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Pattern Recognition Applications and Methods [electronic resource] :Third International Conference, ICPRAM 2014, Angers, France, March 6-8, 2014, Revised Selected Papers /

Contributor(s): Fred, Ana [editor.] | De Marsico, Maria [editor.] | Tabbone, Antoine [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 9443Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015.Edition: 1st ed. 2015.Description: XVI, 279 p. 136 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319255309.Subject(s): Computer science | Artificial intelligence | Computer simulation | Computer graphics | Image processing | Pattern recognition | Computational intelligence | Computer Science | Pattern Recognition | Image Processing and Computer Vision | Artificial Intelligence (incl. Robotics) | Computer Graphics | Simulation and Modeling | Computational IntelligenceOnline resources: Click here to access online
Contents:
Multiple Image Segmentation -- Aggregation of Biclustering Solutions for Ensemble Approach -- Fuzzy C-Means Stereo Segmentation -- SCHOG Feature for Pedestrian Detection -- Learning Prior Bias in Classifier -- Intra-class Variance among Multiple Samples of the Same Person’s Fingerprint in a Cooperative User Scenario -- Kernel Matrix Completion for Learning Nearly Consensus Support Vector Machines -- An Empirical Comparison of Support Vector Machines versus Nearest Neighbour Methods for Machine Learning Applications -- Improving the Detection of Relations between Objects in an Image using Textual Semantics -- A ToF-based Approach to 3D Reconstruction of Isometric Surfaces -- Segmentation of Tomatoes in Open Field Images with Shape and Temporal Constraints -- Fast and Accurate Pedestrian Detection in a Truck’s Blind Spot Camera -- Comparing Different Labeling Strategies in Anomalous Power Consumptions Detection -- An Efficient Shape Feature Extraction, Description and Matching Method using GPU -- Utilization of Multiple Sequence Analyzers for Bibliographic Information Extraction.
In: Springer eBooksSummary: This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Pattern Recognition, ICPRAM 2014, held in Angers, France, in March 2014. The 18 revised full papers were carefully reviewed and selected from 179 submissions and describe up-to-date applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods.
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Multiple Image Segmentation -- Aggregation of Biclustering Solutions for Ensemble Approach -- Fuzzy C-Means Stereo Segmentation -- SCHOG Feature for Pedestrian Detection -- Learning Prior Bias in Classifier -- Intra-class Variance among Multiple Samples of the Same Person’s Fingerprint in a Cooperative User Scenario -- Kernel Matrix Completion for Learning Nearly Consensus Support Vector Machines -- An Empirical Comparison of Support Vector Machines versus Nearest Neighbour Methods for Machine Learning Applications -- Improving the Detection of Relations between Objects in an Image using Textual Semantics -- A ToF-based Approach to 3D Reconstruction of Isometric Surfaces -- Segmentation of Tomatoes in Open Field Images with Shape and Temporal Constraints -- Fast and Accurate Pedestrian Detection in a Truck’s Blind Spot Camera -- Comparing Different Labeling Strategies in Anomalous Power Consumptions Detection -- An Efficient Shape Feature Extraction, Description and Matching Method using GPU -- Utilization of Multiple Sequence Analyzers for Bibliographic Information Extraction.

This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Pattern Recognition, ICPRAM 2014, held in Angers, France, in March 2014. The 18 revised full papers were carefully reviewed and selected from 179 submissions and describe up-to-date applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods.

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