Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization (Paperback)

Ill-Posed Problems in Imaging and Computer Vision.- Selection of the Regularization Parameter.- Introduction to Optimization.- Unconstrained Optimization.- Constrained Optimization.- Frequency-Domain Implementation of Regularization.- Iterative Methods.- Regularized Image Interpolation Based on Data Fusion.- Enhancement of Compressed Video.- Volumetric Description of Three-Dimensional Objects for Object Recognition.- Regularized 3D Image Smoothing.- Multi-Modal Scene Reconstruction Using Genetic Algorithm-Based Optimization.- Appendix A: Matrix-Vector Representation for Signal Transformation.- Appendix B: Discrete Fourier Transform.- Appendix C: 3D Data Acquisition and Geometric Surface Reconstruction.- Appendix D: Mathematical Appendix.- Index.