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2nd international conference on Ubiquitous computing and Multimedia Applications

作者： Bhattacharyya, Debnath Kim, Tai-hoon Hannam Univ Dept Multimedia Daejeon South Korea MPCT Dept Comp Sci & Engn Gwalior India

ISBN: (纸本)9783642209970

In this paper we propose a statistical similarity checking method to identify the similarity between two handwritten signatures. Initially, we process the handwritten signature images in various image processing techniques and store in a magnetic storage device. In turn, during the time of similarity checking, our algorithm generates a binary dotplot matrix for each of the signature image in the storage with the input signature image of a particular user domain. In this paper, these binary dotplot matrices guide us to identify the degree of similarity or homology or duplicate copy of handwritten signature of any user. In this paper we deal with only binary dotplot similarity technique.

关键词： pattern recognition bio-informatics similarity Image processing binary dotplot

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Age Regression from soft Aligned Face Images Using Low Computational Resources

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5th Iberian conference on pattern recognition and Image Analysis (IbPRIA)

作者： Bekios-Calfa, Juan Buenaposada, Jose M. Baumela, Luis Univ Catolica Norte Dept Ingn Sistemas & Computac Av Angamos 0610 Antofagasta Chile Univ Rey Juan Carlos Dept Ciencias Computac Mostoles Spain Univ Politecn Madrid Dept Artificial Intelligence Madrid Spain

ISBN: (纸本)9783642212567;9783642212574

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e. g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application.

关键词： Database systems

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Time Series pattern recognition via softcomputing

Time Series Pattern Recognition via SoftComputing

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international conference on P2P, Parallel, Grid, Cloud and Internet computing (3PGCIC)

作者： Martin Kotyrba Zuzana Oplatkova Eva Volna Roman Senkerik Vaclav Kocian Michal Janosek Department of Informatics and Computers University of Ostrava Ostrava Czech Republic Faculty of Applied Informatics Tomas Bata University Zlin Zlin Czech Republic

In this paper we develop two methods that are able to analyze and recognize patterns in time series. The first model is based on analytic programming (AP), which belongs to soft computing. AP is based as well as genetic programming on the set of functions, operators and so-called terminals, which are usually constants or independent variables. The second one uses an artificial neural network that is adapted by back propagation. Artificial neural networks are suitable for pattern recognition in time series mainly because of learning only from examples. There is no need to add additional information that could bring more confusion than recognition effect. Neural networks are able to generalize and are resistant to noise. On the other hand, it is generally not possible to determine exactly what a neural network learned and it is also hard to estimate possible recognition error. They are ideal especially when we do not have any other description of the observed series. This paper also includes experimental results of time series pattern recognition carried out with both mentioned methods, which have proven their suitability for this type of problem solving.

关键词： Neurons Time series analysis Programming pattern recognition Training Biological neural networks Vectors

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Affective computing and Intelligent Interaction - 4th international conference, ACII 2011, Proceedings

Affective Computing and Intelligent Interaction - 4th Intern...

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4th international conference on Affective computing and Intelligent Interaction, ACII 2011

ISBN: (纸本)9783642245992

The proceedings contain 130 papers. The topics discussed include: affective modeling from multichannel physiology: analysis of day differences;the dynamics between student affect and behavior occurring outside of educational software;unsupervised temporal segmentation of talking faces using visual cues to improve emotion recognition;the affective experience of handling digital fabrics: tactile and visual cross-modal effects;predicting learner engagement during well-defined and ill-defined computer-based intercultural interactions;a pattern-based model for generating text to express emotion;interpretations of artificial subtle expressions (ASEs) in terms of different types of artifact: a comparison of an on-screen artifact with a robot;relevance vector machine based speech emotion recognition;toward a computational model of affective responses to stories for augmenting narrative generation;and toward a computational model of affective responses to stories for augmenting narrative generation.

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Affective computing and Intelligent Interaction - 4th international conference, ACII 2011, Proceedings

Affective Computing and Intelligent Interaction - 4th Intern...

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4th international conference on Affective computing and Intelligent Interaction, ACII 2011

ISBN: (纸本)9783642245701

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Class-incremental kernel discriminative common vectors

Class-incremental kernel discriminative common vectors

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2011 7th international conference on Computational Intelligence and Security, CIS 2011

作者： He, Yunhui Department of Communications Engineering Nanjing University of Information Science and Technology Nanjing 210044 China

ISBN: (纸本)9780769545844

In this paper, we propose an efficient algorithm for implementing the class-incremental kernel discriminative common vectors method via kernel method. One nonlinear discriminative common vector is computed for each class by projecting a sample in each class onto the orthonormal nonlinear discriminative vector. The orthogonalization procedure is performed twice in feature space which is only involved computing a kernel matrix and performing Cholesky decomposition on the kernel matrix. Thus, the real-time performance of classification is guaranteed. The theoretical justification is presented in this paper. © 2011 IEEE.

关键词： pattern recognition

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Recent Advances in Circuits, Systems, Signal and Telecommunications - 5th WSEAS international conference on Circuits, Systems, Signal and Telecommunications, CISST'11

Recent Advances in Circuits, Systems, Signal and Telecommuni...

引用

Applied computing conference 2010, ACC'10

ISBN: (纸本)9789604742714

The proceedings contain 20 papers. The topics discussed include: real-time digital oscilloscope implementation in 90nm CMOS technology FPGA;microcontroller based closed-loop automatic speed control of DC motor using PWM;a design of parameter optimal iterative learning control for linear discrete-time systems;wall climbing robot: mechanical design and implementation;graph method for solving switched capacitors circuits;isolated word recognition based on intelligent segmentation by using hybrid HTD-HMM;utilizing intelligent segmentation in isolated word recognition using a hybrid HTD-HMM;performance studies of antenna pattern design using the minimax algorithm;digital architecture for a median filter of image based on sorting network;and responses of semiconductor arrays due to photon absorption.

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Sampling step importance in hierarchical semantic segmentation of microscopic images

Sampling step importance in hierarchical semantic segmentati...

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15th international conference on System Theory, Control and computing, ICSTCC 2011

作者： Smochina, Cristian Manta, Vasile Kropatsch, Walter Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iasi Romania Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

ISBN: (纸本)9781457711732

The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape, orientation or texture). In this paper we improve the technique presented in [17] used to identify the epithelial nuclei (crypt) against interstitial nuclei in microscopic images taken from colon tissues. In the proposed enhanced approach, the crypt inner boundary is detected using the closing morphological pyramid instead of morphological hierarchy. The outer crypt border is determined by the epithelial nuclei, overlapped by the maximal isoline of the inner boundary. The use of sampling in building the pyramid offers computational efficiency, reduces the amount of used memory, increase the robustness and preserve the quality results. An analysis of the two approaches is performed considering the number of pixels processed to create each level. Also the relation between the levels of the hierarchical structures is established. © 2011 Faculty of Auto Control.

关键词： Computational efficiency

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Characterizing the impact of soft errors on iterative methods in scientific computing 11

Characterizing the impact of soft errors on iterative method...

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Proceedings of the international conference on Supercomputing

作者： Shantharam, Manu Srinivasmurthy, Sowmyalatha Raghavan, Padma Pennsylvania State University United States

ISBN: (纸本)9781450301022

The increase in on-chip transistor count facilitates achieving higher performance, but at the expense of higher susceptibility to soft errors. In this paper, we characterize the challenges posed by soft errors for large-scale applications representative of workloads on supercomputing systems. Such applications are typically based on the computational solution of partial differential equation models using either explicit or implicit methods. In both cases, the execution time of such applications is typically dominated by the time spent in their underlying sparse matrix vector multiplication kernel (SpMV, t ← A·y). We provide a theoretical analysis of the impact of a single soft error through its propagation by a sequence of sparse matrix vector multiplication operations. Our analysis indicates that a single soft error in some ith component of the vector y can corrupt the entire resultant vector in a relatively short sequence of SpMV operations. Additionally, the propagation pattern corresponds to the sparsity structure of the coefficient matrix A and the magnitude of the error grows non-linearly as(||A i||2*)k, after k SpMV operations, where, ||Ai*||2 is the 2-norm of the ith row of A. We corroborate this analysis with empirical observations on a model heat equation using explicit method and well known sparse matrix systems (matrices from a test suite) for the implicit method using iterative solvers such as CG, PCG and SOR. Our results indicate that explicit schemes will suffer from soft error induced numerical instabilities, thus exacerbating intrinsic stability issues for such methods, that impose constraints on relative time and space step sizes. For implicit schemes, linear solver performance through widely used CG and PCG schemes, degrades by a factor as high as 200x, whereas, a stationary scheme such as SOR is inherently soft error resilient. Our results thus indicate the need for new approaches to achieve soft error resiliency in such methods and a critical ev

关键词： Iterative methods

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A novel facial feature extraction method based on ICM network for affective recognition

A novel facial feature extraction method based on ICM networ...

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2011 international Joint conference on Neural Network, IJCNN 2011

作者： Mokhayeri, F. Akbarzadeh, M.-R. Islamic Azad University Mashhad Branch Mashhad Iran Center for Applied Research on Soft Computing and Intelligent Systems Departments of Electrical Engineering and Computer Engineering Ferdowsi University of Mashhad Mashhad Iran

ISBN: (纸本)9781457710865

This paper presents a facial expression recognition approach to recognize the affective states. Feature extraction is a vital step in the recognition of facial expressions. In this work, a novel facial feature extraction method based on Intersecting Cortical Model (ICM) is proposed. The ICM network which is a simplified model of Pulse-Coupled Neural Network (PCNN) model has great potential to perform pixel grouping. In the proposed method the normalized face image is segmented into two regions including mouth, eyes using fuzzy c-means clustering (FCM). Segmented face images are imported into an ICM network with 300 iteration number and pulse image produced by the ICM network is chosen as the face code, then the support vector machine (SVM) is trained for discrimination of different expressions to distinguish the different affective states. In order to evaluate the performance of the proposed algorithm, the face image dataset is constructed and the proposed algorithm is used to classify seven basic expressions including happiness, sadness, fear, anger, surprise and hate The experimental results confirm that ICM network has great potential for facial feature extraction and the proposed method for human affective recognition is promising. Fast feature extraction is the most advantage of this method which can be useful for real world application. © 2011 IEEE.

关键词： Feature extraction

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