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CENTROID-BASED TEXTURE CLASSIFICATION USING THE GENERALIZED GAMMA DISTRIBUTION

CENTROID-BASED TEXTURE CLASSIFICATION USING THE GENERALIZED ...

引用

European Signal Processing Conference

作者： Aurelien Schutz Lionel Bombrun Yannick Berthoumieu Mohamed Najim Université de Bordeaux ENSEIRB-Matmeca Laboratoire IMS Groupe Signal et Image

ISBN: (纸本)9781479936878

This paper introduces a centroid-based (CB) supervised classification algorithm of textured images. In the context of scale/orientation decomposition, we demonstrate the possibility to develop centroid approach based on a stochastic modeling. The aim of this paper is twofold. Firstly, we introduce the generalized Gamma distribution (GΓD) for the modeling of wavelet coefficients. A comparative goodness-of-fit study with various univariate models reveals the potential of the proposed model. Secondly, we propose an algorithm to estimate the centroid from the collection of GΓD parameters. To speed-up the convergence of the steepest descent, we propose to include the Fisher information matrix in the optimization step. Experiments from various conventional texture databases are conducted and demonstrate the interest of the proposed classification algorithm.

关键词： textured images Jeffrey divergence generalized Gamma distribution centroid supervised classification Centroid Gamma Distribution sorting algorithm textures

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CLASSIFICATION OF EX-VIVO BREAST CANCER POSITIVE MARGINS MEASURED BY HYPERSPECTRAL IMAGING

CLASSIFICATION OF EX-VIVO BREAST CANCER POSITIVE MARGINS MEA...

引用

IEEE International Conference on Image Processing

作者： R. Pourreza-Shahri F. Saki N. Kehtarnavaz P. Leboulluec H. Liu University of Texas at Dallas University of Texas at Arlington

ISBN: (纸本)9781479923427

This paper presents our recent development of a classification algorithm for identification of breast cancer margins measured by hyperspectral imaging for the purpose of lowering the number of missed positive margins in breast cancer lumpectomy. After extracting Fourier coefficient selection features and reducing the dimensionality of hyperspectral image data via the Minimum Redundancy Maximum Relevance method, an SVM classifier involving a radial basis kernel function is deployed to separate cancerous tissues from normal tissues. By examining exvivo breast cancer hyperspectral images tagged by a pathologist, the developed classification approach is shown to achieve a sensitivity of about 98% and a specificity of about 99%.

关键词： Hyperspectral imaging of breast cancer breast cancer positive margin classification Fourier coefficient selection features support vector machine Support Vector Network Breast Cancer margins sorting algorithm hyperspectral imagery EX-VIVO image data Segmental Mastectomy Imaging Radial basis function Taxonomy

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Smart Meter Systems Measurements for the Verification of the Detection & Classification algorithms

Smart Meter Systems Measurements for the Verification of the...

引用

Annual Conference of the IEEE Industrial Electronics Society

作者： Thomas Bier Dirk Benyoucef Djaffar Ould Abdeslam Jean Merckle Philipp Klein University Furtwangen University of Haute Alsace

ISBN: (纸本)9781479902255

In this paper the Non-Intrusive Appliance Load Monitoring (NALM) will be described as a technique to improve the current smart meter systems. The development and verification of a measurement system is described. With that system it is possible to simulate and measure the energy consumption of appliances in residential buildings. These measurements are used to develop and verify the disaggregation algorithms for the event detection and the classification. A first event detection algorithm, based on the calculation of the difference values of the power signals is presented. A comparison with another algorithm is shown. The usefulness of that algorithm for the event detection is discussed at the end of the paper.

关键词： Event detection Smart meters Verification measurement system signal power sorting algorithm energy consumption algorithms

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Automatic Error Detection from Pointing Device Input Data 13

Automatic Error Detection from Pointing Device Input Data

引用

Annual Meeting of The American Society for Information Science and Technology

作者： Afroza Sultana Karyn Moffatt McGill University

ISBN: (纸本)9780877155454

Although interaction techniques have been extensively studied under controlled laboratory conditions, little is known about their capabilities during unconstrained free tasks. Understanding real world use is particularly important for older adults, as some may find computer input more challenging due to age-related declines in motor skill. As a step towards addressing this gap, we studied the feasibility of using machine-learning techniques. In this study, we emphasized on identifying errors from submovement behavior, using pen-based data from younger and older adults. We tested four machine-learning algorithms: Decision Trees, Neural Networks, Naive Bayesian Networks, and Rule Induction. Each yielded an accuracy rate around 90%. Moreover, the experiments have identified some useful metrics to classify errors among older adult users.

关键词： Error modeling Classification algorithms Performance measurement Pen based pointing devices Sub-movement analysis sorting algorithm Performance metrics error modeling accuracy rate pointing devices aged person Rule induction Error detection Motor Skills Bayesian networks

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Classification of Wheelchair Commands using Brain Computer Interface: Comparison between Able-Bodied Persons and Patients with Tetraplegia

Classification of Wheelchair Commands using Brain Computer I...

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

作者： Rifai Chai Sai Ho Ling Gregory P. Hunter Yvonne Tran Hung T. Nguyen Key Centre for Health Technologies Faculty of Engineering and Information Technology University

ISBN: (纸本)9781457702150

This paper presents a three-class mental task classification for an electroencephalography based brain computer interface. Experiments were conducted with patients with tetraplegia and able bodied controls. In addition, comparisons with different time-windows of data were examined to find the time window with the highest classification accuracy. The three mental tasks used were letter composing, arithmetic and imagery of a Rubik's cube rolling forward;these tasks were associated with three wheelchair commands: left, right and forward, respectively. An eyes closed task was also recorded for the algorithms testing and used as an additional on/off command. The features extraction method was based on the spectrum from a Hilbert-Huang transform and the classification algorithm was based on an artificial neural network with a fuzzy particle swarm optimization with cross-mutated operation. The results show a strong eyes closed detection for both groups with average accuracy at above 90%. The overall result for the combined groups shows an improved average accuracy of 70.6% at 1s, 74.8% at 2s, 77.8% at 3s, 79.6% at 4s and 81.4% at 5s. The accuracy for individual groups were lower for patients with tetraplegia compared to the able-bodied group, however, does improve with increased duration of the time-window.

关键词： Quadriplegia Particle swarm optimization Paralysis Brain-Computer Interfaces commands Time windows sorting algorithm Wheelchair Patients Religious Missions

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SPATIAL-SPECTRAL CLASSIFICATION BASED ON GROUP SPARSE CODING FOR HYPERSPECTRAL IMAGE

SPATIAL-SPECTRAL CLASSIFICATION BASED ON GROUP SPARSE CODING...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Xiangrong Zhang Peng Weng Jie Feng Erlei Zhang Biao Hou Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University

ISBN: (纸本)9781479911127

In this paper, a novel hyperspectral image classification method is proposed, based on group sparse coding. The method is based on this acknowledgement that larger spatial variation exists in high spatial resolution hyperspectral image, which degrades the separability of hyperspectral image. In order to obtain a smooth representation, each pixel and its spatial neighbors are coded together by group sparse coding. Although nothing about class information is included, the neighbor pixels in a small spatial window are inclined to belong to the same class. Thus, that will reduce the within-class scatter and be favorable to the classification task. Then, the obtained sparse representation vectors are used for hyperspectral image classification with SVM. Experimental results show that our method exceeds the classical classification algorithms in accuracy and regional consistency.

关键词： Hyperspectral image classification Spatial variance Group sparse Spatial-spectral hyperspectral imagery Image classification Taxonomy Sparse coding sorting algorithm images Classification Methods Spatial variations pixel

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VOICE ACTIVITY DETECTION USING A SLIDING-WINDOW, MAXIMUM MARGIN CLUSTERING APPROACH

VOICE ACTIVITY DETECTION USING A SLIDING-WINDOW, MAXIMUM MAR...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Phillip De Leon Salvador Sanchez New Mexico State University Klipsch School of Electrical and Computer Engineering Las Cruces New Mexico U.S.A.

ISBN: (纸本)9781479903573

Recently, an unsupervised, data clustering algorithm based on maximum margin, i.e. support vector machine (SVM) was reported. The maximum margin clustering (MMC) algorithm was later applied to the problem of voice activity detection, however, the application did not allow for real-time detection which is important in speech processing applications. In this paper, we propose a voice activity detector (VAD) based on a sliding window, MMC algorithm which allows for real-time detection. Our system requires a separate initialization stage which imposes an initial detection delay, however, once initialized the system can operate in real-time. Using TIMIT speech under several NOISEX-92 noise backgrounds at various SNRs, we show that our average speech and non-speech hit rates are better than state-of-the-art VADs.

关键词： Speech analysis Classification algorithms Speech analysis sorting algorithm real-time detection Support Vector Network Voice Speech processing Cluster Analysis algorithms activity METAL MATRIX COMPOSITES Mitocin-C

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Operability Improvement for Myoelectric Prosthetic Hand Using Brain Machine Interface Identification of Eight Forearm Motions Based on Myoekectric Potential

Operability Improvement for Myoelectric Prosthetic Hand Usin...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Kana Kuniyasu Jun Ishikawa Department of Robotics and Mechatronics Tokyo Denki University Tokyo Japan

ISBN: (纸本)9781479906505

This paper proposes a myoelectric prosthetic hand control system using a brain machine interface that uses electromyogram signals and other biosignals such electro-encephalographic signals. The proposed system consists of a feature vector extractor, a neural network estimator, a reference trajectory generator, and an adaptive corrector. As a preparation, an experimental system has been prototyped without adaptive correction using other biosignals. The system is to classify eight forearm motions by using two electromyogram signals and to generate reference trajectories for servo controllers of a prosthetic hand. The experimental results showed that the proposed classification algorithm has basically worked well and that the reference trajectory generator has a potential to make appropriate smooth trajectories. Furthermore, electroencephalogram signals during forearm motion identification were measured and analyzed to check whether reaction to indicate any malfunctions was observed.

关键词： myoelectric prosthetic hand electromyogram (EMG) signals brain machine interface (BMI) neural network Neural network signals Forearm trajectory malfunctions control systems sorting algorithm Electromyogram machine servo controller

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CENTROID-BASED TEXTURE CLASSIFICATION USING THE SIRV REPRESENTATION

CENTROID-BASED TEXTURE CLASSIFICATION USING THE SIRV REPRESE...

引用

IEEE International Conference on Image Processing

作者： Aurélien Schutz Lionel Bombrun Yannick Berthoumieu Université de Bordeaux ENSEIRB-Matmeca Laboratoire IMS Groupe Signal et Image

ISBN: (纸本)9781479923427

This paper introduces a centroid-based (CB) supervised classification algorithm of textured images. In the context of scale/orientation decomposition, we demonstrate the possibility to develop centroid approach based on multivariate stochastic modeling. The main interest of the multivariate modeling comparatively to the univariate case is to consider spatial dependency as additional features for characterizing texture content. The aim of this paper is twofold. Firstly, we introduce the Spherically Invariant Random Vector (SIRV) representation for the modeling of wavelet coefficients. Secondly, from the specific properties of the SIRV process, i.e. the independence between the two sub-processes of the compound model, we derive centroid estimation scheme. Experiments from various conventional texture databases are conducted and demonstrate the interest of the proposed classification algorithm.

关键词： textured images Jeffrey divergence SIRV model centroid supervised classification Centroid sorting algorithm textures additional features specific properties

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Segmentation algorithms of Chromosome Images

Segmentation Algorithms of Chromosome Images

引用

International Conference on Computer Science and Network Technology

作者： Wenzhong Yan Dongming Li Dept. of Comput. North China Inst. of Sci. & Technol. Beijing China

ISBN: (纸本)9781479905607

The chromosome segmentation is the most important step in the automatic chromosome analysis system. In this paper, a few basic chromosome segmentation algorithms are investigated firstly. As a common phenomenon, the partial touching and overlapping chromosomes occur in almost every metaphase images. So, as a difficult yet vital problem, the algorithms for overlapping chromosomes are discussed in detail. The principle and the realization of these algorithms are analyzed. Results of these algorithms are compared and discussed.

关键词： biology computing cellular biophysics image segmentation automatic chromosome analysis system chromosome images chromosome segmentation algorithm metaphase image overlapping chromosomes partial touching chromosomes segmentation algorithms Accuracy algorithm design and analysis Biological cells Classification algorithms Entropy Image segmentation Shape chromosome overlapping segmentation image segmentation algorithm design and analysis Chromosomes biology computing cellular biophysics cytology sorting algorithm segmentation Segmentation entropy

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