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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

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A VQ-BASED FAST FACE recognition ALGORIthM USING OPTIMIZED CODEBOOK

A VQ-BASED FAST FACE RECOGNITION ALGORITHM USING OPTIMIZED C...

引用

6th international conference on Wavelet Analysis and pattern recognition

作者： Chen, Qiu Kotani, Koji Lee, Fei-Fei Ohmi, Tadahiro Tohoku Univ New Ind Creat Hatchery Ctr Aoba Ku Aza Aoba 6-6-10 Sendai Miyagi 9808579 Japan Tohoku Univ Grad Sch Engn Dept Elect Aoba Ku Sendai Miyagi 9808579 Japan

ISBN: (纸本)9781424422388

In this paper, we present a VQ-based fast face recognition algorithm using an optimized codebook. Previously, Chen et al. 1161 proposed a novel codebook design method based on the systematic classification and organization of code patterns abstracted from facial images for reliable face recognition. In this paper, an improved codebook design method is proposed. Combined by a systematically organized codebook based on the classification of code patterns and another codebook created by Kohonen's Self-Organizing Maps (SOM), an optimized codebook consisted of 2x2 codevectors for facial images is generated. We demonstrate the performance of our algorithm using publicly available AT&T database containing variations in lighting, posing, and expressions. Compared with the algorithms employing original codebook or SOM codebook separately, experimental results show face recognition using the optimized codebook is more efficient. the highest average recognition rate of 98.2% is obtained for 40 persons' 400 images of AT&T database. A table look-up (TLU) method is also proposed for the speed up of the recognition processing in this paper. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition.

关键词： Face recognition Vector quantization Codebook pattern classification SOM

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IMPROVED FORWARD FLOATING SELECTION ALGORIthM FOR FEATURE SUBSET SELECTION

IMPROVED FORWARD FLOATING SELECTION ALGORITHM FOR FEATURE SU...

引用

6th international conference on Wavelet Analysis and pattern recognition

作者： Nakariyakul, Songyot Casasent, David P. Thammasat Univ Dept Elect & Comp Engn 99 Moo 18 Phaholyothin RdAmpher Khlongluang Pathum Thani 12120 Thailand Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781424422388

We present results on two new databases for a new improved forward floating selection (IFFS) algorithm for selecting a subset of features. the algorithm is an improvement upon the state-of-the-art sequential forward floating selection algorithm that includes a new search strategy to check whether removing any feature in the selected feature set and adding a new one at each sequential step can improve the resultant feature set. We find that this method provides the optimal or quasi-optimal (close to optimal) solutions for many selected subsets and requires significantly less computational load than an exhaustive search optimal feature selection algorithm. Our experimental results for two different databases demonstrate that our algorithm consistently selects better subsets than other quasi-optimal feature selection algorithms do.

关键词： Dimensionality reduction Feature selection Floating feature selection pattern recognition

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View-invariant human action detection using component-wise HMM of body parts

View-invariant human action detection using component-wise H...

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5th international conference on Articulated Motion and Deformable Objects, AMDO 2008

作者： Chakraborty, Bhaskar Pedersoli, Marco Gonzàlez, Jordi Computer Vision Center Dept. de Ciències de la Computació Campus UAB Bellaterra 08193 Spain Llorens i Artigas 4-6 Barcelona 08028 Spain

ISBN: (纸本)3540705163

this paper presents a framework for view-invariant action recognition in image sequences. Feature-based human detection becomes extremely challenging when the agent is being observed from different viewpoints. Besides, similar actions, such as walking and jogging, are hardly distinguishable by considering the human body as a whole. In this work, we have developed a system which detects human body parts under different views and recognize similar actions by learning temporal changes of detected body part components. Firstly, human body part detection is achieved to find separately three components of the human body, namely the head, legs and arms. We incorporate a number of sub-classifiers, each for a specific range of view-point, to detect those body parts. Subsequently, we have extended this approach to distinguish and recognise actions like walking and jogging based on component-wise HMM learning. © 2008 Springer-Verlag.

关键词： Artificial intelligence

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TWO-DIMENSIONAL LOCALITY SENSITIVE DISCRIMINANT ANALYSIS

TWO-DIMENSIONAL LOCALITY SENSITIVE DISCRIMINANT ANALYSIS

引用

6th international conference on Wavelet Analysis and pattern recognition

作者： Wei, Yantao Li, Hong Xia, Tian Huazhong Univ Sci & Technol Inst Pattern Recognit & Artifcial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Math & Stat Wuhan 430074 Peoples R China Univ Adelaide Dept Comp Sci Adelaide SA 5005 Australia

ISBN: (纸本)9781424422388

Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to rind the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images. Firstly, this paper proposes an algorithm named two-dimensional locality sensitive discriminant analysis (2DLSDA), which directly extracts the proper features from image matrices based on LSDA algorithm. And the experimental results on the ORL database show the effectiveness of the proposed algorithm. After that, 2DLSDA plus Fisherface, which was presented for the further dimensionality reduction, was compared with other dimention reduction methods, namely Eigenface, LSDA and 2DLSDA plus PCA. Experiments show that conducting Fisherface after 2DLSDA achieves high recognition accuracy.

关键词： Manifold learning Locality sensitive discriminant analysis Two-dimensional locality sensitive discriminant analysis Face recognition

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RESEARCH OF VISION recognition ON AUTO RACK GIRDERS BASED ON IMPROVED ART2 NEURAL NETWORK AND D-S EVIDENCE thEORY

RESEARCH OF VISION RECOGNITION ON AUTO RACK GIRDERS BASED ON...

引用

6th international conference on Wavelet Analysis and pattern recognition

作者： Han, Li-Qiang Wang, Hua Gao, Ji-Gang Zhang, Shuang Changchun Inst Technol Coll Mech Sci & Engn Changchun 130012 Peoples R China

ISBN: (纸本)9781424422388

For type's recognition of camion rack girders, this paper puts forward a pattern recognition method based on improved ART2 neural network and D-S evidence theory. Firstly, for collected auto rack girders top images, region is partitioned to 16 equal regions (4x4), which covers high-frequency wavelet coefficient with one-layer wavelet transform, and gained local variance of wavelet coefficient in every sub-region is used as a character template;in the same way, is partitioned to 16 sub-images (4x4), estimating numbers of gray value "1" in every sub-region, to gain the area character template. Secondly, data of two character templates are used as inputs of improved ART2 neural network, to gain data of joint weights of network, and the basal confidence m(2), m(2). Finally, gain the total confidence with composition rule of D-S evidence theory, according to the maximum of the total confidence, to recognize types of auto rack girders. Experiments indicate this algorithm may solve on-line vision recognition on hundreds of auto rack girders very well, and possesses advantage of more rapid, more precise and more reliable etc. Type's recognition of camion rack girders has a more broad application future and higher practicality, based on neural network and D-S evidence theory.

关键词： Wavelet coefficient pattern recognition Improved ART2 neural network D-S evidence theory Character template

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Analyzing a Discrete Model of Aplysia Central pattern Generator

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6th international conference on Computational Methods in Systems Biology

作者： Tiwari, Ashish Talcott, Carolyn SRI Int Menlo Pk CA 94025 USA

ISBN: (纸本)9783540885610

We present a discrete formal model of the central pattern generator (CPG) located in the buccal ganglia of Aplysia that is responsible for mediating the rhythmic movements of its foregut during feeding. Our starting point is the continuous dynamical model for pattern generation underlying fictive feeding in Aplysia proposed by Baxter et. al. [1]. the discrete model is obtained as a composition of discrete models of ten individual neurons in the CPG. the individual neurons are interconnected through excitatory and inhibitory synaptic connections and electric connections. We used Symbolic Analysis Laboratory (SAL) to formally build the model and analyzed it using the SAL model checkers. Using abstract discrete models of the individual neurons helps in understanding the buccal motor programs generated by the network in terms of the network connection topology. It also eliminates the need for detailed knowledge of the unknown parameters in the continuous model of Baxter et. al. [1].

关键词： Neurons

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IDENTIFICATION FOR ANIMAL FIBERS WIth ARTIFICIAL NEURAL NETWORK

IDENTIFICATION FOR ANIMAL FIBERS WITH ARTIFICIAL NEURAL NETW...

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6th international conference on Wavelet Analysis and pattern recognition

作者： Shi, Xian-Jun Yu, Wei-Dong Wuhan Univ Sci & Engn Coll Sci Wuhan 430073 Peoples R China

ISBN: (纸本)9781424422388

Scale pattern of animal fibers is different and that is a major reference distinguishing them from each other. Usually, there are four basic shape parameters, including fiber diameters, scale interval, scale perimeter and scale area, to be used for describing the cuticle scale pattern of animal fiber. In present paper, two kinds of animal fiber are checked up under light microscope with a magnification of 40xfor objective and their images are captured by a CCD camera fixed on the microscope. After using a series of image operators on them, the skeletonized binary images only having one pixel wide can be obtained. then, these basic shape parameters of scale are measured and the database composed of numerical data of four comparable indexes, including fiber diameter, scale interval, normalized scale perimeter and normalized scale area, are established. Finally, a multi-parameter neural network classifier, including four input nodes, five hidden nodes and two output nodes, are developed to classify the two kinds of animal fibers. Two sets of classification rules are applied to the classifier respectively and the simulation results show that whether rule 1 or 2, the neural network classifier can always distinguish cashmere from fine wool (70s) effectively and the average classification performance is higher than 90 percent.

关键词： threshold Morphological Manipulations Scale pattern BP Neural Network

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Pervasive Computing - 6th international conference, Pervasive 2008, Proceedings

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6th international conference on Pervasive Computing, Pervasive 2008

ISBN: (纸本)3540795758

the proceedings contain 18 papers. the topics discussed include: robust recognition of reading activity in transit using wearable electrooculography;cooperative techniques supporting sensor-based people-centric inferencing;an integrated platform for the management of mobile location-aware information systems;location conflict resolution with an ontology;evaluation and analysis of a common model for ubiquitous systems interoperability;a context-aware system that changes sensor combinations considering energy consumption;providing an integrated experience of networked media, devices, and services through end-user composition;gaming tourism: lessons from evaluating REXplorer, a pervasive game for tourists;opportunities for pervasive computing in chronic cancer care;and privacy protection for RFID with hidden subset identifiers.

关键词： Ubiquitous computing

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WAVELET SVM ENSEMBLE FOR pattern CLASSIFICATION WIth QUANTUM-INSPIRED EVOLUTIONARY ALGORIthM

WAVELET SVM ENSEMBLE FOR PATTERN CLASSIFICATION WITH QUANTUM...

引用

6th international conference on Wavelet Analysis and pattern recognition

作者： Luo, Zhi-Yong Zhang, Wen-Feng Ye, Bin-Yuan Cai, Lin-Qin Chongqing Univ Posts & Telecommun Coll Automat Chongqing 400065 Peoples R China Northwestern Polytech Univ Coll Automat Xian 710072 Peoples R China Guangdong Vocational Coll Mech & Elect Technol Dept Mech & Elect Engn Guangzhou Peoples R China

ISBN: (纸本)9781424422388

Based on quantum-inspired evolutionary algorithm (QEA), a novel approach of constructing multi-class least squares wavelet SVNI (LS-WSVM) ensemble classifiers is presented, regularization parameters and kernel parameters of LS-WSVM can be optimized. Quantum-inspired evolutionary optimization can get appropriate parameters of LS-WSVM with global search, so the LS-WSVM ensemble model with boosting for the multi-class classifiers is built. And then, classification is studied using single base LS-SVM and LS-SVM ensemble with wavelet and Gaussian kernel, respectively. the simulation results show that the approach for the multi-class LS-WSVM ensemble classifiers is effective, that can obtain the optimal parameters of LS-WSVM with global searching QEA, and improved LS-WSVM provides excellent precision for ensemble classification.

关键词： Least squares wavelet support vector machine (LS-WSVM) Quantum-inspired evolutionary algorithm (QEA) Parameter optimization LS-WSVM ensemble classifiers

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Driver's Cognitive Distraction Detection using AdaBoost on pattern recognition Basis

Driver's Cognitive Distraction Detection using AdaBoost on P...

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IEEE international conference on Vehicular Electronics and Safety

作者： Miyaji, Masahiro Danno, Mikio Kawanaka, Haruki Oguri, Koji Aichi Prefectural Univ Grad Sch Informat Sci & Technol Nagakute Aichi 4801198 Japan Toyota Info Technol Ctr Co Ltd Minato ku Tokyo 1070052 Japan

ISBN: (纸本)9781424423590

Detecting the mental and physical states which occur in a driver immediately before a traffic accident and then providing information to or warning the driver is an effective means of reducing traffic accidents. this study is focused on driver distraction, a state which can easily lead to traffic accidents, and reproduced this distraction in a driving simulator by providing conversation or arithmetic tasks to the subjects. Stereo cameras were used as the means to track subjects' eye and head movements. these movements were tracked and their standard deviations were set as classification features of pattern recognition, and the AdaBoost method was used to detect subject distraction. the interval between heart R-waves was also added as a classifier feature, in order to improve cognitive distraction detection performance. the results were then compared with the SVM method from the AIDE Project, which was carried out as part of the EU 6th Framework Programme.

关键词： Adaptive boosting

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