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检索条件"机构=Processing and Pattern Recognition Laboratory"

共 778 条记录，以下是301-310 订阅

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Minimizing the topological structure of line images 7th

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7th Joint IAPR International Workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Kropatsch, Walter G. Burge, Mark Vienna University of Technology Institute of Automation 183/2 Pattern Recognition and Image Processing Group Treitlstr.3 WienA-1040 Austria Johannes Kepler University Institute of Systems Science Computer Vision Laboratory LinzA-4040 Austria

ISBN: (纸本)3540648585

We present a new algorithm based on Dual Graph Contraction (DGC) to transform the Run Graph into its Minimum Line Property Preserving (MLPP) form which, when implemented in parallel, requires O(log(longestcurve)) steps. A MLPP graph of a line image compliments the structural information in geometric graph representations like the run graph. Using such a graph and its dual, line image analysis systems can efficiently detect topological features like loops and holes and make use of relations like containment. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Topology

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Needle segmentation in 3D ultrasound images based on phase-grouping

Needle segmentation in 3D ultrasound images based on phase-g...

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MIPPR 2009 - Medical Imaging, Parallel processing of Images, and Optimization Techniques: 6th International Symposium on Multispectral Image processing and pattern recognition

作者： Zhao, Siying Qiu, Wu Ming, Yuchi Ding, Mingyue Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Hubei 10086 China School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China School of Life Science and Technology Huazhong University of Science and Technology Hubei 10086 China

ISBN: (纸本)9780819478085

Three Dimensional (3D) ultrasound images can provide spatial information to help doctors locate the needle position precisely in ultrasound-guided surgery. In this paper, we present a method called "3D Phase-grouping" to segment the needle inside the patient. The method is an extension from 2D phase-grouping to the 3D case via a new mathematic model-the outer products of adjacent orientation vectors. Firstly, the voxels with the same outer products of the gradient orientation vectors of the adjacent points are divided into Line Support Regions (LSR). Then, the needle is extracted with the 3D Least-Squares line fitting method in the maximal LSR. Synthetic and 3D ultrasound phantom data were tested. The segmentation results were evaluated by the angular deviation, position deviation and computational time. Compared with the 3D Hough transform, 3D Phase-Grouping is more accurate and faster without using pre-information. The computational complexity and robustness of the algorithm remain to be our future research topics. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Needles

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Incremental learning in nonstationary environments with controlled forgetting

Incremental learning in nonstationary environments with cont...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ryan Elwell Robi Polikar Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering Department Rowan University Glassboro NJ USA

We have recently introduced an incremental learning algorithm, called Learn ++ .NSE, designed for Non-Stationary Environments (concept drift), where the underlying data distribution changes over time. With each dataset drawn from a new environment, Learn ++ .NSE generates a new classifier to form an ensemble of classifiers. The ensemble members are combined through a dynamically weighted majority voting, where voting weights are determined based on classifiers' age-adjusted accuracy on current and past environments. Unlike other ensemble-based concept drift algorithms, Learn ++ .NSE does not discard prior classifiers, allowing potentially cyclical environments to be learned more effectively. While Learn ++ .NSE has been shown to work well on a variety of concept drift problems, a potential shortcoming of this approach is the cumulative nature of the ensemble size. In this contribution, we expand our analysis of the algorithm to include various ensemble pruning methods to introduce controlled forgetting. Error or age-based pruning methods have been integrated into the algorithm to prevent potential out-voting from irrelevant classifiers or simply to save memory over an extended period of time. Here, we analyze the tradeoff between these precautions and the desire to handle recurring contexts (cyclical data). Comparisons are made using several scenarios that introduce various types of drift.

关键词： Neutron spin echo Algorithm design and analysis Voting

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Brain MRI Segmentation with Patch-based CNN Approach

Brain MRI Segmentation with Patch-based CNN Approach

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第35届中国控制会议

作者： Zhipeng CUI Jie YANG Yu QIAO Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

Brain Magnetic Resonance Image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in modern medical *** methods were developed for automatic segmenting of brain MRI but failed to achieve desired *** this paper,we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network(CNN).Each brain MRI acquired from a small portion of public dataset is firstly divided into *** of these patches are then used for training CNN,which is used for automatic segmentation of brain *** results showed that our approach achieved better segmentation accuracy compared with other deep learning methods.

关键词： CNN Deep Learning Brain MRI Segmentation Patch-based

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Sub-manifold distance based object recognition in clutter

Sub-manifold distance based object recognition in clutter

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International Conference on Natural Computation

作者： Zhou, Hua Cai, Chao Ding, Mingyue National Lab for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Imaging Processing and Intelligence Control Key Laboratory of Education Ministry School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781424459612

Object recognition is challenging problem in computer vision due to appearance variation and presence of visual clutter and occlusions. Recently manifolds are thought to be fundamental for visual perception, and manifold learning algorithms are developed for discovering intrinsical features. Selective visual attention mechanism provides tools to reduce computation cost and avoid the influence of clutter background. In this paper, we described a new object recognition method, the submanifold distance (SMD) algorithm, which is induced by the visual attention mechanism to provide complex object recognition. Experiments with airport remote sensing images illustrated that our proposed algorithm can recognize complex objects accurately, robustly and quickly. ©2010 IEEE.

关键词： Object recognition

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Mining top-K Frequent and Flexible pattern from sequences

Mining top-K Frequent and Flexible Pattern from sequences

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2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Junyan, Zhang Fan, Min Information Science and Technology College Chengdu University China University Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781479916603

pattern Mining is a popular issue in biological sequence analysis. With the introduction of wildcard gaps, more interesting patterns can be mined. In this paper, we propose a new definition related to pattern frequency, under which the Apriori property holds. We define a pattern mining problem called Ming top-K Frequent patterns (MFP), where gaps are mined instead of specified. Compared with existing problems, MFP does not require any domain knowledge of the user. However, theoretical analysis and experimental results show that MFP favors inflexible patterns. We then define another problem where the flexibility threshold of each gap is specified by the user. The problem is called Mining top-K Frequent and Flexible patterns (MF2P). We develop algorithm with polynomial complexities for both problems. patterns can grow from both sides. Some interesting biological patterns mined by our algorithms are discussed. © 2013 IEEE.

关键词： Bioinformatics

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An Efficient Face recognition System Using a New Optimized Localization Method

An Efficient Face Recognition System Using a New Optimized L...

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International Conference on pattern recognition

作者： H.R. Kanan K. Faez M. Ezoji Image Processing and Pattern Recognition Laboratory Electrical Engineering Department Amirkabir University of Technology슠 Tehran Iran

In this paper a system is developed for face recognition processes. Preprocessing and face localization is necessary to obtain a high classification rate in face recognition tasks. In this study after preprocessing of face images, for omitting the redundant information such as background and hair, the oval shape of face is approximated by an ellipse using shape information. Then the parameters (orientation and center coordinates) of this ellipse are optimized using genetic algorithm (GA). High order pseudo Zernike moment invariant (PZMI) which has useful properties is utilized to produce feature vectors. Also radial basis function neural network (RBFNN) with HLA learning rule has been used as a classifier. Simulation results on ORL database indicate that the error rate of proposed system which uses genetic algorithm for optimizing the face localization step is lower than an older system which described in (H. Haddadnia et al., 2003)

关键词： Face recognition Optimization methods Face detection Shape Genetic algorithms pattern recognition Image processing Neural networks Feature extraction Hair

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An image-sequence compressing algorithm based on homography transformation for unmanned aerial vehicle

An image-sequence compressing algorithm based on homography ...

引用

International Symposium on Intelligence Information processing and Trusted Computing

作者： Gong, Junbin Zheng, Chenlin Tian, Jinwen Wu, Dingxue Institute for Pattern Recognition and Artificial Intelligence National Key Laboratory of Science and Technology on Multi-spectral Information Processing Huazhong University of Science and Technology Wuhan 430074 China College of Computer Science and Technology Huanggang Normal University Huanggang 438000 China

ISBN: (纸本)9780769541969

Focus on the image compressing problem of unmanned aerial vehicle with high compression ratio, fixed compressing ratio and low computational complexity requirement, a low-complexity image-sequence compressing algorithm based on homography transformation was proposed. The image sequences were dynamically divided into framegroups according the data from airborne inertial navigation systems, and the intermediate frames in the same frame-group was bi-directionally predicted by the first-frame and the end-frame with homography transformation. The homography matrix was got approximately by the airborne inertial navigation systems firstly and then was accurately computed by fast multiple sub-areas template matching. At the end the first frame and the residual images of the intermediate frames of the same frame-group was merged into a big image and coded by JPEG2000 to generate fixed-size code streams. The experiment results show that the proposed algorithm was with high compression performance, low computational complexity and excellent capacity for code-size control and will has good prospect in engineer. © 2010 IEEE.

关键词： Image compression

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Density-Aware Part-Based Object Detection with Positive Examples

Density-Aware Part-Based Object Detection with Positive Exam...

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International Conference on pattern recognition

作者： Ekaterina Riabchenko Joni-Kristian Kämäräinen Ke Chen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Deparment of Signal Processing Tampere University of Technology

ISBN: (纸本)9781479952106

Part-based models have become the mainstream approach for visual object classification and detection. The key tools adopted by the most methods are interest point detectors and descriptors, shared codes for object parts (visual codebook) and discriminative learning using positive and negative class examples. Distinction of our method from the existing part-based methods for object detection is the use of sparse class-specific landmarks with semantic meaning. The landmarks are the additional distinguished information of object location in the proposed framework. Additionally, localising semantic and discriminative landmarks (object parts) is significant in other related applications of computer vision, such as facial expression recognition and pose/orientation estimation of objects. Therefore, we propose a model which deviates from the mainstream by the fact that the object parts' appearance and spatial variation, constellation, are explicitly modelled in a generative probabilistic manner. With using only positive examples our method can achieve object detection accuracy comparable to state-of-the-art discriminative method.

关键词： Training Probabilistic logic Estimation Face Feature extraction Object detection Detectors

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Estimating the connection probability for 2-D mesh networks by Monte Carlo methods

Estimating the connection probability for 2-D mesh networks ...

引用

International Conference on Computer and Management

作者： Liang, Meilian Liang, Binmei Shao, Zehui Xu, Xiaodong School of Mathematics and Information Science Guangxi University Nanning 530004 China Key Laboratory of Pattern Recognition and Intelligent Information Processing School of Information Science and Technology Chengdu University Chengdu 610106 China Guangxi Academy of Sciences Nanning 530007 China

ISBN: (纸本)9781424492831

Fault tolerance is a central issue in the design and implementation of interconnection networks for large parallel systems. Connection probability of a network is a good network fault tolerance measure. For a mesh of given size and node failure probability, the gap between the known upper and lower bounds on the connection probability is often very large. In this paper we design algorithms to estimate the connection probability for 2-D meshes by Monte Carlo methods. The experiment is carefully designed and performed, and the simulation results give good estimates of the connection probability for 2-D meshes and can be used to evaluate the known lower and upper bounds on connection probability for 2-D meshes. ©2011 IEEE.

关键词： Monte Carlo methods

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