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101 0 $aeng
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183 $acr
200 00$aEmpirical evaluation methods in computer vision$b[electronic resource] /$feditors, Henrik I. Christensen, P. Jonathon Phillips
210 $aRiver Edge, N.J. $cWorld Scientific$dc2002
215 $a1 online resource (172 p.)
225 1 $aSeries in machine perception and artificial intelligence ;$vv. 50
300 $aAll but two contributions are revised papers from a workshop held in 2000.
311 $a981-02-4953-5
320 $aIncludes bibliographical references.
327 $aContents ; 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
327 $a1.6. 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
327 $a2.3. 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
327 $a3.3.1. 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
327 $a3.4.3. 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
327 $a4.1. Introduction
330 $aThis book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the performance of medical image processing algorithms. Sample Chapter(s)
Foreword (228 KB)
Chapter 1: Introduction (505 KB)
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410 0$aSeries in machine perception and artificial intelligence ;$vv. 50.
606 $aComputer vision$xEvaluation$vCongresses
608 $aElectronic books.
615 0$aComputer vision$xEvaluation
676 $a006.3/7
701 $aChristensen$b H. I$g(Henrik I.),$f1962-$0845483
701 $aPhillips$b P. Jonathon$0891083
801 0$bMiAaPQ
801 1$bMiAaPQ
801 2$bMiAaPQ
906 $aBOOK
912 $a9910458199803321
996 $aEmpirical evaluation methods in computer vision$91990351
997 $aUNINA