Lin Wen-Chieh, Liu Yanxi
Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan.
IEEE Trans Pattern Anal Mach Intell. 2007 May;29(5):777-92. doi: 10.1109/TPAMI.2007.1053.
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. 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.
近规则纹理（NRT）是相对于其规则原点的一种几何和光度变形，规则原点是由单个瓦片的二维平移形成的全等壁纸图案。动态NRT是运动中的NRT。尽管NRT在人造和自然环境中普遍存在，但针对NRT的有效计算算法却很少。本文解决了在动态NRT建模和跟踪中的特定计算挑战，包括模糊对应、遮挡以及剧烈的光照和外观变化。我们为三维时空空间中的动态NRT提出了一种基于格网的马尔可夫随机场（MRF）模型。我们的模型由一个全局格网结构和一个图像观测模型组成，全局格网结构表征多个纹理基元之间的拓扑约束，图像观测模型处理局部几何和外观变化。基于所提出的MRF模型，我们开发了一种跟踪算法，该算法利用置信传播和粒子滤波来有效应对动态NRT跟踪的特殊挑战，而无需对运动类型或光照条件做任何假设。我们针对现有跟踪算法对所提方法进行了定量评估，并展示了其在视频编辑中的应用。
