Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g. , recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gen erate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis.
Inhaltsverzeichnis
1. Introduction. - 1. Face Detection. - 2. Gesture Recognition. - 3. Book Overview. - 2. Detecting Faces in Still Images. - 1. Introduction. - 2. Detecting Faces In A Single Image. - 3. Face Image Databases and Performance Evaluation. - 4. Discussion and Conclusion. - 3. Recognizing Hand Gestures Using Motion Trajectories. - 1. Introduction. - 2. Motivation and Approach. - 3. Motion Segmentation. - 4. Skin Color Model. - 5. Geometric Analysis. - 6. Motion Trajectories. - 7. Recognizing Motion Patterns Using Time-Delay Neural Network. - 8. Experiments. - 9. Discussion and Conclusion. - 4. Skin Color Model. - 1. Proposed Mixture Model. - 2. Statistical Tests. - 3. Experimental Results. - 4. Applications. - 5. Discussion and Conclusion. - 5. Face Detection Using Multimodal Density Models. - 1. Introduction. - 2. Previous Work. - 3. Mixture of Factor Analyzers. - 4. Mixture of Linear Spaces Using Fisher s Linear Discriminant. - 5. Experiments. - 6. Discussion and Conclusion. - 6. Learning to Detect Faces with SNoW. - 1. Introduction. - 2. Previous Work. - 3. SNoW Learning Architecture. - 4. Learning to Detect Faces. - 5. Empirical Results. - 6. Analyzing SNoW: Theoretical and Empirical Results. - 7. Generation and Efficiency. - 8. Discussion and Conclusion. - 7. Conclusion and Future Work. - 1. Conclusion. - 2. Future Work. - Appendices. - A Covariance of Two Normally Distributed Variables. - B Conditional Distributions of Multiple Correlation Coefficient. - References.
