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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Christensen, Henrik I.
Otros Autores: Phillips, P. Jonathon
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore : World Scientific Publishing Company, 2002.
Colección:Series in machine perception and artificial intelligence.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Christensen, Henrik I. 
245 1 0 |a Empirical Evaluation Methods in Computer Vision. 
260 |a Singapore :  |b World Scientific Publishing Company,  |c 2002. 
300 |a 1 online resource (172 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Series in Machine Perception and Artificial Intelligence 
588 0 |a Print version record. 
520 |a 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: Automate. 
504 |a Includes bibliographical references. 
505 0 |a 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 Evaluation. 
505 8 |a 1.6. 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 Complexity. 
505 8 |a 2.3. 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 Measures. 
505 8 |a 3.3.1. 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 Eigenvector. 
505 8 |a 3.4.3. 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 Detector. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Computer vision  |x Evaluation  |v Congresses. 
650 6 |a Vision par ordinateur  |x Évaluation  |v Congrès. 
655 7 |a Conference papers and proceedings  |2 fast 
700 1 |a Phillips, P. Jonathon. 
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830 0 |a Series in machine perception and artificial intelligence. 
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