, existing algorithms cannot solve non-rigid object matching problem effectively. This paper mainly focuses on matching patterns on cloth which often deforms greatly. First, we extract stable regions from the given image. Second, we combine several features in region as bundled feature. In addition, we describe the characteristic of region based on bundled feature and the relationship among regions based on star topology. Finally, we establish the problem model and get the matching result by mathematical programming technique and refinement. Experimental results on both synthetic data and real image show the good performance of our algorithm. © 2013 IEEE.

关键词： Mathematical programming

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Hyper-graph matching with bundled feature

Hyper-graph matching with bundled feature

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4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013

作者： Li, Deyuan Zhou, Yue Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783642420566

Hyper-graph matching algorithm describes the whole structure of object by high-order topology. Previous work has presented many methods to build and solve the problem model. This paper mainly focuses on feature description and result optimization. First, we combine several features in stable region of object as bundled feature, it can describe more relationship by hyper-graph model. Second, we properly extend previous work to build and solve the hyper-graph model. Finally, we optimize the matching result by iteration and modifying constraints, it improves the accuracy effectively. Comparative experiments verify the good performance of our algorithm, especially for non-rigid object matching. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Iterative methods

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Rendering scenes with participating media based on RBFs for photon mapping using graphics hardware

Rendering scenes with participating media based on RBFs for ...

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12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry, VRCAI 2013

作者： Wang, Yuanlong Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9781450325905

The photon mapping is one of the more widely used algorithms for rendering scenes with participating media. Currently, it suffers from two main problems: one is how to improve the rendering efficiency, and another is how to reduce large memory requirement to store the photons. In this paper, we propose a hardware-accelerated algorithm that is based on radial basis function model for rendering participating media with multiple scattering. The radial basis function model is used to describe the photon distribution in participating media, and the main purpose is to provide high reconstruction quality using a relatively low number of basis function terms. Consequently, it can result in faster rendering. The radiance estimation is designed according to the radial basis function model. The implementation demonstrates that proposed method is the attractive low memory representation and it is capable of fully simulating the scattering of participating media with interactive feedback to the user. © 2013 ACM.

关键词： Radial basis function networks

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