LEADER 05149nam 22006254a 450 001 9910784783003321 005 20230607221448.0 010 $a981-277-742-3 035 $a(CKB)1000000000400604 035 $a(EBL)1679481 035 $a(OCoLC)879023551 035 $a(SSID)ssj0000146225 035 $a(PQKBManifestationID)11147785 035 $a(PQKBTitleCode)TC0000146225 035 $a(PQKBWorkID)10182481 035 $a(PQKB)10508712 035 $a(MiAaPQ)EBC1679481 035 $a(WSP)00004965 035 $a(Au-PeEL)EBL1679481 035 $a(CaPaEBR)ebr10201187 035 $a(CaONFJC)MIL505408 035 $a(EXLCZ)991000000000400604 100 $a20020920d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 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 $aThis book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the performance of medical image processing algorithms. Sample Chapter(s)
Foreword (228 KB)
Chapter 1: Introduction (505 KB)
< 410 0$aSeries in machine perception and artificial intelligence ;$vv. 50. 606 $aComputer vision$xEvaluation$vCongresses 615 0$aComputer vision$xEvaluation 676 $a006.3/7 701 $aChristensen$b H. I$g(Henrik I.),$f1962-$0845483 701 $aPhillips$b P. Jonathon$01117491 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784783003321 996 $aEmpirical evaluation methods in computer vision$93854246 997 $aUNINA