Progress in Pattern Recognition, Image Analysis and Applications 模式識別、圖像分析與應(yīng)用進展/會議錄

內(nèi)容概要

This book constitutes the refereed proceedings of the 9th Iberoamerican Congress on Pattern Recognition, CIARP 2004, held in Puebla, Mexico in October 2004.　　The 87 revised full papers presented were carefully reviewed and selected from 158 submissions. All current issues in pattern recognition, image analysis, and computer vision are addressed including applications in areas like robotics, entertainment, space exploration, data mining, document analysis, and natural language processing.

書籍目錄

Use of Context in Automatic Annotation of Sports VideosContent Based Retrieval of 3D DataClifford Geometric Algebra:A Promising Framework for Computer Vision, Robotics and Learning ..Adaptive Color Model for Figure-Ground Segmentation in Dynamic EnvironmentsReal-Time Infrared Object Tracking Based on Mean ShiftOptimal Positioning of Sensors in 2DComputer Vision Algorithms Versus Traditional Methods in Food Technology: The Desired CorrelationRadiance Function Estimation for Object ClassificationDetecting and Ranking Saliency for Scene DescriptionDecision Fusion for Object Detection and Tracking Using Mobile CamerasSelection of an Automated Morphological Gradient Threshold for Image SegmentationLocalization of Caption Texts in Natural Scenes Using a Wavelet TransformationA Depth Measurement System with the Active Vision of the Striped Lighting and Rotating MirrorFast Noncontinuous Path Phase-Unwrapping Algorithm Based on Gradients and MaskColor Active Contours for Tracking Roads in Natural Environments Generation of N-Parametric Appearance-Based Models Through Non-uniform SamplingGaze Detection by Wide and Narrow View Stereo CameraA New Auto-associative Memory Based on Lattice AlgebraImage Segmentation Using Morphological Watershed Applied to Cartography3D Object Surface Reconstruction Using Growing Self-organised NetworksSingle Layer Morphological Perceptron Solution to the N-Bit Parity ProblemRobust Self-organizing MapsExtended Associative Memories for Recalling Gray Level PatternsNew Associative Memories to Recall Real-Valued Patterns……Author Index

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