Recent Advances in Computer Vision: Theories and Applications (Studies in Computational Intelligence)
ISBN-10: 3030029999
ISBN-13: 9783030029999
Edition 版本: 1st ed. 2019
Released: 2019-02-04
Pages: 425 pages
Book Description
This book presents a collection of high-quality research by leading experts in computer vision and its applications. Each of the 16 chapters can be read independently and discusses the principles of a specific topic,reviews up-to-date techniques,presents outcomes,and highlights the challenges and future directions. As such the book explores the latest trends in fashion creative processes,facial features detection,visual odometry,transfer learning,face recognition,feature description,plankton and scene classification,video face alignment,video searching,and object segmentation. It is intended for postgraduate students,researchers,scholars and developers who are interested in computer vision and connected research disciplines,and is also suitable for senior undergraduate students who are taking advanced courses in related topics. However,it is also provides a valuable reference resource for practitioners from industry who want to keep abreast of recent developments in this dynamic,exciting and profitable research field.
Cover
Computer Vision for Supporting Fashion Creative Processes
Facial Features Detection and Localization
Advances and Trends in Video Face Alignment
Video Similarity Measurement and Search
Analysis and Evaluation of Keypoint Descriptors for lmage Matching
Feature Extraction of Color lmages Using Quaternion Moments
Face Recognition Using Exact Gaussian-Hermit Moments
Face Recognition with Discrete Orthogonal Moments
Content-Based Image Retrieval lUsing Multiresolution Feature Descriptors
Landmark Recognition: From Small-Scale to Large-Scale Retrieval
Ocean Ecosystems Plankton Classification
Boundary Detection of Echocardiographic lmages During Mitral Regurgitation
Motion Estimation Made Easy: Evolution and Trends in Visual Odometry
Deep Ear Recognition Pipeline
Scene Classification Using Transfer Learning
Hyperspectral lmage: Fundamentals and Advances[/erphpdown]
