- Full Description
This comprehensive volume is an essential reference tool for professional and academic researchers in the filed of computer vision, image processing, and applied mathematics. Continuing rapid advances in image processing have been enhanced by the theoretical efforts of mathematicians and engineers. This marriage of mathematics and computer vision - computational vision - has resulted in a discrete approach to image processing that is more reliable when leveraging in practical tasks. This comprehensive volume provides a detailed discourse on the mathematical models used in computational vision from leading educators and active research experts in this field. Topical areas include: image reconstruction, segmentation and object extraction, shape modeling and registration, motion analysis and tracking, and 3D from images, geometry and reconstruction. The book also includes a study of applications in medical image analysis. Handbook of Mathematical Models in Computer Vision provides a graduate-level treatment of this subject as well as serving as a complete reference work for professionals.
- Table of Contents
Table of Contents
- Image Reconstruction.
- Diffusion Filters and Wavelets.
- Total Variation Image Restoration.
- Based Image and Surface Inpainting.
- Boundary Extraction, Segmentation and Grouping.
- Theories and Applications of Graph Cuts in Vision and Graphics.
- Minimal Paths and Fast Marching Methods for Image Analysis.
- Integrating Shape and Texture in Deformable Models.
- Variational Segmentation with Shape Priors.
- Curve Propogation, Level Set Methods and Grouping.
- Shape Modeling and Registration.
- Invariant Processing and Occlusion Resistant Recognition of Planar Shapes.
- Planar Shape Analysis and Its Applications in Image
- Based Inferences.
- Diffeomorphic Point Matching.
- Driven, Point
- Based Image Registration.
- Motion Analysis, Optical Flow and Tracking.
- Optical Flow Estimation.
- Image Alignment and Stitching.
- Visual Tracking.
- Shape Gradient for Image and Video Segmentation.
- Based Human Motion Capture.
- Modeling Dynamic Scenes.
- 3D from Images, Projective Geometry and Stereo Reconstruction.
- Differential Geometry from the Frenet Point of View.
- Shape from Shading.
- Calibration, Motion and Shape Recovery from 3D Image Sequences.
- view Recontruction of Static and Dynamic Scenes.
- Medical Image Analysis.
- Interactive Graph
- Based Segmentation Methods in Cardiovascular Imaging.
- Segmentation of Diffusion Tensor Images.
- Statistical Methods of Medical Image Registration.
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