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Object Decomposition based on Superellipses

Object Decomposition based on Superellipses

引用

International conference on Innovative Computing, Information and Control (ICICIC)

作者： Wu-Chih Hu Department of Computer Science and Information Engineering National Penghu University Taiwan

Superellipse is a flexible primitive and it can represent a large variety of shapes. Hence, this paper presents a superellipse decomposition of object contours. In the proposed method, part segmentation algorithm based on breakpoint types is used to obtain the convex parts of object contours. These convex parts are isolated and fitted by using superellipses. Tests show that the proposed method has a number of interesting properties including being scale/rotation/translation invariant, threshold-free, and efficient

关键词： Shape Fitting Partitioning algorithms pattern recognition Humans computer science Testing Application software computer vision Feature extraction

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Multiple Cardiac Arrhythmia recognition Using Adaptive Wavelet Network

Multiple Cardiac Arrhythmia Recognition Using Adaptive Wavel...

引用

Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Chia-Hung Lin Pei-Jarn Chen Yung-Fu Chen You-Yun Lee Tainsong Chen Department of Electrical Engineering Kao-Yuan Institute of Technology Kaohsiung Taiwan Institute of Biomedical Engineering National Cheng Kung University Tainan Taiwan Department of Electrical Engineering Southern Taiwan University of Technology Tainan Taiwan Department of Computer Science and Information Engineering Da-Yeh University Changhua Taiwan

This paper proposes a method for electrocardiogram (ECG) heartbeat pattern recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting from feature extraction and conversion of QRS complexes, and then identifying cardiac arrhythmias based on the detected features. The discrimination method of ECG beats is a two-subnetwork architecture, consisting of a wavelet layer and a probabilistic neural network (PNN). Morlet wavelets are used to extract the features from each heartbeat, and then PNN is used to analyze the meaningful features and perform discrimination tasks. The AWN is suitable for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results obtained by testing the data of the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed method

关键词： Adaptive systems Electrocardiography Heart beat Feature extraction pattern recognition computer vision Neural networks Wavelet analysis Data mining Performance analysis

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Directional Filter Bank: An Enhancement for Fingerprint Feature Detection

Directional Filter Bank: An Enhancement for Fingerprint Feat...

引用

IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： S. Tantachun C. Pintavirooj M. Sangworasil Y. Kitjaidure Department of Electronics Faculty of Engineering King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

Fingerprints is the most popular biometric modality. Fingerprint features include core, delta, ridge bifurcation, ridge ending, enclosure and short edge. In order to increase the performance of fingerprint identification system, it is essential that these directional-related features are needed to be enhanced. In this paper, we purpose the directional filter bank to enhance the fingerprint features which later used in classification step. Our 2D FIR filter is designed using a 2D frequency-transform method which is easily implemented and easily imposed a zero phase response. Our proposed technique demonstrates the promising results

关键词： Filter bank Fingerprint recognition computer vision Image matching Biometrics Finite impulse response filter Discrete wavelet transforms Image reconstruction pattern recognition Humans

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Adaptive Weighting of Local Classifiers by Particle Filter

Adaptive Weighting of Local Classifiers by Particle Filter

引用

International conference on pattern recognition

作者： K. Hotta University of Electro-Communications Chofu Tokyo Japan

ISBN: (纸本)0769525210

This paper presents adaptive weighting method for combining local classifiers by particle filter. In recent years, the effectiveness of combination of local classifiers (features) is reported. However, those methods can not cope with partial occlusion or shadows by illumination direction changes, because the stable weight is used for combining local classifiers. To be robust to them, the weight should be changed adoptively. Namely, we must select the good weight set given high likelihood from the weight space adoptively. For this purpose, particle filter is used. Each particle corresponds to the weight set for combining local classifiers. By selecting the particle (weight set) given high likelihood in current situation, the proposed method can cope with partial occlusion. The proposed method is applied to face tracking problem. Performance is evaluated by using the test sequence that the occluded area is changed dynamically. The proposed method decreases the weight for occluded region automatically, and it can track face under partial occlusion. Effectiveness of the proposed method is shown by comparison with stable weight set used in conventional methods

关键词： Particle filters Robustness Lighting Object detection Target tracking Testing Support vector machines Support vector machine classification Face detection computer vision

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Fractal Image Compression on Spiral Architecture

Fractal Image Compression on Spiral Architecture

引用

International conference on computer Graphics, Imaging and Visualization, CGIV

作者： Xiangjian He Huaqing Wang Qiang Wu T. Hintz Namho Hur Broadcasting System Research Group Digital Broadcasting Research Division Electronics and Telecommnications Research Institute Daejeon South Korea Computer Vision Research Group University of Technology Sydney Australia

Image compression has many applications. For example, it is an important step for distributed and network based pattern recognition. For real time object recognition or reconstruction, image compression can greatly reduce the image size, and hence increase the processing speed and enhance performance. Fractal image compression is a relatively recent image compression method. Its basic idea is to represent images as a fixed point of a contractive iterated function system (IFS). Spiral architecture (SA) is a novel image structure on which images are displayed as a collection of hexagonal pixels. The efficiency and accuracy of image processing on SA have been demonstrated in many recently published papers. We have shown the existence of contractive IFS's through the construction of a complete metric space on SA. The selection of range and domain blocks for fractal image compression is highly related to the uniform image separation specific to SA. In this paper, we review the current research work on fractal image compression based on SA. We compare the results obtained on SA and the traditional square structure in terms of compression ratio and PSNR

关键词： Fractals Image coding Spirals computer architecture Broadcast technology Pixel Image storage Helium computer vision Application software

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An Image Segmentation Framework Based on Patch Segmentation Fusion

An Image Segmentation Framework Based on Patch Segmentation ...

引用

International conference on pattern recognition

作者： Lei Zhang Xun Wang N. Penwarden Qiang Ji Rensselaer Polytechnic Institute Troy NY USA

ISBN: (纸本)0769525210

In this paper we present an image segmentation framework based on patch segmentation fusion. An image is first split into small patches. Segmentation is then performed on each patch using the algorithms of standard normalized cut (Shi and Malik, 1997), mean shift clustering (C. D. and M. P., 2002), or K-means clustering. Each region in a patch segmentation is assigned a label so as to represent different parts. After that, a connectedness value is calculated between any two overlapping patch segmentations with certain kinds of labeling. A weight called border strength is calculated for a segmentation with a certain labeling. We optimize a global criterion function that quantifies the consistency and quality of patch segmentations by a simulated annealing algorithm in order to find the optimal patch segmentations and labeling. Finally, global segmentation is reconstructed by fusing patch segmentations by multiple techniques. Experimental results on natural images are reported. Precision and recall rates are also calculated to evaluate the performance quantitively

关键词： Image segmentation Image edge detection Clustering algorithms Labeling Partitioning algorithms Simulated annealing Graphical models Image reconstruction computer vision Supervised learning

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Multi-Scale Analysis of Odd Gabor Transform for Edge Detection

Multi-Scale Analysis of Odd Gabor Transform for Edge Detecti...

引用

International conference on Innovative Computing, Information and Control (ICICIC)

作者： Zhenfeng Zhu Hanqing Lu Yao Zhao Institute of Information and Science Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China

This paper proposes an adaptive edge detection technique based on multi-scale analysis of odd Gabor transform. In order to obtain the odd Gabor transform based edge response efficiently, an improved scheme is suggested, which can avoid the inverse influence brought by the estimation error of gradient direction on the construction of odd Gabor filter templates. To sharpen the edge response, an adjacent scales multiplication in odd Gabor transform domain is introduced. Moreover an adaptive threshold determining method related to the exponential distribution property of sharpened edge response is also presented. The experimental results on both synthetic and real world images show that our scheme is robust and has good edge detection performance

关键词： Image edge detection Gabor filters Detectors Estimation error Robustness computer vision Fourier transforms Convolution Laboratories pattern recognition

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Feature Ranking and Selection for Intrusion Detection Using Artificial Neural Networks and Statistical Methods

Feature Ranking and Selection for Intrusion Detection Using ...

引用

International Joint conference on Neural Networks (IJCNN)

作者： A. Tamilarasan S. Mukkamala A.H. Sung K. Yendrapalli Department of Computer Science New Mexico Institute of Mining and Technology Socorro NM USA

This paper describes results concerning the robustness and generalization capabilities of artificial neural networks in detecting intrusions using network audit trails. Through a variety of comparative experiments, it is found that neural network performs the best for intrusion detection. Feature selection is as important for intrusion detection as it is for many other problems. We present our work of identifying intrusion and normal pertinent features and evaluating the applicability of these features in detecting intrusions. We also present different feature selection methods for intrusion detection. It is demonstrated that, with appropriately chosen features, intrusions can be detected in real time or near real time.

关键词： Intrusion detection Artificial neural networks Statistical analysis Neural networks pattern recognition computer vision Detectors Performance analysis Humans Artificial intelligence

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A Modified Fuzzy K-means Clustering using Expectation Maximization

A Modified Fuzzy K-means Clustering using Expectation Maximi...

引用

IEEE International conference on Fuzzy Systems (FUZZ-IEEE)

作者： S. Nasser R. Alkhaldi G. Vert Department of Computer Science and Engineering University of Nevada슠Reno Reno NV USA

K-means is a popular clustering algorithm that requires a huge initial set to start the clustering. K-means is an unsupervised clustering method which does not guarantee convergence. Numerous improvements to K-means have been done to make its performance better. Expectation Maximization is a statistical technique for maximum likelihood estimation using mixture models. It searches for a local maxima and generally converges very well. The proposed algorithm combines these two algorithms to generate optimum clusters which do not require a huge value of K and each cluster attains a more natural shape and guarantee convergence. The paper compares the new method with Fuzzy K-means on benchmark iris data.

关键词： Clustering algorithms Convergence Clustering methods pattern recognition Fuzzy logic Maximum likelihood estimation Shape Iris Data engineering computer vision

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Extracting High Level Semantics by Means of Speech, Audio, and Image Primitives in Surveillance Applications

Extracting High Level Semantics by Means of Speech, Audio, a...

引用

IEEE International conference on Image Processing

作者： Lutz Goldmann Amjad Samour Mustafa Karaman Thomas Sikora Communication Systems Group Technical University Berlin Berlin Germany

Traditional surveillance systems are usually based on visual information only. With the emerging multimedia analysis techniques, interests are changing towards systems that incorporate multiple sensors and different modalities, which leads to new ways of analyzing this multimedia data and more sophisticated applications. This paper shortly reviews the ideas of traditional surveillance systems and explains actual research interests in this domain. Then, it focuses on the typical structure, goals, and applications of multimedia surveillance systems. These issues are supported by short descriptions of selected analysis steps of such a system currently under development. Some experimental results are given to illustrate the extracted semantics and to assess the performance of the individual steps.

关键词： Speech Surveillance Multimedia systems Data mining Information analysis Vehicles computer vision pattern recognition Smart cameras Data analysis

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