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20240909213021.0 |
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m o d |
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140501s2002 si ob 000 0 eng d |
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|a MHW
|b eng
|e pn
|c MHW
|d EBLCP
|d OCLCO
|d OCLCQ
|d NLE
|d OCLCQ
|d ZCU
|d MERUC
|d U3W
|d OCLCF
|d ICG
|d INT
|d AU@
|d OCLCQ
|d DKC
|d OCLCQ
|d LEAUB
|d OCLCQ
|d OCLCO
|d OCLCQ
|d OCLCO
|d OCLCL
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| 019 |
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|a 859886768
|a 1086412259
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|a 9789812777423
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|a 9812777423
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|z 9810249535
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|z 9789810249533
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|a (OCoLC)879023551
|z (OCoLC)859886768
|z (OCoLC)1086412259
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| 050 |
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4 |
|a TA1634
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| 049 |
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|a HCDD
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| 100 |
1 |
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|a Christensen, Henrik I.
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| 245 |
1 |
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|a Empirical Evaluation Methods in Computer Vision.
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| 260 |
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|a Singapore :
|b World Scientific Publishing Company,
|c 2002.
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| 300 |
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|a 1 online resource (172 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Series in Machine Perception and Artificial Intelligence
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| 588 |
0 |
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|a Print version record.
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| 520 |
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|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.
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| 504 |
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|a Includes bibliographical references.
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| 505 |
0 |
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|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.
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| 505 |
8 |
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|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.
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| 505 |
8 |
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|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.
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| 505 |
8 |
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|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.
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| 505 |
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|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.
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| 650 |
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|a Computer vision
|x Evaluation
|v Congresses.
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| 655 |
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7 |
|a Conference papers and proceedings
|2 fast
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| 700 |
1 |
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|a Phillips, P. Jonathon.
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| 758 |
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|i has work:
|a Empirical evaluation methods in computer vision (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGVb6J9T6jWBkYDW9FYhQC
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|z 9789810249533
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| 830 |
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0 |
|a Series in machine perception and artificial intelligence.
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| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=1679481
|y Click for online access
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| 903 |
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|a EBC-AC
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| 994 |
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|a 92
|b HCD
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