PDE and Level Sets: Algorithmic Approaches to Static and Motion Imagery

PDE & Level Sets: Algorithmic Approaches to Static & Motion Imagery is specially dedicated to the segmentation of complex shapes from the field of imaging sciences using level sets and PDEs. It covers the fundamentals of level sets, different kinds of concepts of both geodesic curvature flows and planar flows, as well as the power of incorporation of regional-statistics in level set framework. In covering this material, this book presents segmentation of object-in-motion imagery based on level sets in eigen analysis framework, while also presenting classical problems of boundary completion in cognitive images, like the pop-up of subjective contours in the famous triangle of Kanizsa using surface evolution framework, or the mean curvature evolution of a graph with respect to the Riemannian metric induced by the image. All results are presented for modal completion of cognitive objects with missing boundaries.
PDE & Level Sets: Algorithmic Approaches to Static & Motion Imagery is aimed at researchers and educators in imaging sciences, biomedical engineering, applied mathematics, algorithmic development, computer vision, signal processing, computer graphics and multimedia in general, both in academia and industry.
Key Features:
Presents detailed review of PDEs and level sets,
Covers concepts of geodesic curvature flows and planar flows,
Offers advance applications of level sets for crack propagation and planar cracks,
Describes multi-resolution level sets for segmentation of video images,
Identifies fusion of fuzzy techniques in level set framework,
Discusses the role of subjective surfaces and Riemannian metric.