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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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Speed estimation for scene objects using stereo visual odometry methods

Speed estimation for scene objects using stereo visual odome...

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2013 IEEE 9th International Conference on Intelligent computer Communication and processing, ICCP 2013

作者： Golban, Catalin Nedevschi, Sergiu Image Processing and Pattern Recognition Group Computer Science Department Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781479914937

This paper proposes a novel method to determine the speed of the surrounding vehicles in traffic scenarios. Relying on the video information obtained from a stereo camera mounted on a moving vehicle, we first determine the vehicle ego motion based on static scene features then we determine the relative motion between objects based on features situated on the moving objects. For robustness to false feature matches everything is plugged into a multi-RANSAC framework. The novelty of the method consist in the fact that the relative motion between the objects can be determined with the same algorithm that was previously used for ego motion estimation, the only difference consisting in the geometric constraints that are imposed to the subset of point features considered for inliers set detection and evaluation. Also, the proposed method does not rely on the fact that objects are detected previously and it does not detect the objects. © 2013 IEEE.

关键词： Vehicles

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Publishing rdf from relational database based on d2R improvement

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WSEAS Transactions on Information science and Applications 2013年第8期10卷 241-248页

作者： Chen, Ying Zhao, Xiaoming Zhang, Shiqing Department of Computer Science Taizhou University 605 Dongfang Avenue Linhai Taizou Zhejiang China Institute of Image Processing and Pattern Recognition Taizhou University 605 Dongfang Avenue Linhai Taizou Zhejiang China

As a key technology to implement Semantic Web, linked data have gradually been an academic and industrial concern. Linked data represents a practice of technologies on the web and linked structure data. The goal of linked data is to enable people to share structured data on the web as easily as they can share documents today. On the Web of data structured with linked data, users can jump from one dataset to another. Compared with the Web of document which enables one to jump from one document to another, data Web provides much more links and has semantics. Mapping relational databases to RDF is a fundamental problem for the development of linked data. This paper firstly makes an improvement with R2RML to open source software named D2R which can publish linked data, and then takes an example of the data concerning a college enrollment in Sichuan of China in 2011, demonstrating that we can publish the data in RDB into RDF as linked data through the new approach more efficient and make it browsable.

关键词： Linked data

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

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) 2013年 7877 LNCS卷 VI页

作者： Kropatsch, Walter G. Artner, Nicole M. Haxhimusa, Yll Jiang, Xiaoyi Vienna University of Technology Department of Pattern Recognition and Image Processing Favoritenstr. 9-11 186-3 1040 Vienna Austria University of Münster Department of Computer Science Einsteinstr. 62 48149 Münster Germany

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Speed estimation for scene objects using stereo visual odometry methods

Speed estimation for scene objects using stereo visual odome...

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IEEE International Conference on Intelligent computer Communication and processing

作者： Catalin Golban Sergiu Nedevschi Image Processing and Pattern Recognition Group Computer Science Department Technical University of Cluj-Napoca

ISBN: (纸本)9781479914920

关键词： Visual odometry Obstacles speed Speed detection Ego motion Motion estimation Speed estimation

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Feedback-free and hybrid distributed video coding using neural networks

Feedback-free and hybrid distributed video coding using neur...

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2012 11th International Conference on Information science, Signal processing and their Applications, ISSPA 2012

作者： Nickaein, Isaac Rahmati, Mohammad Ghidary, Saeed Shiry Zohrabi, Abtin Image Processing and Pattern Recognition Lab Department of Computer Engineering and IT Amirkabir University of Technology Tehran Iran Abtin Zohrabi Faculty of Computer Science University of New Brunswick Canada

ISBN: (纸本)9781467303828

Distributed Video Coding (DVC) is a new class of video coding techniques with the aim of coding the decentralized video sources. While the Stanford Wyner-Ziv codec is a well-known architecture in DVC literature, one of its main drawbacks is the presence of a feedback channel from the decoder to the encoder. This feedback channel makes the use of the codec impractical in some applications. Since the only application of the feedback channel is in requesting more parity bits from the encoder, it could be omitted if the encoder estimates the required parity bits and sends them at once. In this paper, a new method of bitrate estimation using a neural network trained by a new set of features is proposed. In addition, a Hybrid mode is proposed that reduces computational complexity at the decoder in a conventional Wyner-Ziv codec. © 2012 IEEE.

关键词： image coding

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Sampling step importance in hierarchical semantic segmentation of microscopic images

Sampling step importance in hierarchical semantic segmentati...

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15th International Conference on System Theory, Control and Computing, ICSTCC 2011

作者： Smochina, Cristian Manta, Vasile Kropatsch, Walter Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iasi Romania Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

ISBN: (纸本)9781457711732

The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape, orientation or texture). In this paper we improve the technique presented in [17] used to identify the epithelial nuclei (crypt) against interstitial nuclei in microscopic images taken from colon tissues. In the proposed enhanced approach, the crypt inner boundary is detected using the closing morphological pyramid instead of morphological hierarchy. The outer crypt border is determined by the epithelial nuclei, overlapped by the maximal isoline of the inner boundary. The use of sampling in building the pyramid offers computational efficiency, reduces the amount of used memory, increase the robustness and preserve the quality results. An analysis of the two approaches is performed considering the number of pixels processed to create each level. Also the relation between the levels of the hierarchical structures is established. © 2011 Faculty of Auto Control.

关键词： Computational efficiency

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Sampling step importance in hierarchical semantic segmentation of microscopic images

Sampling step importance in hierarchical semantic segmentati...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Cristian Smochina Vasile Manta Walter Kropatsch Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iasi Romania Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

关键词： Cryptography image segmentation image reconstruction image resolution Microscopy Buildings Shape

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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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