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3-D object recognition based on SVM and stereo-vision: Application in endoscopic imaging

3-D object recognition based on SVM and stereo-vision: Appli...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Jad Ayoub Bertrand Granado Olivier Romain Yasser Mhanna Université de Cergy Pontoise France SYEL LIP6 Université Pierre et Marie Curie Paris France Department of Physics and Electronics Faculty of Sciences Lebanese University Beirut Lebanon

In this paper we focus on the recognition of threedimensional objects captured by an active stereo vision sensor. The study is related to our research project Cyclope, this embedded sensor based on active stereo-vision approach allows real time 3D objects reconstruction. Our medical application requires differentiation between hyperplastic and adenomatous polyps during 3D endoscopic imaging. The detection algorithm consists of SVM classifier trained on robust feature descriptors of a surfacic 3D point cloud extracted from the surface of studied object. We compared our feature extraction method with others. Experimental results were encouraging and show correct classification rate of approximately 97%. The work contains many techniques concerning image processing and system calibration and provides detailed statistics about the detection rate and the computing complexity.

关键词： Feature extraction Three dimensional displays Support vector machines Clouds pattern recognition Image reconstruction Kernel

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Real-time robotic hand control using hand gestures 2nd

Real-time robotic hand control using hand gestures

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2010 The 2nd international conference on Machine Learning and computing, ICMLC 2010

作者： Raheja, Jagdish Lal Shyam, Radhey Kumar, Umesh Prasad, P. Bhanu Pilani - 333031 Rajasthan India

ISBN: (纸本)9780769539775

This paper presents a new approach for controlling robotic hand or an individual robot by merely showing hand gestures in front of a camera. With the help of this technique one can pose a hand gesture in the vision range of a robot and corresponding to this notation, desired action is performed by the robotic system. Simple video camera is used for computer vision, which helps in monitoring gesture presentation. This approach consists of four modules: (a) A real time hand gesture formation monitor and gesture capture, (b) feature extraction, (c) pattern matching for gesture recognition, (d) Command determination corresponding to shown gesture and performing action by robotic system. Real-time hand tracking technique is used for object detection in the range of vision. If a hand gesture is shown for one second, the camera captures the gesture. Object of interest is extracted from the background and the portion of hand, representing the gesture, is cropped out using the statistical property of hand. Extracted hand gesture is matched with the stored database of hand gestures using pattern matching. Corresponding to the matched gesture, action is performed by the robot. © 2010 IEEE.

关键词： pattern matching

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Scene recognition using visual attention, invariant local features and visual landmarks

Scene recognition using visual attention, invariant local fe...

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2010 international conference on Image Processing, Computer Vision, and pattern recognition, IPCV 2010

作者： López-García, Fernando García-Díaz, Anton Fdez-Vidal, Xosé R. Pardo, Xosé M. Instituto de Automática e Informática Industrial Universidad Politécnica de Valencia Valencia Spain Dept. Electrónica e Computación Universidade de Santiago de Compostela Santiago de Compostela Spain

ISBN: (纸本)9781601321541

We consider the task of scene recognition, in the context of a robot-like navigation application, using a visual attention model of bottom-up saliency, invariant local features and visual landmarks, and the Nearest Neighbor rule for classification. Experimental work shows that important reductions in the number of prototypes used by the NN classifier can be achieved using saliency maps. We also present a novel approach to extract visual landmarks that uses the model of bottom-up saliency to localize interest points, and color centiles plus local binary pattern histograms to collect local description of them. In the experiments, this later approach outperforms SIFT features by achieving similar recognition results but further reductions in the size of the database of prototypes, thus providing bigger savings in computational costs.

关键词： Local binary pattern

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Sample constraint clustering and it's applications in pattern recognition

Sample constraint clustering and it's applications in patter...

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2010 7th international conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Xuan, Shibin Liu, Yiguang College of Computer Sichuan University Chengdu 610065 China College of Maths and Computer Science Guangxi University for Nationalities Nanning 530006 China

ISBN: (纸本)9781424459346

In the pattern recognition subspace method, the researcher has paid more attention to extract feature subspace, then expressed individual prototype with the training sample mean. Because the number of training sample is limited, there is certain difference between the sample mean and the individual prototype. In order to reduce this difference, a sample restraint clustering algorithm was proposed, which make up of it's clustering objective function with the Fisher criterion, and it's goal lies in minimizing within classes and maximizing between classes. The recurrence formula computing each cluster centroid is derived direct from the objective function. In the random produced sample space, the clustering experiment indicated the proposed method is able to reduce disparity between cluster centroid and the individual prototype. In the face recognition experiment, the positive recognition ratio of some algorithms may be improved when it's prototype is replaced with cluster centroid calculated by the proposed algorithm rather than the mean of training samples. ©2010 IEEE.

关键词： Clustering algorithms

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Discriminative Training of Subspace Gaussian Mixture Model for pattern Classification

Discriminative Training of Subspace Gaussian Mixture Model f...

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6th international conference on Intelligent computing

作者： Liu, Xiao-Hua Liu, Cheng-Lin Chinese Acad Sci NLPR Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9783642149214

The Gaussian mixture model (GMM) has been widely used in pattern recognition problems for clustering and probability density estimation For pattern classification however, the GMM has to consider two issues model structure in high-dimensional space and discriminative training for optimizing the decision boundary In this paper we propose a classification method using subspace GMM density model and discriminative training During discriminative training under the minimum classification error (MCE) criterion both the GMM parameters and the subspace parameters are optimized discriminatively Our experimental results on the MNIST handwritten digit data and UCI datasets demonstrate the superior classification performance of the proposed method

关键词： Subspace GMM EM algorithm Discriminative training MCE

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3D pulmonary embolism (PE) segmentation of CTA images using level sets

3D pulmonary embolism (PE) segmentation of CTA images using ...

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2010 international conference on Image Processing, Computer Vision, and pattern recognition, IPCV 2010

作者： Ebrahimdoost, Y. Dehmeshki, J. Ellis, T.J. Firoozbakht, M. Qanadli, S.D. Islamic Azad University Islamshahr Branch Islamshahr Tehran Iran Medical Imaging Research Group Faculty of Computing Information Systems and Mathematics Penrhyn Road Kingston Upon Thames Surry KT1 2EE United Kingdom Cardiovascular and International Radiology Department of Radiology University of Lausanne Switzerland

ISBN: (纸本)9781601321541

A primary task in 3D medical image segmentation is classifying voxels/pixels into an object or discrete blobs. This is used to create a 3D rendering for multiple objects and supports quantitative analysis for estimating density, size and other morphometric parameters. A pulmonary embolism (PE) is a sudden blockage in the pulmonary artery which has very serious consequence which can be fatal in some of cases. A major clinical challenge is to correctly diagnosis patients with PE. In this paper we use a level set algorithm for 3D segmentation of pulmonary embolism from CT image datasets. In order to have a desirable segmentation of pulmonary embolism, we first apply a morphological erosion operation to reduce the connectivity between the pulmonary embolism and its neighbourhood and then apply the level set method to segment the pulmonary embolism. The results show the capability of the methodology to segment a PE from the surrounding tissues in 3D.

关键词： Image segmentation

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Detecting Halftone Dots for Offset Print Quality Assessment Using soft computing

Detecting Halftone Dots for Offset Print Quality Assessment ...

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2010 IEEE World Congress on Computational Intelligence

作者： Lundstrom, Jens Verikas, Antanas Halmstad Univ Intelligent Syst Lab S-30118 Halmstad Sweden

ISBN: (纸本)9781424469208

Nowadays in printing industry most of information processing steps are highly automated, except the last one-print quality assessment and control. We present a way to assess one important aspect of print quality, namely the distortion of halftone dots printed colour pictures are made of. The problem is formulated as assessing the distortion of circles detected in microscale images of halftone dot areas. In this paper several known circle detection techniques are explored in terms of accuracy and robustness. We also present a new circle detection technique based on the fuzzy Hough transform (FHT) extended with k-means clustering for detecting positions of accumulator peaks and with an optional fine-tuning step implemented through unsupervised learning. Prior knowledge about the approximate positions and radii of the circles is utilized in the algorithm. Compared to FHT the proposed technique is shown to increase the estimation accuracy of the position and size of detected circles. The techniques are investigated using synthetic and natural images.

关键词： Hough transforms circle detection techniques fuzzy Hough transform fuzzy set theory halftone dots detection image colour analysis k-means clustering object detection offset print quality assessment pattern clustering print quality assessment print quality control printing industry printing industry quality control soft computing unsupervised learning unsupervised learning

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Structured local binary Haar pattern for graphics retrieval

Structured local binary Haar pattern for graphics retrieval

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2010 IEEE international conference on Intelligent computing and Intelligent Systems, ICIS 2010

作者： Su, Song-Zhi Chen, Shu-Yuan Li, Shang-An Li, Shao-Zi Duh, Der-Jyh Cognitive Science Department Xiamen University Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen China Department of Computer Engineering and Science Yuan-Ze University Taiwan Department of Computer Science and Information Engineering Ching-Yun University Taiwan

ISBN: (纸本)9781424465835

Feature extraction is an important issue in graphics retrieval. Local feature based descriptors are currently the predominate method used in image retrieval and object recognition. Inspired by the success of Haar feature and Local Binary pattern (LBP), a novel feature named structured local binary Haar pattern (SLBHP) is proposed for graphics retrieval in this paper. SLBHP encodes the polarity instead of the magnitude of the difference between accumulated gray values of adjacent rectangles. Experimental results on graphics retrieval show that the discriminative power of SLBHP is better than those of using edge points (EP), Haar feature, and LBP even in noisy condition. ©2010 IEEE.

关键词： Local binary pattern

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A discrete particle swarm optimization with random selection solution for the shortest path problem

A discrete particle swarm optimization with random selection...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Marina Yusoff Junaidah Ariffin Azlinah Mohamed Intelligent System Group Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam Selangor Darul Ehsan Malaysia Institute of Infrastructure Engineering & Sustainable Management Flood-Marine Excellence Centre Faculty of Civil Engineering Universiti Teknologi MARA Shah Alam Selangor Darul Ehsan Malaysia

This article proposes a discrete particle swarm optimization (DPSO) for solution of the shortest path problem (SPP). The proposed DPSO adopts a new solution mapping which incorporates a graph decomposition and random selection of priority value. The purpose of this mapping is to reduce the searching space of the particles, leading to a better solution. Detailed descriptions of the new solution and the DPSO algorithm are elaborated. Computational experiments involve an SPP dataset from previous research and road network from Malaysia. The DPSO is compared with a genetic algorithm (GA) using naive and new solution mapping. The results indicate that the proposed DPSO is highly competitive and shows good performance in both fitness value and processing time.

关键词： Gallium Convergence Particle swarm optimization Mathematical model Equations Roads pattern recognition

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MCIT'2010: international conference on Multimedia computing and Information Technology

MCIT'2010: International Conference on Multimedia Computing ...

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international conference on Multimedia computing and Information Technology, MCIT'2010

ISBN: (纸本)9781424470037

The proceedings contain 31 papers. The topics discussed include: extracting membership functions by ACS algorithm without specifying actual minimum support;application of TTCN-3 test language to testing information systems in eHealth domain;Gabor wavelet for road sign detection and recognition using a hybrid classifier;on the study of overhead reduction for confused document encrypting schemes;interactive applications for mobile TV;A user level Markov model for service based CRRM algorithm;multi-criteria decision making (MCDM) network selection model providing enhanced QoS differentiation to customers;content-aware selective reliability for DCCP video streaming;pattern matching approach towards real-time traffic sign recognition;vehicle detection using morphological image processing technique;a novel coloring framework for grayscale images;and anticipation the consumed electrical power in smart home using evolutionary algorithms.

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