Higher level segmentation: Detecting and grouping of invariant repetitive patterns

Yunliang Cai, George Baciu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

The efficient and robust extraction of invariant patterns from an image is a long-standing problem in computer vision. Invariant structures are often related to repetitive or near-repetitive patterns. The perception of repetitive patterns in an image is strongly linked to the visual interpretation and composition of textures. Repetitive patterns are products of both repetitive structures as well as repetitive reflections or color patterns. In other words, patterns that exhibit near-stationary behavior provide a rich information about objects, their shapes, and their texture in an image. In this paper, we propose a new algorithm for repetitive pattern detection and grouping. The algorithm follows the classical region growing image segmentation scheme. It utilizes a mean-shift-like dynamics to group local image patches into clusters. It exploits a continuous joint alignment to (a) match similar patches and (b) refine the subspace grouping. The result of higher-level grouping for image patterns can be used to infer the geometry of object surfaces and estimate the general layout of a crowded scene.
Original languageEnglish
Title of host publication2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Pages694-701
Number of pages8
DOIs
Publication statusPublished - 1 Oct 2012
Event2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 - Providence, RI, United States
Duration: 16 Jun 201221 Jun 2012

Conference

Conference2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Country/TerritoryUnited States
CityProvidence, RI
Period16/06/1221/06/12

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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