A Lattice-based MRF model for Dynamic Near-regular Texture Tracking and Manipulation

Abstract

A near-regular texture (NRT) is a geometric and photometric deformation from its regular origin ?a congruent wallpaper pattern formed by 2D translations of a single tile. A dynamic NRT is an NRT under motion. Correspondingly, the basic unit of a dynamic NRT is a well-defined texton, as a geometrically and photometrically deformed tile, moving through a 3D spatiotemporal space. Although NRTs are pervasive in man-made and natural environments, effective compu- tational algorithms for NRTs are few. Through a systematic and quantitative comparison study of multiple texture synthesis algorithms, we are able to show that faithful NRT synthesis has challenged most of the state of the art texture synthesis algorithms. Our recent work on static NRTs analysis and manipulation [Liu et al., 2004] is the first algorithmic treatment aimed specifically to preserve the regularity and randomness in real-world near regular textures. The theme of this thesis is to address computational issues in modeling, tracking and manipulating dynamic NRTs. One basic observation on dynamic NRT is its topology invariance property: the lattice structure of a dynamic NRT remains invariant despite its drastic geometry or appearance variations. We propose a lattice-based Markov- Random-Field (MRF) model for dynamic NRT in a 3D spatiotemporal space. Our dynamic NRT model consists of a global lattice structure that characterizes the topological constraint among multiple textons and an image observation model that handles local geometry and appearance variations. Our model behaves like a network of statistically varied springs. Based on our dynamic NRT model, we develop a tracking algorithm that can effectively handle the special challenges of dynamic NRT tracking, including: ambiguous correspondences, occlusions, illumination variations, and appearance variations. Our algorithm does not assume the type of motion that a dynamic NRT may undergo. Furthermore, we implement a dynamic NRT manipulation system that can replace and superimpose images on a dynamic NRT from an unknown environment. The main contributions of this thesis are: First, a novel and general quadrilateral lattice-based MRF model is proposed for dynamic NRT. Second, we implement a dynamic NRT tracking algorithm that can effectively handle real-world dynamic NRT with occlusions. Third, the proposed dynamic NRT framework makes it possible to accomplish several video editing and manipulation tasks, including real-world dynamic NRT synthesis, replacement, and superimposition