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检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition"

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Entropy-Based Automatic Segmentation of Bones in Digital X-ray Images

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4th international conference on pattern recognition and Machine Intelligence (PReMI)

作者： Bandyopadhyay, Oishila Chanda, Bhabatosh Bhattacharya, Bhargab B. Camellia Inst Technol Dept CSE Kolkata 700129 India Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India

ISBN: (纸本)9783642217869

Bone image segmentation is an integral component of orthopedic Xray image analysis that aims at extracting the bone structure from the muscles and tissues. Automatic segmentation of the bone part in a digital X-ray image is a challenging problem because of its low contrast with the surrounding flesh, which itself needs to be discriminated against the background. The presence of noise and spurious edges further complicates the segmentation. In this paper, we propose an efficient entropy-based segmentation technique that integrates several simple steps, which are fully automated. Experiments on several X-ray images reveal encouraging results as evident from a segmentation entropy quantitative assessment (SEQA) metric [Hao, et al. 2009].

关键词： Entropy Digital X-ray LOCO Medical imaging Segmentation

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Driver recognition based on dynamic handgrip pattern on steeling wheel

Driver recognition based on dynamic handgrip pattern on stee...

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2011 12th ACIS international conference on software Engineering, Artificial Intelligence Networking and Parallel Distributed computing, SNPD 2011

作者： Chen, Ronghua She, Mary Fenghua Sun, Xiangping Kong, Lingxue Wu, Yuchuan Institute for Technology Research and Innovation Deakin University Geelong VIC Australia School of Mechanical Engineering and Automation Wuhan Textile University Wuhan China

ISBN: (纸本)9780769544755

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. © 2011 IEEE.

关键词： Automobile steering equipment

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2011 international conference on Multimedia computing and Systems, ICMCS'11

2011 International Conference on Multimedia Computing and Sy...

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2011 international conference on Multimedia computing and Systems, ICMCS'11

ISBN: (纸本)9781612847306

The proceedings contain 175 papers. The topics discussed include: Image browsers - effective and efficient tools for managing large image collections;fast block matching algorithms using frequency domain;a robust iris localization method of facial faces;induction generator rotor faults modeling and diagnosis based on double PQ transformation;HMM-based gait modeling and recognition under different walking scenarios;frequency assignment for cellular mobile systems using a hybrid Tabu search with an adaptive constraint satisfaction technique;a constraint programming based platform for planning assistance and schedule generation: case of small IT projects;eye state analysis using iris detection based on circular Hough transform;explaining how intelligent control has improved the way we live: a survey on the use of fuzzy logic controllers in daily human life;and an efficient new soft-decision decoding algorithm for binary cyclic codes.

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A Low-Order Model of Biological Neural Networks for Hierarchical or Temporal pattern Clustering, Detection and recognition

A Low-Order Model of Biological Neural Networks for Hierarch...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Lo, James Ting-Ho Univ Maryland Baltimore Cty Dept Math & Stat Baltimore MD 21250 USA

ISBN: (纸本)9781424496365

A low-order model (LOM) of biological neural networks, which is biologically plausible, is herein reported. LOM is a recurrent hierarchical network composed of novel models of dendritic trees for encoding information, spiking neurons for computing subjective probability distributions and generating spikes, nonspiking neurons for transmitting inhibitory graded signals to modulate their neighboring spiking neurons, unsupervised and supervised covariance learning and accumulation learning mechanisms, synapses, a maximal generalization scheme, and feedback connections with different delay durations. An LOM with a main network that learns without supervision and clusters similar patterns, and offshoot structures that learn with supervision and assign labels to clusters formed in the main network is proposed as a learning machine that learns and retrieves easily, generalizes maximally on corrupted, distorted and occluded temporal and spatial patterns, and utilizes fully the spatially and temporally associated information.

关键词： Biological information theory Biological neural networks Biological system modeling Covariance matrix Integrated circuit modeling LOM Machine learning Neurons accumulation learning mechanisms biological neural networks biology computing delay durations dendritic trees feedback connections learning (artificial intelligence) low order model neural nets pattern clustering probability probability distributions spiking neurons supervised covariance learning temporal pattern clustering

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Wideband transmit beampattern design for MIMO array

Wideband transmit beampattern design for MIMO array

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2011 IEEE international conference on Signal Processing, Communications and computing, ICSPCC 2011

作者： Jin, Yong Liu, Xian-Xing Huang, Jian-Guo Institute of Image Processing Pattern Recognition Henan University KaiFeng 475000 China College of Marine Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781457708947

Transmit beamforming design for Multiple Input Multiple Output (MIMO) array is studied. The performance of narrowband transmits beamforming design methods is detailed discussed through simulations. Aiming at wideband transmit beamforming design;suppose each sensor transmits at the same power level, three criteria namely conventional least square beampattern matching, minimum targets azimuth cross-correlation matching and minimum sidelobe matching are proposed to constrain the cross-relation matrix of wideband transmitting signals. Through equivalent transform, above optimum problems are described as Semi-Definite Quadratic Programming. Convex optimization techniques are used to find the numerical solution of the resulted optimization problems. Simulations show that our method is effective and promising. © 2011 IEEE.

关键词： Quadratic programming

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A principled discussion of information combination rules in different representation settings

A principled discussion of information combination rules in ...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Didier Dubois Weiru Liu Jianbing Ma Henri Prade Institut de Recherche en Informatique de Toulouse-IRIT Université de Toulouse Toulouse France School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast UK

Depending on the representation setting, different combination rules have been proposed for fusing information from distinct sources. Moreover in each setting, different sets of axioms that combination rules should satisfy have been advocated, thus justifying the existence of alternative rules (usually motivated by situations where the behavior of other rules was found unsatisfactory). These sets of axioms are usually purely considered in their own settings, without in-depth analysis of common properties essential for all the settings. This paper introduces core properties that, once properly instantiated, are meaningful in different representation settings ranging from logic to imprecise probabilities. The following representation settings are especially considered: classical set representation, possibility theory, and evidence theory which is rich enough to include as particular cases i) sets (when there is one focal element), ii) probabilities (when focal elements are singletons), and iii) possibilities (when focal elements are nested). This unified discussion of combination rules across different settings is expected to provide some fresh look on some old but basic issues in information fusion.

关键词： Merging Possibility theory Reliability Frequency modulation pattern recognition Distance measurement Uncertainty

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Action unit detection using sparse appearance descriptors in space-time video volumes

Action unit detection using sparse appearance descriptors in...

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IEEE international conference on Automatic Face and Gesture recognition

作者： Jiang, Bihan Valstar, Michel F. Pantic, Maja Department of Computing Imperial College London United Kingdom Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands

ISBN: (纸本)9781424491407

Recently developed appearance descriptors offer the opportunity for efficient and robust facial expression recognition. In this paper we investigate the merits of the family of local binary pattern descriptors for FACS Action-Unit (AU) detection. We compare Local Binary patterns (LBP) and Local Phase Quantisation (LPQ) for static AU analysis. To encode facial expression dynamics, we extend the purely spatial representation LPQ to a dynamic texture descriptor which we call Local Phase Quantisation from Three Orthogonal Planes (LPQ-TOP), and compare this with the Local Binary patterns from Three Orthogonal Planes (LBP-TOP). The efficiency of these descriptors is evaluated by a fully automatic AU detection system and tested on posed and spontaneous expression data collected from the MMI and SEMAINE databases. Results show that the systems based on LPQ achieve higher accuracy rate than those using LBP, and that the systems that utilise dynamic appearance descriptors outperform those that use static appearance descriptors. Overall, our proposed LPQ-TOP method outperformed all other tested methods. © 2011 IEEE.

关键词： Local binary pattern

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Extracting refrained phrases from music signals using a frequent episode pattern mining algorithm

Extracting refrained phrases from music signals using a freq...

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2011 IEEE international conference on Granular computing, GrC 2011

作者： Fujikawa, Jumpei Kida, Takuya Katoh, Takashi Graduate School of Information Science and Technology Hokkaido University Sapporo Japan

ISBN: (纸本)9781457703713

In this paper, we discuss a method for extracting refrained phrases from a music signal by a discrete knowledge discovery processing approach instead of a signal processing approach. The proposed method consists of two processes: translating a music signal into a sequence of events that represent pitch information, and then mining the frequent patterns from the event sequences. The former is performed by computing chroma vectors at every beat interval, and the latter is performed by enumerating the frequent episode patterns. We carried out a preliminary experiment on some pieces in the RWC music databases to examine if the extracted patterns represent the refrained phrases. © 2011 IEEE.

关键词： Data mining

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Proposing a CNN Based Architecture of Mid-level Vision for Feeding the WHERE and WHAT Pathways in the Brain

Proposing a CNN Based Architecture of Mid-level Vision for F...

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2nd Swarm, Evolutionary and Memetic computing conference (SEMCCO 2011)

作者： Das, Apurba Roy, Anirban Ghosh, Kuntal CDAC Kolkata India Techno India Kolkata India Indian Stat Inst Machine Intelligence Unit Kolkata India Indian Stat Inst Ctr Soft Comp Res Kolkata India

ISBN: (纸本)9783642271717

In the central visual pathway originating from the eye, a bridging is required between two hierarchical tasks, that of pixel based information recording by visual pathway at low level on one hand and that of object recognition at high level on the other. Such a bridge which may be designated as a mid-level block-grained integration has here been modeled by a multi-layer flexible cellular neural network (F-CNN). The proposed CNN architecture is validated by different intermediate level tasks involving rigid and deformable pattern recognition. Execution of such tasks by the proposed architecture, it has been shown, is capable of generating valid and significant inputs for the WHERE (dorsal) and WHAT (ventral) pathways in the brain. The model includes the proposal of a feedback (also by CNN architecture) to the lower mid-level from the higher mid-level dorsal and ventral pathways for flexible cell (physiological receptive field) size adjustment in the primary visual cortex towards successful 'where' and 'what' identifications for high-level vision.

关键词： Cellular Neural Network (CNN) dorsal and ventral pathways visual cortical column scale-space attention model

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Deep Convex Net: A Scalable Architecture for Speech pattern Classification

Deep Convex Net: A Scalable Architecture for Speech Pattern ...

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12th Annual conference of the international-Speech-Communication-Association 2011 (INTERSPEECH 2011)

作者： Deng, Li Yu, Dong Microsoft Res Redmond WA USA

ISBN: (纸本)9781618392701

We recently developed context-dependent DNN-HMM (Deep-Neural-Net/Hidden-Markov-Model) for large-vocabulary speech recognition. While achieving impressive recognition error rate reduction, we face the insurmountable problem of scalability in dealing with virtually unlimited amount of training data available nowadays. To overcome the scalability challenge, we have designed the deep convex network (DCN) architecture. The learning problem in DCN is convex within each module. Additional structure-exploited fine tuning further improves the quality of DCN. The full learning in DCN is batch-mode based instead of stochastic, naturally lending it amenable to parallel training that can be distributed over many machines. Experimental results on both MNIST and TIMIT tasks evaluated thus far demonstrate superior performance of DCN over the DBN (Deep Belief Network) counterpart that forms the basis of the DNN. The superiority is reflected not only in training scalability and CPU-only computation, but more importantly in classification accuracy in both tasks.

关键词： deep learning scalability convex optimization neural network deep belief network phone state classification batch-mode training parallel computing

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