In this paper, we study the problem of rotation invariant texture classifications. There are several methods in texture recognition problem, we compare three best known methods such us: Gabor wavelet filter, Local Bin...
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In this paper, we study the problem of rotation invariant texture classifications. There are several methods in texture recognition problem, we compare three best known methods such us: Gabor wavelet filter, Local Binary pattern operators (LBP) and co-occurrence matrix (GLCM). A multi-class Support Vector Machines (SVM) is used as a classifier. The three methods are evaluated based on two different databases: Brodatz and Outex to bring out a comparative study about the discrimination capabilities of those different families of texture classification methods. The experimental results show that some of the studied methods are more compatible with this classification problem than the others. The SVM classifier approve the running time of the algorithm of classification.
The proceedings contain 40 papers. The special focus in this conference is on Applied Reconfigurable computing. The topics include: Accurate floating point arithmetic through hardware error-free transformations;active...
ISBN:
(纸本)9783642194740
The proceedings contain 40 papers. The special focus in this conference is on Applied Reconfigurable computing. The topics include: Accurate floating point arithmetic through hardware error-free transformations;active storage networks for accelerating K-means data clustering;An FPGA implementation for texture analysis considering the real-time requirements of vision-based systems;CReAMS: An embedded multiprocessor platform;dataflow graph partitioning for optimal spatio-temporal computation on a coarse grain reconfigurable architecture;A pipeline interleaved heterogeneous SIMD soft processor array architecture for MIMO-OFDM detection;Design, implementation, and verification of an adaptable processor in lava HDL;Towards an adaptable multiple-ISA reconfigurable processor;FPGA-based cherenkov ring recognition in nuclear and particle physics experiments;biologically-Inspired massively-parallel architectures: A reconfigurable neural modelling platform;FPGA-based smith-waterman algorithm: Analysis and novel design;index to constant weight codeword converter;on-Chip Ego-Motion estimation based on optical flow;Comparison between heterogeneous mesh-based and tree-based application specific FPGA;Dynamic VDDswitching technique and mapping optimization in dynamically reconfigurable processor for efficient energy reduction;MEMS interleaving read operation of a holographic memory for optically reconfigurable gate arrays;FaRM: Fast reconfiguration manager for reducing reconfiguration time overhead on FPGA;feasibility analysis of reconfigurable computing in low-power wireless sensor applications;hierarchical optical flow estimation architecture using color cues;Magnetic Look-Up Table (MLUT) featuring radiation hardness, high performance and low power;a reconfigurable audio beamforming multi-core processor;reconfigurable stream-processing architecture for sparse linear solvers;The krawczyk algorithm: Rigorous bounds for linear equation solution on an FPGA;A dynamic reconfigurable CPL
This paper addresses gait recognition, the problem of identifying people by the way of their walk. The proposed system consists of a model-free approach which extracts features directly from the human silhouette. The ...
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This paper addresses gait recognition, the problem of identifying people by the way of their walk. The proposed system consists of a model-free approach which extracts features directly from the human silhouette. The dynamics of the gait are modeled using Hidden Markov Models. Experiments have been carried out on the CASIA dataset C consisting of 153 people under four walking scenarios: normal walking, slow walking, fast walking and walking while carrying a bag. The results obtained are promising and compare favorably with existing approaches.
This paper proposes a novel biometric authentication method based on the recognition of drivers' dynamic handgrip on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect hand...
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This paper proposes a novel biometric authentication method based on the recognition of drivers' dynamic handgrip on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect handgrip data exerted by the hands of drivers who intend to start the vehicle. Then, the likelihood-ratio-based classifier is designed to distinguish rightful driver of a car after analyzing their inherent dynamic features of grasping. The experimental results obtained in this study show that mean acceptance rates of 85.4% for the trained subjects and mean rejection rates of 82.65% for the un-trained ones are achieved by the classifier in the two batches of testing. It can be concluded that the driver verification approach based on dynamic handgrip recognition on steering wheel is a promising biometric technology and will be further explored in the near future in smart car design.
Proceedings oftheSixthinternationalconference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. This proceedings doesnt on...
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ISBN:
(纸本)9783642256578
Proceedings oftheSixthinternationalconference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. This proceedings doesnt only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. The book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, patternrecognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. The proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific research fields. Dr. Yinglin Wang is a professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; Dr. Tianrui Li is a professor at the School of Information Science and Technology, Southwest Jiaotong University, China.
The objective of this work is to propose a new template matching scheme which is able to deal with the recognition issue against rotation. The proposed scheme, rotation-invariant filter-driven template matching (RI-FT...
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The objective of this work is to propose a new template matching scheme which is able to deal with the recognition issue against rotation. The proposed scheme, rotation-invariant filter-driven template matching (RI-FTM), starts to transform a Cartesian-coordinate pattern to a polar-coordinate pattern. Subsequently, we put our emphasis on how to estimate an appropriate filter which is adopted to establish the connection between query pattern and reference pattern, and then the similarity between those two patterns is computed via sum of squared differences (SSD). In addition, the proposed method can shorten the width of filter to reduce the computing time. The experiment results will demonstrate our method is capable of recognitions of license plate characters and commercial logos.
Classification system and textural features play increasingly an important role in remotely sensed images classification and many patternrecognition applications. In this work, we propose to fuse the information outp...
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Classification system and textural features play increasingly an important role in remotely sensed images classification and many patternrecognition applications. In this work, we propose to fuse the information outputed by 3 well-known classifiers: Support Vector Machines (SVM), Neural Network (NN) and Parzen Window (PW). These classifiers were combined according to the Dempster-Shafer theory. The input textural feature used are selected according the GMMFS algorithm. The classification results show that the proposed method gives high performances than those of classifiers separately considered.
Texture can be considered as a repeating pattern of local variation of pixel intensities. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. One of the difficult...
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Texture can be considered as a repeating pattern of local variation of pixel intensities. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. One of the difficulties in texture classification was the lack of tools that characterize textures. Classification of textures has received attention during last few decades. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. Then DCT is applied on the gray level images to obtain DCT coefficient. These DCT coefficient are use to train the neural network. For classifying the images using DCT, two popular softcomputing techniques namely neurocomputing and neuro-fuzzy computing are used. A performance comparison was made among the softcomputing models for the texture classification problem. It is observed that the proposed neuro-fuzzy model performed better than the neural network.
This paper presents a method of estimation of facial expression intensity from a sequence of binary facial images obtained from video. The binarization has been done using a neuro-visual model of figure ground segrega...
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This paper presents a method of estimation of facial expression intensity from a sequence of binary facial images obtained from video. The binarization has been done using a neuro-visual model of figure ground segregation. The Local Binary pattern (LBP) is taken as characteristic feature of a face with expression. This pattern gets evolved in the temporal domain over the sequence. The dynamics of the pattern, starting from a neutral face, is characterised by Hausdorff distance. Back Propagation (BP) Neural Networks are trained to estimate the expression intensity level of the basic expressions.
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