Dynamic NRT Tracking and Manipulation

Dynamic Near-regular Texture Tracking and Manipulation

Overview

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 computational 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 (SIGGRAPH 2004) is the first algorithmic treatment aimed specifically to preserve the regularity and randomness in real world near regular textures.

The theme of this project 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. Furthermore, we implement a dynamic NRT manipulation system that can replace and superimpose augmented images on a dynamic NRT from an unknown environment.

Publications

· Wen-Chieh Lin and Yanxi Liu, “Tracking Dynamic Near-regular Textures under Occlusions and Rapid Movements,” 9th European Conference on Computer Vision. Graz, Austria. May, 2006.

· Wen-Chieh Lin, “A Lattice-based MRF Model for Dynamic Near-regular Texture Tracking and Manipulation,” Technical Report CMU-RI-TR-05-58, Ph.D. Thesis, Robotics Institute, Carnegie Mellon University, Dec, 2005.

· Wen-Chieh Lin and Yanxi Liu, “A Lattice-based MRF Model for Dynamic Near-regular Texture Tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 5, 2007, pp. 777-792.

Links to results

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Texton Detection Results