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 Evaluation -- 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 -- 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 -- 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 -- 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 -- 4.1. Introduction -- 4.2. Approach -- 4.3. Data Collection -- 4.4. Velocity Estimation -- 4.4.1. Colour Segmentation and Velocity Estimation -- 4.4.2. IR Velocity Estimation -- 4.4.3. Velocity Correlation -- 4.4.4. Data combination -- 4.4.5. Conclusions -- 4.5. Neural Network Fall Detector -- 4.5.1. Data Preparation Network Design and Training -- 4.5.2. Testing -- 4.6. Conclusions -- References.

Chapter 5 Task-Based Evaluation of Image Filtering within a Class of Geometry-Driven-Diffusion Algorithms -- 5.1. Introduction -- 5.2. Nonlinear Geometry-Driven Diffusion Methods of Image Filtering -- 5.3. Diffusion-Like Ideal Filtering of a Noise Corrupted Piecewise Constant Image Phantom -- 5.4. Stochastic Model of the Piecewise Constant Image Phantom Corrupted by Gaussian Noise -- 5.5. Estimates of Probability Distribution Parameters for Characterization of Filtering Results -- 5.6. Implementation results -- 5.7. Conclusions -- References -- Chapter 6 A Comparative Analysis of Cross-Correlation Matching Algorithms Using a Pyramidal Resolution Approach -- 6.1. Introduction -- 6.2. Area Based Matching Algorithms -- 6.3. Cross-Correlation Algorithms -- 6.4. Pyramidal Processing Scheme -- 6.4.1. Number of Layers -- 6.4.2. Decimation Function -- 6.4.3. Matching Process -- 6.4.4. Interpolation -- 6.4.5. Disparity Maps -- 6.5. Experimental Results -- 6.5.1. Experiment Layout -- 6.5.2. Disparity Maps -- 6.5.3. Disparity Error -- 6.5.4. Computational Load -- 6.6. Conclusion -- References -- Chapter 7 Performance Evaluation of Medical Image Processing Algorithms -- 7.1. Introduction -- 7.2. Presentations -- 7.2.1. New NCI Initiatives in Computer-Aided Diagnosis -- 7.2.2. Performance Characterization of Image and Video Analysis Systems at Siemens Corporate Research -- 7.2.3. Validating Registration Algorithms: A Case Study -- 7.2.4. Performance Evaluation of Image Processing Algorithms in Medicine: A Clinical Perspective -- 7.2.5. Performance Evaluation: Points for Discussion -- 7.3. Panel Discussion -- References.