The Analysis of Invariant Repetitive Patterns in Images and Videos

The efficient and robust extraction of repetitive patterns from images is a ground challenge problem in computer vision. The repetitive patterns in images are products of both repetitive structures as well as repetitive reflections or color patterns. The perception of repetitive patterns in an image is strongly linked to the visual interpretation and composition of objects or textures. In other words, patterns that exhibit near-stationary behavior provide rich information about objects, their shapes, and their textures in an image. However, methodologies for repetition detection are still limited and there are few methods for analyzing the repetition composition. For the repetitive pattern detection and compositional analysis, there are three fundamental problems which we need to solve: 1. How to identify and extract repetitive invariant patterns (i.e., textons, texels, voxels) from images and videos. 2. How to cluster and group the repetitive patterns according to their intrinsic geometry. 3. How to construct the composition model for the detected repetitive patterns, when the patterns can be organized in a global structure. To answer the first and the second questions, we propose a new algorithm for repetitive pattern detection and grouping. The algorithm follows the classical region growing image segmentation scheme. A modified mean-shift algorithm is utilized to group local image patches into clusters. A continuous joint alignment is exploited to match similar patches and refine the subspace grouping. To answer the third question, we propose an algorithm for inferring the composition structure of the repetitive patterns. The inference algorithm is based on the data-driven structural completion fields and the compositional analysis of structure generators. The global structure is then recovered by identifying the