Content-aware Shape Deformation

Abstract

In computer vision, the term shape refers to a variety objects such as images, videos and three-dimensional models that are collected from laser scans. Shape deformations refer to all kind of methods that aim to alter the object’s shape or form based on specific applications. The content-aware shape deformation is the process that depends on the local content/information of shapes in order to apply the required change. Segmentation is one of the most common problems that is considered an important pre-process step for a lot of computer vision applications. It aims at partitioning the object into multiple meaningful regions/segments in order to separately deal with each region or cluster. Concerning the image segmentation process, it depends mainly on the local content of image features such as color, boundary, texture, edges or any combination of attributes to locate objects in the image. In other words, each of the pixels in an image region is assigned the same label and is similar with respect to some characteristic or computed property. Graph-based segmentation methods are very promising and more appropriate to find exact solutions to solve the image segmentation problem via optimization methods. One of the most powerful graph based image segmentation methods is the GrabCut technique that depends on a probabilistic model in order to segment color images iteratively. One of the main contributions of this thesis is to apply modifications and extensions to the current semi-automatic binary-label GrabCut technique in order to solve existing problems and improve segmentation accuracy of natural images. All the proposed image segmentation techniques are evaluated for relevant accuracy criteria, and comparative studies are constructed with relevant techniques. The semi-automatic GrabCut capabilities are extended to segmentation of human faces from images of full humans. The main contribution is the introduction of a new prior face location model to the GrabCut energy minimization function in addition to the existing color model. The location model considers the distance distribution of the pixels from the silhouette boundary of a fitted head, of a 3D morphable model, to the image. The proposed technique succeeds in eliminating the camouflage problem associated with the original GrabCut technique. In addition, it improves the segmentation accuracy with an error rate of 0.19% in comparison to the rate of 0.29% of the original GrabCut.