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Conference on Medical Imaging, Parallel Processing of Images, and Optimization Techniques

作者： Xu, Tiantian Huang, Shuqin Ding, Mingyue Zhang, Songgeng Huazhong Univ Sci & Technol Coll Life Sci & Technol Dept Biomed Engn Key LabEduc Minist China Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Key LabEduc Minist China Wuhan 430074 Hubei Peoples R China Beijing Tsinghua R&D Ind Inst Beijing 100085 Peoples R China

ISBN: (纸本)9780819469533

In this paper, several image enhancement methods for B-model US image, including Linear Transformation, Histogram Equalization, Brightness Preserving Bi-histogram Equalization (BBHE)([2]), Minimum Brightness Error Bi-Histogram Equalization (MMBEBHE)([2]), and Fuzzy Enhancement, were compared each other. Based on the subjective evaluations from human vision and feature extraction of the ROI after the enhancement, the advantages and disadvantages of each method were found out. Furthermore, the best enhancement algorithm, fuzzy enhancement algorithm was applied to our study and its impact on the feature extraction was compared with or without the fuzzy enhancement.

关键词： B-model US enhancement fuzzy enhancement

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Combinatorial and statistical methods for part selection for object recognition

Combinatorial and statistical methods for part selection for...

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18th International Conference on pattern recognition (ICPR 2006)

作者： Zhao, Zhipeng Vashist, Akshay Elgammal, Ahmed MuchnikO, Ilya Kulikowski, Casimir Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA Rutgers State Univ Ctr Discrete Math & Theoret Comp Sci Piscataway NJ 08854 USA

In object recognition tasks, where images are represented as constellations of image patches, often many patches correspond to the cluttered background. In this paper, we present a two-stage method for selecting the image patches which characterize the target object class and are capable of discriminating between the positive images containing the target objects and the complementary negative images. The first stage uses a combinatorial optimization formulation on a weighted multipartite graph. The following stage is a statistical method for selecting discriminative patches from the positive images. Another contribution of this paper is the part-based probabilistic method for object recognition, which uses a common reference frame instead of reference patch to avoid possible occlusion problems. We also explore different feature representation using principal component analysis (PCA) and 2D PCA. The experiment demonstrates our approach has outperformed most of the other known methods on a popular benchmark dataset while approaching the best known results.

关键词： class recognition computer vision feature selection object detection pattern representation and modelling

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Human visual perception, gestalt principles and duality region-contour - Application to computer image analysis of human cornea endothelium

Human visual perception, gestalt principles and duality regi...

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2nd International Conference on computer Graphics Theory and Applications/2nd International Conference on computer vision Theory and Applications

作者： Gavet, Yarm Pinoli, Jean-Charles Thuret, Gilles Gain, Philippe Ecole Natl Super Mines Ctr Ingn & Sante F-42023 St Etienne France Fac Med St Etienne France

ISBN: (纸本)9789728865740

The human visual system is far more efficient than a computer to analyze images, especially when noise or poor acquisition process make the analysis impossible by lack of information. To mimic the human visual system, we develop algorithms based on the gestalt theory principles: proximity and good continuation. We also introduce the notion of mosaic that we reconstruct with those principles. Mosaics can be defined as geometry figures (squares, triangles), or issued from a contour detection system or a skeletonization process. The application presented here is the detection of cornea endothelial cells. They present a very geometric structure that give enough information for a non expert to be able to perform the same analysis as the ophthalmologist, that mainly consists on counting the cells and evaluating the cell density.

关键词： pattern recognition segmentation grouping cognitive and biologically inspired computer vision mathematical morphology medical image analysis computational geometry Gestalt theory Contour closure Mosaic reconstruction image analysis

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Audio fingerprinting: Combining computer vision & data stream processing

Audio fingerprinting: Combining computer vision & data strea...

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32nd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Baluja, Shumeet Covell, Michele Google Inc 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

ISBN: (纸本)1424407281

In this paper, we present Waveprint, a novel system for audio identification. Waveprint uses a combination of computer-vision techniques and large-scale-data-stream processing algorithms to create compact fingerprints of audio data that can be efficiently matched. The resulting system has excellent identification capabilities for small snippets of audio that have been degraded in a variety of manners, including competing noise, poor recording quality, and cell-phone playback. We measure the tradeoffs between performance, memory usage, and computation through extensive experimentation. The system is more efficient in terms of memory usage and computation, while being more accurate, when compared with previous state of the art systems.

关键词： acoustic applications acoustic signal processing pattern recognition music

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A vision-based architecture for intent recognition

A vision-based architecture for intent recognition

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3rd International Symposium on Visual Computing

作者： Tavakkoli, Alireza Kelley, Richard King, Christopher Nicolescu, Mircea Nicolescu, Monica Bebis, George Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

ISBN: (纸本)9783540768555

Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among multiple agents or detection of situations that can pose a particular threat. We propose an approach that allows a physical robot to detect the intentions of others based on experience acquired through its own sensory-motor abilities. It uses this experience while taking the perspective of the agent whose intent should be recognized. The robot's capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a non-parametric recursive modeling approach. Our intent recognition method uses a novel formulation of Hidden Markov Models (HMM's) designed to model a robot's experience and its interaction with the world while performing various actions.

关键词： pattern recognition

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A weight vector feature for 3D shape matching

A weight vector feature for 3D shape matching

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7th International Workshop on pattern recognition in Information Systems PRIS 2007;In Conjunction with ICEIS 2007

作者： Lu, Yingliang Kaneko, Kunihiko Makinouchi, Akifumi Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Department of Information and Network Engineering Kurume Institute of Technology Fukuoka Japan

ISBN: (纸本)9789728865931

In the present paper, we introduce a novel segment weight vector to matching 3D objects rapidly. We split a 3D object into parts according to its topology, and make up the extracted the thickness feature of each part as a feature vector of the 3D object. Furthermore, we present a new solution for improving the accuracy of the similarity queries. Since the proposed method is based on partial features, it is particularly suited to searching objects having distinct part structures and is invariant to part architecture.

关键词： computer vision

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

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关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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A boosting discriminative model for moving cast shadow detection

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6th International Conference on Energy Minimization Methods in computer vision and pattern recognition, EMMCVPR 2007

作者： Yufei, Zha Ying, Chu Duyan, Bi Signal and Information Processing Lab. Engineering College of Air Force Engineering University xi'an China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology China

ISBN: (纸本)9783540741954

Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a boosting discriminative model for moving cast shadow detection. Firstly, color invariance subspace and texture invariance subspace are obtained by the color and texture difference between current image and background image;then, boosting is selected based on theses subspaces to discriminate cast shadow from moving objects;finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields for accurate image segmentation through graph cut. Results show that the proposed method excels classical method both in indoor and outdoor scene. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Image analysis

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pattern recognition and Machine Intelligence: Second International Conference, PReMI 2007, Proceedings

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2nd International Conference on pattern recognition and Machine Intelligence, PReMI 2007

ISBN: (纸本)3540770453

The proceedings contain 82 papers. The topics discussed include: ensemble approached of support vector machines for multiclass classification;weighted k-nearest leader classifier for large data sets;hybrid approaches for clustering;recognizing patterns of dynamic behaviors based on multiple relations in soccer robotics domain;a adaptive algorithm for failure recovery during dynamic service composition;fault diagnosis using dynamic time warping;a multiscale change detection technique robust to registration noise;image quality assessment based on perceptual structural similarity;topology adaptive active membrane;bit plane encoding and encryption;segmenting multiple textured objects using geodesic active contour and DWT;projection onto convex sets with watermarking for error concealment;and automatic guidance of a tractor using computer vision.

关键词： pattern recognition

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Dimensionless monocular SLAM

Dimensionless monocular SLAM

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3rd Iberian Conference on pattern recognition and Image Analysis

作者： Civera, Javier Davison, Andrew J. Montiel, J. M. M. Univ Zaragoza Dpto Informat & Ingn Sistemas Zaragoza Spain Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

ISBN: (纸本)9783540728481

It has recently been demonstrated that the fundamental computer vision problem of structure from motion with a single camera can be tackled using the sequential, probabilistic methodology of monocular SLAM (Simultaneous Localisation and Mapping). A key part of this approach is to use the priors available on camera motion and scene structure to aid robust real-time tracking and ultimately enable metric motion and scene reconstruction. In particular, a scene object of known size is normally used to initialise tracking. In this paper we show that real-time monocular SLAM can be initialised with no prior knowledge of scene objects within the context of a powerful new dimensionless understanding and parameterisation of the problem. When a single camera moves through a scene with no extra sensing, the scale of the whole motion and map is not observable, but we show that up-to-scale quantities can be robustly estimated. Further we describe how the monocular SLAM state vector can be partitioned into two parts: a dimensionless part, representing up-to-scale scene and camera motion geometry, and an extra metric parameter representing scale. The dimensionless parameterisation permits tuning of the probabilistic SLAM filter in terms of image values, without any assumptions about scene scale, but scale information can be put back into the estimation if it becomes available. Experimental results with real image sequences showing SLAM without an initialisation object, different image tuning examples and scenes with the same underlying dimensionless geometry are presented.

关键词： computer vision

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