HANDBOOK OF PATTERN RECOGNITION AND COMPUTER VISION (5TH EDITION)

Inbunden, Engelska, 2016

2 970 kr

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Beskrivning

Pattern recognition, image processing and computer vision are closely linked areas which have seen enormous progress in the last fifty years. Their applications in our daily life, commerce and industry are growing even more rapidly than theoretical advances. Hence, the need for a new handbook in pattern recognition and computer vision every five or six years as envisioned in 1990 is fully justified and valid.The book consists of three parts: (1) Pattern recognition methods and applications; (2) Computer vision and image processing; and (3) Systems, architecture and technology. This book is intended to capture the major developments in pattern recognition and computer vision though it is impossible to cover all topics.The chapters are written by experts from many countries, fully reflecting the strong international research interests in the areas. This fifth edition will complement the previous four editions of the book.

Produktinformation

Utgivningsdatum:2016-02-19
Mått:175 x 250 x 36 mm
Vikt:1 170 g
Format:Inbunden
Språk:Engelska
Antal sidor:584
Upplaga:5
Förlag:World Scientific Publishing Co Pte Ltd
ISBN:9789814656528

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Innehållsförteckning

Pattern Recognition Methods and Applications:Fundamental Methodological Issues of Syntactic Pattern Recognition (Mariusz Flasinski); Learning from Examples (Marco Loog); Pattern Recognition Using Integrated Deep Generative and Discriminative Models (Li Deng); Information Theoretic Clustering Using a K-nn Approach (Vidar Vikjord and Robert Jenssen); Rejection Mechanisms in Random Forests with Application to Medical Image Classification (Laurent Heutte); Recent Advances on Optimum-Path Forest for Data Classification: Supervised, Semi-Supervised and Unsupervised Learning (Joao Paulo Papa, Willian Paraguassu, Amorim, Alexandre Falcao, Joao Manuel and R S Tavares); On Curvelet-Based Texture Features for Pattern Classification (Ching-Chung Li and Wen-Chyi Lin); On Computer Recognition and Evaluation of Coins (C Y Suen et al.); Supervised and Unsupervised Feature Extraction Methods for Underwater Fish Species Recognition (Meng-Che Chuang, Jeng-Neng Hwang and Kresimir Willimans); Personalized Music Emotion Recognition Using Gaussian Mixture Model (Yi-Hsuan Yang, Ju-Chiang Wang, Alex Chen and Homer Chen); Computer Vision and Image Processing:Context Assisted Face Tagging for Images and Videos (Liyan Zhang and Sharad Mehrotra); A Review of Statistical Shapes (Alan Brunton, Timo Bolkart and Stefanie Wuhrer); Tracking without Appearance Descriptors (Mehrsan Javan Roshtkari and Martin D Levine); Knowledge Augmented Visual Learning (Q Ji); Graph Edit Distance - Novel Approximation Algorithms and Applications (Kaspar Riesen and Horst Bunke); Latest Developments of LSTM Neural Networks with Applications of Document Image Analysis (Marcus Liwicki, Volkmar Frinken and Zeshan Afzal); Analyzing Remote Sensing Images with Mathematical Morphology: From Classic Filtering to Advanced Self-Dual Attribute Profiles (Gabriele Cavallaro, Mauro Dalla Mura and Jon Atli Benediktsson); Manifold-Based Sparse Representation for Hyperspectral Image Classification (Y T Tang); A Review of Texture Analysis Methods and Their Applications in Medical Image Analysis of The Brain (R Maani, S Kalra and Y H Yang); 3D Tomosynthesis to Detect Breast Cancer (C Y Suen et al.); System, Architecture and Technology:Visual Object Recognition in Large Datasets (Sedat Ozer); Efficient Identification of Faces in Video Streams Using Low-Power Multi-Core Devices (D Prieur, E Grander, Y Savaria and C Thibeault); Shadow Modeling Based Upon Rayleigh Scattering and Mie Theory (Lin Gu and Antonio Robles Kelly); Tracking Using Structural Support-Vector Detectors and Robust Kalman Filtering (Eraldo Ribeiro); An Overview of Industrial Use of 3D Vision for Inspection (David Michael); Vision Challenges in Image-Based Barcode Readers (Xianju Wang and Xiangyun Ye); Kernel-Based Learning for Fault Detection and Identification in Fuel Cell Systems (Andrea De Giorgi, Gabriele Moser, Lissy Pellaco, Sebastiano B Serpico and Andrea Trucco);