Series in Machine Perception and Artificial Intelligence - Vol. 50
EMPIRICAL EVALUATION METHODS IN COMPUTER VISION
edited by Henrik I Christensen & P Jonathon Phillips (Royal Institute of Technology, Stockholm, Sweden)
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.
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.
| 172pp |
Pub. date: May 2002 |
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