05146nam 22006374a 450 991081014670332120240404142638.0981-277-742-3(CKB)1000000000400604(EBL)1679481(OCoLC)879023551(SSID)ssj0000146225(PQKBManifestationID)11147785(PQKBTitleCode)TC0000146225(PQKBWorkID)10182481(PQKB)10508712(MiAaPQ)EBC1679481(WSP)00004965(Au-PeEL)EBL1679481(CaPaEBR)ebr10201187(CaONFJC)MIL505408(EXLCZ)99100000000040060420020920d2002 uy 0engur|n|---|||||txtccrEmpirical evaluation methods in computer vision /editors, Henrik I. Christensen, P. Jonathon Phillips1st ed.River Edge, N.J. World Scientificc20021 online resource (172 p.)Series in machine perception and artificial intelligence ;v. 50All but two contributions are revised papers from a workshop held in 2000.981-02-4953-5 Includes bibliographical references.Contents ; Foreword ; Chapter 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 Evaluation1.6. Training Stage 1.7. Testing Stage ; 1.8. Summary and Discussion ; References ; Chapter 2 Training/Test Data Partitioning for Empirical Performance Evaluation ; 2.1. Introduction ; 2.2. Formal Problem Definition ; 2.2.1. Distance Function ; 2.2.2. Computational Complexity2.3. Genetic Search Algorithm 2.4. A Testbed ; 2.5. Experimental Results ; 2.6. Conclusions ; References ; Chapter 3 Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures ; 3.1. Introduction ; 3.2. The FERET Database ; 3.3. Distance Measures3.3.1. Adding Distance Measures 3.3.2. Distance Measure Aggregation ; 3.3.3. Correlating Distance Metrics ; 3.3.4. When Is a Difference Significant ; 3.4. Selecting Eigenvectors ; 3.4.1. Removing the Last Eigenvectors ; 3.4.2. Removing the First Eigenvector3.4.3. Eigenvalue Ordered by Like-Image Difference 3.4.4. Variation Associated with Different Test/Training Sets ; 3.5. Conclusion ; References ; Chapter 4 Design of a Visual System for Detecting Natural Events by the Use of an Independent Visual Estimate: A Human Fall Detector4.1. IntroductionThis 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. <i>Sample Chapter(s)</i><br>Foreword (228 KB)<br>Chapter 1: Introduction (505 KB)<br> <Series in machine perception and artificial intelligence ;v. 50.Computer visionEvaluationCongressesComputer visionEvaluation006.3/7Christensen H. I(Henrik I.),1962-845483Phillips P. Jonathon1117491MiAaPQMiAaPQMiAaPQBOOK9910810146703321Empirical evaluation methods in computer vision4118363UNINA