Empirical Evaluation Methods in Computer VisioneBook - 2002
This book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the performance of medical image processing algorithms. Sample Chapter(s). Foreword (228 KB). Chapter 1: Introduction (505 KB). Contents: Automated Performance Evaluation of Range Image Segmentation Algorithms; Training/Test Data Partitioning for Empirical Performance Evaluation; Analyzing PCA-Based Face Recognition Algorithms: Eigenvector Selection and Distance Measures; Design of a Visual System for Detecting Natural Events by the Use of an Independent Visual Estimate: A Human Fall Detector; Task-Based Evaluation of Image Filtering Within a Class of Geometry-Driven-Diffusion Algorithms; A Comparative Analysis of Cross-Correlation Matching Algorithms Using a Pyramidal Resolution Approach; Performance Evaluation of Medical Image Processing Algorithms. Readership: Students and researchers in computer vision.
Publisher: River Edge : World Scientific Publishing Company, 2002
Copyright Date: ©2002
Branch Call Number: Electronic book
Characteristics: 1 online resource (170 pages)