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检索条件"机构=Computer Vision and Pattern"

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A Progressive Framework for Two-Way Clustering Using Adaptive Subspace Iteration for Functionally Classifying Genes

A Progressive Framework for Two-Way Clustering Using Adaptiv...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： J.S. Shaik M. Yeasin Computer Vision Pattern and Image Analysis Laboratory Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

This paper presents an adaptive subspace based two-way clustering of microarray data. To analyze the data at various scales a "Progressive" framework is introduced. The goals are to functionally classify genes and also to find differentially expressed genes in microarray expression profiles. Empirical analysis on Colon Cancer dataset shows that ASI performs favorably in grouping genes with similar functions and finding genes that may have been involved in the formation of colon cancer. It was also observed that the proposed algorithm is robust against ordering of samples and yield results consistent with ground truth information.

关键词： Clustering algorithms Cancer Colon Data analysis Robustness Biological tissues Proteins computer vision Image analysis Performance analysis

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Functionally Classifying Genes from Microarray Data Using Linear and Non-linear Data Projection

Functionally Classifying Genes from Microarray Data Using Li...

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ACS/IEEE International Conference on computer Systems and Applications

作者： J. Shaik M. Yeasin Computer Vision Pattern and Image Analysis (CVPIA) laboratory Department of Electrical and Computer Engineering University Of Memphis Memphis TN USA

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Localized Support Vector Machines for Classification

Localized Support Vector Machines for Classification

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International Joint Conference on Neural Networks (IJCNN)

作者： Ming Dong Jing Wu Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA

Support vector machines (SVMs) have been promising methods in pattern recognition because of their solid mathematical foundation. In this paper, we propose a localized SVM classification scheme (LSVM). In which we first cluster the training data in each category, and then train a set of SVMs based on these dusters. The SVMs trained from the clusters in each category that are nearest to the given input pattern are then selected for the final classification. Our experiments on six UCI datasets show that LSVM outperforms the traditional SVM.

关键词： Support vector machines Support vector machine classification Kernel USA Councils Solids Machine learning Training data Nails Neural networks Clustering algorithms

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Kernel-based Recognition of Human Actions Using Spatiotemporal Salient Points

Kernel-based Recognition of Human Actions Using Spatiotempor...

引用

IEEE computer Society Conference on computer vision and pattern Recognition Workshops (CVPRW)

作者： A. Oikonomopoulos I. Patras M. Pantic Computing Department Imperial College London UK Computer Vision and Pattern Recognition Group University of York UK

This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. We derive a suitable distance measure between the representations, which is based on the Chamfer distance, and we optimize this measure with respect to a number of temporal and scaling parameters. In this way we achieve invariance against scaling, while at the same time, we eliminate the temporal differences between the representations. We use Relevance Vector Machines (RVM) in order to address the classification problem. We propose new kernels for use by the RVM, which are specifically tailored to the proposed spatiotemporal salient point representation. The basis of these kernels is the optimized Chamfer distance of the previous step. We present results on real image sequences from a small database depicting people performing 19 aerobic exercises.

关键词： Humans Spatiotemporal phenomena pattern recognition computer vision Image recognition Image sequences Detectors Entropy Image retrieval Educational institutions

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Fully-automated analysis of muscle fiber images with combined region and edge-based active contours

Fully-automated analysis of muscle fiber images with combine...

引用

Workshops Bildverarbeitung fur die Medizin: Algorithmen Systeme Anwendungen, BVM 2006 - Workshop on Image Processing for Medicine: Algorithms - Systems - Applications, BVM 2006

作者： Brox, Thomas Kim, Yoo-Jin Weickert, Joachim Feiden, Wolfgang Computer Vision and Pattern Recognition Group University of Bonn Romerstr. 164 53117 Bonn Germany Institute of Neuropathology Saarland University 66421 Homburg/Saar Germany Math. Image Analysis Group Saarland University 66041 Saarbrücken Germany

ISBN: (纸本)9783540321361

This paper presents a new approach to automated muscle fiber analysis based on segmenting myofibers with combined region and edge-based active contours. It provides reliable and fully-automated processing, thus, enabling time-saving batch processing of the entire biopsy sample stemming from routinely HE-stained cryostat sections. The method combines color, texture, and edge cues in a level set based active contour model succeeded by a refinement with morphological filters. Falsepositive segmentations as compared to former methods are minimized. A quantitative comparison between manual and automated analysis of muscle fibers images did not reveal any significant differences. We gratefully acknowledge partial funding by the DFG.

关键词： Batch data processing

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Performance Evaluation of Subspace-based Algorithm in Selecting Differentially Expressed Genes and Classification of Tissue Types from Microarray Data

Performance Evaluation of Subspace-based Algorithm in Select...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： J.S. Shaik M. Yeasin Computational Vision Pattern and Image Analysis Laboratory Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

This paper presents the implementation and evaluation of subspace-based clustering algorithm for robust selection of differentially expressed genes as well as the classification of tissue types from microarray data. The performance of the proposed algorithm is compared against other well known clustering algorithms and the quality of clusters is evaluated using a number of cluster validation indices. Empirical analyses on a number of synthetic and real microarray data sets suggest that the proposed subspace-based algorithm is robust in selecting differentially expressed genes and performs significantly better compared to popular clustering algorithms in selecting differentially expressed genes and classifying different tissue types.

关键词： Clustering algorithms Robustness Algorithm design and analysis Data analysis Partitioning algorithms computer vision Image analysis Performance analysis Biomedical imaging Diseases

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3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis

3D and Infrared Face Reconstruction from RGB data using Cano...

引用

International Conference on pattern Recognition

作者： M. Reiter R. Donner G. Langs H. Bischof Pattern Recognition and Image Processing Group Vienna University of Technology Austria Graz University of Technology Institute for Computer Graphics and Vision Austria

In this paper, we apply a multiple regression method based on canonical correlation analysis (CCA) to face data modelling. CCA is a factor analysis method which exploits the correlation between two high dimensional signals. We first use CCA to perform 3D face reconstruction and in a separate application we predict near-infrared (NIR) face texture. In both cases, the input data are color (RGB) face images. Experiments show, that due to the correlation between input and output signal, only a small number of canonical factors are needed to describe the functional relation of RGB images to the respective output (NIR images and 3D depth maps) with reasonable accuracy

关键词： Shape Face detection Lighting Image reconstruction Image analysis Image databases pattern analysis Rendering (computer graphics) Image color analysis pattern recognition

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Recognition of Handwritten Kannada Numerals

Recognition of Handwritten Kannada Numerals

引用

International Conference on Information Technology (ICIT)

作者： N. Sharma U. Pal F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Mie Japan

This paper deals with a quadratic classifier based scheme for the recognition of off-line handwritten numerals of Kannada, an important Indian script. The features used in the classifier are obtained from the directional chain code information of the contour points of the characters. The bounding box of a character is segmented into blocks and the chain code histogram is computed in each of the blocks. Here we have used 64 dimensional and 100 dimensional features for a comparative study on the recognition accuracy of our proposed system. This chain code features are fed to the quadratic classifier for recognition. We tested our scheme on 2300 data samples and obtained 97.87% and 98.45% recognition accuracy using 64 dimensional and 100 dimensional features respectively, from the proposed scheme using five-fold cross-validation technique.

关键词： Handwriting recognition Character recognition pattern recognition Histograms Natural languages computer vision Testing Automation Neural networks Training data

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Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning

Region-based Image Annotation using Asymmetrical Support Vec...

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Conference on computer vision and pattern Recognition (CVPR)

作者： Changbo Yang Ming Dong Jing Hua Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets.

关键词： Machine learning Support vector machines Image retrieval pattern recognition computer science Training data Supervised learning computer vision Feature extraction Image segmentation

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Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features

Car Plate Detection Using Cascaded Tree-Style Learner Based ...

引用

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Qiang Wu Huaifeng Zhang Wenjing Jia Xiangjian He Jie Yang Tom Hintz Computer Vision Research Group University of Technology Broadway NSW Australia Institute of Image Process. & Pattern Recognition Shanghai Jiaotong University China

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.

关键词： Object detection Licenses Detectors computer vision pattern recognition Lighting Interference Australia Image recognition Robustness

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