A lattice-based MRF model for dynamic near-regular texture tracking

Wen Chieh Lin, Yanxi Liu

Research output: Contribution to journalArticle

59 Scopus citations

Abstract

A near-regular texture (NRT) is a geometric and photometric deformation from its regular originA-a congruent wallpaper pattern formed by 2D translations of a single tile. A dynamic NRT is an NRT under motion. Although NRTs are pervasive in man-made and natural environments, effective computational algorithms for NRTs are few. This paper addresses specific computational challenges in modeling and tracking dynamic NRTs, including ambiguous correspondences, occlusions, and drastic illumination and appearance variations. We propose a lattice-based Markov-Random-Field (MRF) model for dynamic NRTs in a 3D spatiotemporal space. Our 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. Based on the proposed MRF model, we develop a tracking algorithm that utilizes belief propagation and particle filtering to effectively handle the special challenges of the dynamic NRT tracking without any assumption on the motion types or lighting conditions. We provide quantitative evaluations of the proposed method against existing tracking algorithms and demonstrate its applications in video editing.

Original languageEnglish (US)
Pages (from-to)777-792
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume29
Issue number5
DOIs
StatePublished - May 1 2007

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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