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Pattern Recognition Applications and Methods: 6th International Conference, ICPRAM 2017, Porto, Portugal, February 24-26, 2017, Revised Selected Papers

Pattern Recognition Applications and Methods: 6th International Conference, ICPRAM 2017, Porto, Portugal, February 24-26, 2017, Revised Selected Papers

Publisher Springer, Berlin
Year
Pages 235
Version paperback
Language English
ISBN 9783319936468
Categories Pattern recognition
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Book description

This book contains revised and extended versions of selected papers from the 6th International Conference on Pattern Recognition, ICPRAM 2017, held in Porto, Portugal, in February 2017.

The 13 full papers presented were carefully reviewed and selected from 139 initial submissions. They aim at making visible and understandable the relevant trends of current research on pattern recognition.

Pattern Recognition Applications and Methods: 6th International Conference, ICPRAM 2017, Porto, Portugal, February 24-26, 2017, Revised Selected Papers

Table of contents

Control Variates as a Variance Reduction Technique for Random Projections.- Graph Classification with Mapping Distance Graph Kernels.- Domain Adaptation Transfer Learning by Kernel Representation Adaptation.- Optimal Linear Imputation with a Convergence Guarantee.- Condensing Deep Fisher Vectors: To Choose or to Compress?.- Emotion Recognition using Neighborhood Components Analysis and ECG/HRV-based Features.- A Conversive Hidden non-Markovian Model Approach for 2D and 3D Online Movement Trajectory Verification.- Prediction of User Interest by Predicting Product Text Reviews.- Blood Vessel Delineation in Endoscopic Images With Deep Learning.- Semi-Automated Testing of an Architectural Floor Plan Retrieval Framework: Quantitative and Qualitative Comparison of Semantic Pattern-based Matching Approaches.- Characterization of a Virtual Glove for Hand Rehabilitation based on Orthogonal LEAP Controllers.- Congestion Analysis Across Locations based on Wi-Fi Signal Sensing.- Text Line Segmentation in Handwritten Documents based on Connected Components Trajectory Generation.

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