Handbook of Pattern Recognition and Computer Vision. Группа авторов
Программы.
Handbook of Pattern Recognition and Computer Vision
Год выпуска 0
isbn 9789811211089
Автор произведения Группа авторов
Жанр Программы
Издательство Ingram
Written by world-renowned authors, this unique compendium presents the most updated progress in pattern recognition and computer vision (PRCV), fully reflecting the strong international research interests in the artificial intelligence arena. Machine learning has been the key to current developments in PRCV. This useful comprehensive volume complements the previous five editions of the book. It places great emphasis on the use of deep learning in many aspects of PRCV applications, not readily available in other reference text. Contents: Theory, Technology and Systems: A Brief Introduction to Part 1 (C H Chen) Optimal Statistical Classification (Edward R Dougherty, Jr and Lori Dalton) Deep Discriminative Feature Learning Method for Object Recognition (Weiwei Shi and Yihong Gong) Deep Learning Based Background Subtraction: A Systematic Survey (Jhony H Giraldo, Huu Ton Le, and Thierry Bouwmans) Similarity Domains Network for Modeling Shapes and Extracting Skeletons without Large Datasets (Sedat Ozer) On Curvelet-Based Texture Features for Pattern Classification (Reprinted from Chapter 1.7 of 5th HBPRCV) (Ching-Chung Li and Wen-Chyi Lin) An Overview of Efficient Deep Learning on Embedded Systems (Xianju Wang) Random Forest for Dissimilarity-Based Multi-View Learning (Simon Bernard, Hongliu Cao, Robert Sabourin, and Laurent Heutte) A Review of Image Colourisation (Bo Li, Yu-Kun Lai, and Paul L Rosin) Recent Progress of Deep learning for Speech Recognition (Jinyu Li and Dong Yu) Applications: A Brief Introduction to Part 2 (C H Chen) Machine Learning in Remote Sensing (Ronny Hänsch) Hyperspectral and Spatially Adaptive Unmixing for Analytical Reconstruction of Fraction Surfaces from Data with Corrupt Pixels (Fadi Kizel and Jon Atli Benediktsson) Image Processing for Sea Ice Parameter Identification from Visual Images (Qin Zhang) Applications of Deep Learning to Brain Segmentation and Labeling of MRI Brain Structures (Evan Fletcher and Alexander Knaack) Automatic Segmentation of IVUS Images Based on Temporal Texture Analysis (A Gangidi and C H Chen) Deep Learning for Historical Document Analysis (Foteini Simistira Liwicki and Marcus Liwicki) Signature Verification via Graph-Based Methods (Paul Maergner, Kaspar Riesen, Rolf Ingold, and Andreas Fischer) Cellular Neural Network for Seismic Pattern Recognition (Kou-Yuan Huang and Wen-Hsuan Hsieh) Incorporating Facial Attributes in Cross-modal Face Verification and Synthesis (Hadi Kazemi, Seyed Mehdi Iranmanesh, and Nasser M Nasrabadi) Connected and Autonomous Vehicles in the Deep Learning Era: A Case Study on Computer-Guided Steering (Rodolfo Valientea, Mahdi Zamana, Yaser P Fallaha, and Sedat Ozer) Readership: Graduate students, academics, practitioners, researchers, computer scientists, electrical and medical engineers.Deep Learning;Statistical Pattern Recognition;Random Forest Classification;Machine Learning;Image Colorization;Document Analysis;Seismic Recognition;Face Recognition;Remote Sensing;Medical Imaging00