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A mobile camera tracking system using GbLN-PSO with an adaptive window

A mobile camera tracking system using GbLN-PSO with an adapt...

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2nd international conference on Computational intelligence, Modelling and Simulation 2011, CIMSim 2011

作者： Musa, Zalili Bakar, Rohani Abu Watada, Junzo Faculty of Computer System and Software Engineering University Malaysia Pahang Kuantan Malaysia Graduate School of Information Production and Systems Waseda University Fukuoka Japan

ISBN: (纸本)9780769545622

The availability of high quality and inexpensive video camera, as well as the increasing need for automated video analysis is leading towards a great deal of interest in numerous applications. However the video tracking systems is still having many open problems. Thus, some of research activities in a video tracking system are still being explored. Generally, most of the researchers are used a static camera in order to track an object motion. However, the use of a static camera system for detecting and tracking the motion of an object is only capable for capturing a limited view. Therefore, to overcome the above mentioned problem in a large view space, researcher may use several cameras to capture images. Thus, the cost will increases with the number of cameras. To overcome the cost increment a mobile camera is employed with the ability to track the wide field of view in an environment. Conversely, mobile camera technologies for tracking applications have faced several problems;simultaneous motion (when an object and camera are concurrently movable), distinguishing objects in occlusion, and dynamic changes in the background during data capture. In this study we propose a new method of Global best Local Neighborhood Oriented Particle Swarm Optimization (GbLN-PSO) to address these problems. The advantages of tracking using GbLN-PSO are demonstrated in experiments for intelligent human and vehicle tracking systems in comparison to a conventional method. The comparative study of the method is provided to evaluate its capabilities at the end of this paper. © 2011 IEEE.

关键词： pattern matching

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Enriched score access for computer assisted composition in PWGL 1

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3rd international conference on Mathematics and Computation in Music, MCM 2011

作者： Kuuskankare, Mika Sibelius Academy Department for Doctoral Studies in Music and Research Helsinki Finland STMS: IRCAM/CNRS/UPMC Paris France

ISBN: (数字)9783642215902

ISBN: (纸本)9783642215896

PWGL is a visual composition environment that can be used to, among other things, solve musical constraints problems. The constraints system within PWGL, PWGLConstraints, allows us to write rules using a special pattern-matching language. Typically, the assignments use as a starting point a score prepared with the help of Expressive Notation Package (ENP). In this paper we present an extension to the PWGLConstraints pattern-matching language which allows us to access information from ENP to assist with the compositional process. ENP provides a rich library of standard and user-definable expressions called ENP-expressions. They range from standard articulation markings (such as staccatos and slurs) to fully interactive multi-purpose graphical expressions. A special syntax is developed which allows us to retrieve information about and contained by the expressions. In this paper, the syntax and the present state of the system are illustrated using a working example. © Springer-Verlag Berlin Heidelberg 2011.

关键词： pattern matching

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Study the iris segmentation method for biometric pattern recognition

Study the iris segmentation method for biometric pattern rec...

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international conference on Next Generation Networks and Services (NGNS)

作者： Hentati Raida Moncef Boussellmi Mohamed Abid Research Unit CES National school of Engineers of Sfax Sfax Tunisia

Several system require authenticating a person's identity before giving access to resources. With new advances in technology, biometrics is one of the most promising techniques in recognition human. Biometrics intends to identify a person by his physical and/or behavioural characteristic. Among biometric technologies, iris recognition has received increasing attention due to its high reliability. Iris recognition is a biometric authentication technology that uses pattern recognition techniques based on iris characteristics. In this paper we propose a new biometric-based Iris segmentation system. The system acquires the biometric data in the data base CASIA version 1.0. The experimental results are compared to release the performances of our algorithms.

关键词： Iris recognition Transforms Feature extraction Image edge detection Eyelashes Iris Noise

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Hiding depth map into stereo image in JPEG format using reversible watermarking 11

Hiding depth map into stereo image in JPEG format using reve...

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3rd international conference on Internet Multimedia Computing and Service, ICIMCS 2011

作者： Wang, Wenyi Zhao, Jiying Tam, Wa James Speranza, Filippo Wang, Zhou School of Information Technology and Engineering University of Ottawa 800 King Edward Ave. Ottawa ON K1N 6N5 Canada Advanced Video Systems Communication Research Centre Canada 3701 Carling Ave. Ottawa ON K2H 8S2 Canada Department of Electrical and Computer Engineering University of Waterloo 200 University Ave. West Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781450309189

This paper presents a lossless data hiding approach based on Discrete Cosine Transform (DCT) as a new application of watermarking. In this application, a depth map obtained from a stereoscopic image pair is embedded into one of the two images using a reversible watermarking algorithm. Different from existing approaches which hide depth map in spatial domain, the depth information is hidden in the quantized DCT domain of the stereo image in our method. This modification makes the watermarking algorithm compatible with JPEG and MPEG compression. The bit-shift operation is utilized to embed the depth map into its associated 2D image reversibly for the purpose of obtaining stereo image with high visual quality after the depth map is extracted. © 2011 ACM.

关键词： Discrete cosine transforms

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Robust Eye Movement recognition Using EOG Signal for Human-Computer Interface

Robust Eye Movement Recognition Using EOG Signal for Human-C...

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2nd international conference on Software Engineering and Computing Systems (ICSECS 2011)

作者： Aungsakun, Siriwadee Phinyomark, Angkoon Phukpattaranont, Pornchai Limsakul, Chusak 15 Kanjanavanich Rd Hat Yai 90112 Songkhla Thailand

ISBN: (纸本)9783642221903

Electrooculography (EOG) signal is one of the useful biomedical signals. Development of EOG signal as a control signal has been paid more increasing interest in the last decade. In this study, we are proposing a robust classification algorithm of eight useful directional movements that it can avoid effect of noises, particularly eye-blink artifact. Threshold analysis is used to detect onset of the eye movements. Afterward, four beneficial time features are proposed that are peak and valley amplitude positions, and upper and lower lengths of two EOG channels. Suitable threshold conditions were defined and evaluated. From experimental results, optimal threshold values were selected for each parameters and classification accuracies approach to 100% for three subjects testing. To avoid the eye-blink artifact, the first derivative was additionally implemented.

关键词： Electrooculography (EOG) signal eye movement eye blink artifact robust pattern recognition human-machine interface (HMI)

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Unsupervised texture image classification using self-organizing maps

Unsupervised texture image classification using self-organiz...

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2011 international conference on Artificial intelligence, ICAI 2011

作者： Petrov, Nedyalko Jordanov, Ivan School of Computing University of Portsmouth Buckingham Building Lion Terrace Portsmouth Hampshire PO1 3HE United Kingdom

ISBN: (纸本)9781601321855

In this paper we report results from unsupervised texture image classification on a data set of images collected with our intelligent machine vision system for pattern recognition (assuming no a priori human vision expert knowledge is available for the image classes). The simulation of the investigated system includes four main phases: data collection and feature extraction, feature analysis, classifier training, and classifier testing and evaluation. Self-Organizing Maps (SOM) are used for classification of the collection of images into several classes, based on their features and texture characteristics. Three main experiments are conducted during this research: in the first one, all extracted features are used for training the classifiers without any statistical pre-processing of the dataset;in the second simulation, the classifiers are trained after normalization of the available data;and in the last experiment, the trained SOMs use linear transformations of the original features, received after pre-processing with principal component analysis (PCA). Each test is performed 50 times and the classification results are assessed using three commonly applied metrics, namely: accuracy rate, sensitivity and specificity. Finally, the findings of this investigation are compared with results from other authors.

关键词： Feature extraction

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Security of a new password authentication scheme using fuzzy extractor with Smart Card

Security of a new password authentication scheme using fuzzy...

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international conference on Communication Software and Networks, ICCSN

作者： Xiaoyi Duan XiuYing Li Department of Electronics and Information Engineering Beijing Electronic Science and Technology Institute Beijing China

Yang-Yang proposed a new key authentication scheme based on fuzzy extractor with Smart Card. The authors claimed their scheme can resist the guessing attack, the parallel attack, masquerade attack and other attack types. In this paper, we find that the scheme proposed by Yang-Yang was unable to resist the masquerade attack as they originally claimed.

关键词： Fingerprint recognition Artificial intelligence Silicon Authentication

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3rd international Congress on Tools for Teaching Logic, TICTTL 2011

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3rd international Congress on Tools for Teaching Logic, TICTTL 2011

ISBN: (纸本)9783642213496

The proceedings contain 30 papers. The special focus in this conference is on Tools for Teaching Logic. The topics include: Panda: A Proof Assistant in Natural Deduction for All. A Gentzen Style Proof Assistant for Undergraduate Students;the Question of the Question in Critical Thinking?;adding a Dimension to Logic Diagramming;the Many Rewards of Putting Absolutely Everything into Introductory Logic;The SELL Project: A Learning Tool for E-Learning Logic;ten Years of Computer-Based Tutors for Teaching Logic 2000-2010: Lessons Learned;logic in Action: An Open Logic Courseware Project;a Teaching Tool for Proving Equivalences between Logical Formulae;mhy Bib I Fail Logic? Dyslexia in the Teaching of Logic;e-learning and Semantic Technologies: Tools and Concepts;information-Theoretic Perspective for Teaching Logic;teaching Sound Principles about Invalidity;systematic Errors as an Input for Teaching Logic;The AProS Project: Teaching Logic to Business and Engineering Students;using a Learner- and Teacher-Friendly Environment for Turing machine Programming and Testing;using an Argument Ontology to Develop Pedagogical Tool Suites;visual Tools for Teaching Propositional Logic;logicamente: A Virtual Learning Environment for Logic Based on Learning Objects;a Framework for Coping with Logically-Minded Arguments in Philosophy;a Logic Teaching Tool Based on Tableaux for Verification and Debugging of Algorithms;CT2.0: A Collaborative Database of Examples for Teaching Informal Logic;designing an Introductory Course to Elementary Symbolic Logic within the Blackboard E-learning Environment;Araucaria-PL: Software for Teaching Argumentation Theory;teaching Logic in Philosophy;ORGANON: Learning Management System for Basic Logic Courses;variables in Mathematics Education.

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Notice of Violation of IEEE Publication Principles: Neural network based intelligent local face recognition using local pattern averaging

Notice of Violation of IEEE Publication Principles: Neural n...

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international conference on Electronic Computer Technology

作者： N. Vivekanandan Reddy D. Abhilash Krishna P. Sharath Reddy R. Shirisha Department of Information Technology Sree Chaitanya College of Engineering Karim Nagar India

Notice of Violation of IEEE Publication Principles "Neural network Based Intelligent Local Face recognition Using Local pattern Averaging" by *** Reddy, *** Krishna, *** Reddy, *** in the 2011 3rd international conference on Electronics Computer Technology (ICECT), July 7, 2011, pp. 363 – 367 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: "Intelligent Local Face recognition" by Adnan Khashman in Recent Advances in Face recognition, Kresimir Delac, Mislav Grgic and Marian Stewart Bartlett (Ed.), InTech, 2008 This paper presents a brief review of known face recognition methods such as Principal Component Analysis (PCA) (Turk & Pentland, 1991), Linear Discriminant Analysis (LDA) (Belhumeur et al., 1997) and Locality Preserving Projections (LPP) (He et al., 2005), in addition to intelligent face recognition systems that use neural networks such as (Khashman, 2006) and (Khashman, 2007). There are many works emerging every year suggesting different methods for face recognition (Delac & Grgic, 2007); these methods are mostly appearance. This paper will also provide a detailed case study on intelligent local face recognition, where a neural network is used to identify a person upon presenting his/her face image. Local pattern averaging is used for face image preprocessing prior to training or testing the neural network. Averaging is a simple but efficient method that creates

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international conference on Tools for Artificial intelligence (ICTAI)

作者： Andre Burkovski Sebastian Klenk Gunther Heidemann Department for Intelligent Systems University of Stuttgart Stuttgart Germany

The Normalized Compression Distance (NCD) has gained considerable interest in pattern recognition as a similarity measure applicable to unstructured data of very different domains, such as text, DNA sequences, or images. NCD uses existing compression programs such as gzip to compute similarity between objects. NCD has unique features: It does not require any prior knowledge, data preprocessing, feature extraction, domain adaptation or any parameter settings. Further, the NCD can be applied to symbolic data and raw signals alike. In this paper we decompose the NCD and introduce a method to measure compression-based similarity without the need to use compression. The Length Delimiting Dictionary Distance (LD 3 ) takes the one component essential in compression methods, the dictionary generation, and strips the NCD of all dispensable components. The LD 3 performs "compression based pattern recognition without compression", keeping all of the above benefits of the NCD while achieving better speed and recognition rates. We first review the NCD, introduce LD 3 as the "essence" of NCD, and evaluate the LD 3 based on language tree experiments, authorship recognition, and genome phylogeny data.

关键词： Dictionaries Complexity theory pattern recognition Compressors Measurement Compression algorithms Image coding

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