Image Feature Detectors and Descriptors (eBook, PDF)

Foundations and Applications
Redaktion: Awad, Ali Ismail; Hassaballah, Mahmoud

• Format: PDF

Produktbeschreibung

This book provides readers with a selection of high-quality chapters that cover both theoretical concepts and practical applications of image feature detectors and descriptors. It serves as reference for researchers and practitioners by featuring survey chapters and research contributions on image feature detectors and descriptors. Additionally, it emphasizes several keywords in both theoretical and practical aspects of image feature extraction. The keywords include acceleration of feature detection and extraction, hardware implantations, image segmentation, evolutionary algorithm, ordinal measures, as well as visual speech recognition.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 438
• Erscheinungstermin: 22. Februar 2016
• Englisch
• ISBN-13: 9783319288543
• Artikelnr.: 44897919

Inhaltsangabe

Introduction.- Image Features Detection, Description and Matching.- A Review of Image Interest Point Detectors: From Algorithms to FPGA Hardware Implementations.- Image Features Extraction, Selection and Fusion for Computer Vision.- Image Feature Extraction Acceleration.- Satellite Image Registration: A Comparative Study Using Invariant Local Features.- Redundancy Elimination in Video Summarization.- A Real Time Dactylology Based Selective Image Encryption Using Speeded Up Robust Features Extraction Technique and Artificial Neural Network.- Spectral Reflectance Images and Applications.- Image Segmentation using an Evolutionary Method based on Allostatic Mechanisms.- Image Analysis and Coding Based on Ordinal Data Representation.- Intelligent Detection of Foveal Zone from Colored Fundus Images of Human Retina through a Robust Combination of Fuzzy-Logic and Active Contour Model.- Registration of Digital Terrain Images using Nondegenerate Singular Points.- Visual Speech Recognition with Selected Boundary Descriptors.- Application of Texture Features for Classification of Primary Benign and Primary Malignant Focal Liver Lesions.- Application of Statistical Texture Features for Breast Tissue Density Classification.