Reset Filters
Filter Related Topics: 实习

Cover is for reference only

Empirical evaluation methods in computer vision /

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 expe...

Full description

Saved in:
Bibliographic Details
Corporate Authors: World Scientific (Firm)
Group Author: Christensen, H. I. (Henrik I.), 1962- (Editor); Phillips, P. Jonathon. (Editor)
Published: World Scientific Pub. Co.,
Publisher Address: Singapore ; River Edge, N.J. :
Publication Dates: 2002.
Literature type: eBook
Language: English
Series: Series in machine perception and artificial intelligence ; v. 50
Subjects:
Computer vision > Evaluation > Congresses.
Electronic books.
Online Access: http://www.worldscientific.com/worldscibooks/10.1142/4965#t=toc
Summary: 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.
Carrier Form: 1 online resource (ix,159pages) : illustrations.
Bibliography: Includes bibliographical references.
ISBN: 9789812777423 (electronic bk.)
CLC: TP391.41-532
Contents: ch. 1. Automated performance evaluation of range image segmentation algorithms. 1.1. Introduction. 1.2. Scoring the segmented regions. 1.3. Segmentation performance curves. 1.4. Training of algorithm parameters. 1.5. Train-and-test performance evaluation. 1.6. Training stage. 1.7. Testing stage. 1.8. Summary and discussion -- ch. 2. Training/test data partitioning for empirical performance evaluation. 2.1. Introduction. 2.2. Formal problem definition. 2.3. Genetic search algorithm. 2.4. A testbed. 2.5. Experimental results. 2.6. Conclusions -- ch. 3. Analyzing PCA-based face recognition algorithms: eigenvector selection and distance measures. 3.1. Introduction. 3.2. The FERET database. 3.3. Distance measures. 3.4. Selecting eigenvectors. 3.5. Conclusion -- ch. 4. Design of a visual system for detecting natural events by the use of an independent visual estimate: a human fall detector. 4.1. Introduction. 4.2. Approach. 4.3. Data collection. 4.4. Velocity estimation. 4.5. Neural network fall detector. 4.6. Conclusions -- ch. 5. Task-based evaluation of image filtering within a class of geometry-driven-diffusion algorithms. 5.1. Introduction. 5.2. Nonlinear geometry-driven diffusion methods of image filtering. 5.3. Diffusion-like ideal filtering of a noise corrupted piecewise constant image phantom. 5.4. Stochastic model of the piecewise constant image phantom corrupted by Gaussian noise. 5.5. Estimates of probabihty distribution parameters for characterization of filtering results. 5.6. Implementation results. 5.7. Conclusions -- ch. 6. A comparative analysis of cross-correlation matching algorithms using a pyramidal resolution approach. 6.1. Introduction. 6.2. Area based matching algorithms. 6.3. Cross-correlation algorithms. 6.4. Pyramidal processing scheme. 6.5. Experimental results. 6.6. Conclusion -- ch. 7. Performance evaluation of medical image processing algorithms. 7.1. Introduction. 7.2. Presentations. 7.3. Panel discussion.

Related Book Recommendations