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Composite Kernels for Fusing Colour Information in Face Verification Systems

Composite Kernels for Fusing Colour Information in Face Veri...

引用

International Conference on Digital Image processing, ICDIP

作者： Fahimeh Salimi Mohammad T. Sadeghi Signal Processing Research Laboratory Department of Electrical and Computer Engineering Yazd University Yazd Iran

In this work, a novel method of fusing colour information in feature level is proposed considering a face verification system. For this purpose, composite kernels which have been already used in support vector machine classifier is applied within the framework of the generalised discriminant analysis (GDA) algorithm. The performance of the resulting system is evaluated using the XM2VTS face database and its associated experimental protocols. Our experiments show that by combining colour information using the proposed approach the good classification performance demonstrated by the kernel based methods can be further improved.

关键词： Kernel Principal component analysis Feature extraction Support vector machines Support vector machine classification Signal processing algorithms Face recognition Linear discriminant analysis Color Digital signal processing

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Color quantization using principal components for initialization of Kohonen SOFM

Color quantization using principal components for initializa...

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IEEE International Conference on Image processing

作者： Dimitris Mavridis Nikos Papamarkos Image Processing and Multimedia Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Greece

A new method is proposed for initializing Kohonen's self-organizing feature maps (SOFM) of fixed zero neighborhood radius for use in color quantization. The method employs the two largest principal components of the input image so that the initial weights of a number of neurons approach the input image color distribution. The rest of the neurons are initialized using the smallest principal component of the input image. Namely, standard SOFM is applied to the projection of the input image pixels onto the plane spanned by the two largest principal components and to pixels of the original image defined by the smallest principal component. The neuron values which emerge initialize the final SOFM of fixed zero neighborhood radius that performs the color quantization of the original image. Experimental results show that the proposed method can often produce smaller quantization errors than standard SOFM and other color quantization methods.

关键词： Quantization Neurons Neural networks Pixel Image color analysis Image processing Clustering algorithms Fuzzy neural networks Vectors Lattices

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Fast face recognition using a combination of image pyramid and hierarchical clustering algorithms

Fast face recognition using a combination of image pyramid a...

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Hajar Momeni Mohammad T Sadeghi Hamid R Abutalebi Signal Processing Research Laboratory Department of Electrical and Computer Engineering Yazd University Yazd Iran

Classifying an unknown object in image retrieval systems using the nearest neighbour classifier would be very time consuming when the number of the objects within the associated database is high. Generating a dendrogram using a Hierarchical Agglomerative Clustering (HAC) algorithm and searching the database images from coarse to fine resolutions using image pyramids are two important groups of techniques widely used for dealing with this problem. In this paper, a novel algorithm is proposed by combining these methods within the framework of a face recognition system. The search process is performed in a coarse-to-fine manner using image pyramids. On the bottom level of the pyramid (the finest resolution), a set of dendrograms is formed using the HAC algorithm. Our experimental studies show that the recognition process can be speeded up by a factor of around 65 compare to the basic nearest neighbour classifier. In such a condition, however, the recognition rate is slightly reduced.

关键词： Face recognition Clustering algorithms Prototypes Image resolution Signal processing algorithms Image databases Computational complexity Fingerprint recognition Face detection Laboratories

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Comparison of basis-vector selection methods for structural modeling of hyperspectral imagery

Comparison of basis-vector selection methods for structural ...

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Imaging Spectrometry XIV

作者： Peña-Ortega, Carolina Vélez-Reyes, Miguel Computer Engineering Department Laboratory for Applied Remote Sensing and Image Processing Electrical University of Puerto Rico Mayagüez Campus P.O.Box 3535 Mayagüez PR 00681-3535 United States

ISBN: (纸本)9780819477477

This paper presents a comparison of different methods for structural modeling of hyperspectral imagery for target detection. We study structured models, based on linear subspaces and convex polyhedral cones, and their application for target detection. Different training methods are studied: Singular Value Decomposition (SVD) is used for subspace modeling, and Maximum Distance (MaxD) and Positive Matrix Factorization (PMF) for convex polyhedral modeling. We study different detectors based on orthogonal and oblique projections for subspace and convex polyhedral cones and evaluate their performance. Experimental results using HYDICE imagery are presented. © 2009 SPIE.

关键词： Singular value decomposition

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Performance analysis of the Neyman-Pearson fusion center for spectrum sensing in a Cognitive Radio network

Performance analysis of the Neyman-Pearson fusion center for...

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Eurocon

作者： Somayeh Mosleh Ali Akbar Tadaion Mostafa Derakhtian Signal Processing Laboratory Department of Electrical and Computer Engineering Yazd University Yazd Iran Department of Electrical Engineering Shiraz University Shiraz Iran

A key technology in cognitive radio (CR) is spectrum sensing that senses the spectrum and reports the available vacant channels. However, due to some effects such as fading or shadowing, an individual sensor may not be able to reliably detect the existence of a primary user (PU). Cooperative spectrum sensing that is proposed to solve such problem, uses a distributed detection system to overcome the severe decadent of received signal strength at some locations in the network. This paper considers the performance of a distributed Neyman-Pearson (N-P) detection system consisting of N sensors and a fusion center, in which the decision rules of the sensors have been given and the decisions of different sensors are mutually independent conditioned on both hypotheses. Theoretical analysis on the performance of this fusion center is carried out. We obtain the conditions for the fusion center to achieve an overall probability of detection that is greater than the local probability of detection of each sensor. Numerical results show that the AND, OR and majority decision fusion rules are the special cases of the N-P fusion rule.

关键词： Performance analysis Cognitive radio Chromium Frequency Sensor fusion Detectors Sensor systems Fading Intelligent networks Shadow mapping

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Real-time processing of simplex growing algorithm

Real-time processing of simplex growing algorithm

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2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009

作者： Wu, Chao-Cheng Chang, Chein-I. Ren, Husen Chang, Yang Lang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Center for Space and Remote Sensing Research National Central University Jhong-Li Tao-Yuan Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781424433957

Simplex growing algorithm (SGA) was recently developed as an alternative to the N-finder algorithm (N-FINDR) which is shown to be a promising endmember extraction technique. This paper further extends the SGA to a real-time processing algorithm, referred to as real-time SGA (RT SGA) that can effectively address four major issues arising in practical implementation for N-FINDR, (1) use of random initial endmembers which causes inconsistent final results, (2) very high computational complexity which results from an exhaustive search for finding all endmembers simultaneously, (3) requirement of dimensionality reduction because of enormous data volumes to be processed and (4) lack of real-time capability. ©2009 IEEE.

关键词： Extraction

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A theoretical model for spontaneous seizure generation based on Markov chain process

A theoretical model for spontaneous seizure generation based...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Farzaneh Shayegh Saeed Sadri Rasoul AmirFattahi Digital signal Processing Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Here a theoretical model for seizure generation is presented. This is done according to a simple physiological model of epileptic activities in which seizure occurrence is mainly attributed to imbalance of excitatory/inhibitory ratio. Although factors that play a role in ictogenesis are not exactly known, this process can be assumed as a stochastic process. Indeed, we claim that in neural synapses both excitatory and inhibitory gains change randomly. We propose that their values theoretically change as a tri-variant 2 nd -order Markov chain process. States of Markov chain must be selected such that any sudden change is avoided. Important result of this modeling is that epileptic behavior of a cortical area may appear in various manners not in a unique one. Thus different observations in seizure onset all are validated and it is proved that they do not contradict each other.

关键词： Brain modeling Epilepsy Electroencephalography Neurons Signal generators Stochastic processes Neural engineering Digital signal processing Electronic mail Synchronous generators

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Decision Level Fusion of Colour Histogram Based Classifiers for Clustering of Mouth Area Images

Decision Level Fusion of Colour Histogram Based Classifiers ...

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International Conference on Digital Image processing, ICDIP

作者： Fahimeh Salimi Mohammad T. Sadeghi Signal Processing Research Laboratory Department of Electrical and Computer Engineering Engineering Research Centre Yazd University Yazd Iran

It is well known that in many situations combining diverse classifiers can improve the performance of a classification system. In this paper, a new histogram based lip segmentation technique is proposed considering local kernel histograms in different illumination invariant colour spaces. The histogram is computed in local areas using two Gaussian kernels; one in the colour space and the other in the spatial domain. Using the estimated histogram, the posterior probability associated to non-lip class is then computed for each pixel. This process is performed considering different colour spaces. A weighted averaging method is then used for fusing the posterior probability values. As the result a new score is obtained which is used for labeling the pixels as lip or non-lip. The advantage of the proposed method is that the segmentation process is totally unsupervised. So, the method is robust against different variations such as variation in lip shape, skin colour, facial hair, illumination, etc. Moreover, an improved performance is achieved by fusing colour information.

关键词： Histograms Mouth Kernel Lighting Diversity reception Image segmentation Color Labeling Robustness Shape

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A fusion-based approach for tolerating faults in finite state machines

A fusion-based approach for tolerating faults in finite stat...

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International Symposium on parallel and Distributed processing (IPDPS)

作者： Vinit Ogale Bharath Balasubramanian Vijay K. Garg Parallel and Distributed Systems Laboratory Department of Electrical and Computer Engineering University of Texas Austin USA IBM India Research Laboratory New Delhi India

ISBN: (纸本)9781424437511

Given a set of n different deterministic finite state machines (DFSMs) modeling a distributed system, we examine the problem of tolerating f crash or Byzantine faults in such a system. The traditional approach to this problem involves replication and requires n middot f backup DFSMs for crash faults and 2 middot n middot f backup DFSMs for Byzantine faults. For example, to tolerate two crash faults in three DFSMs, a replication based technique needs two copies of each of the given DFSMs, resulting in a system with six backup DFSMs. In this paper, we question the optimality of such an approach and present an approach called (f, m)-fusion that permits fewer backups than the replication based approaches. Given n different DFSMs, we examine the problem of tolerating f faults using just m additional DFSMs. We introduce the theory of fusion machines and provide an algorithm to generate backup DFSMs for both crash and Byzantine faults. We have implemented our algorithms in Java and have used them to automatically generate backup DFSMs for several examples.

关键词： Automata computer crashes Laboratories Concurrent computing Distributed computing Fusion power generation Standby generators Thermal sensors Counting circuits Java

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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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