Generalized Boundaries from Multiple Image Interpretations

Marius Leordeanu, Rahul Sukthankar, Cristian Sminchisescu

Research output: Contribution to journalArticlepeer-review

32 Citations (SciVal)

Abstract

Boundary detection is a fundamental computer vision problem that is essential for a variety of tasks, such as contour and region segmentation, symmetry detection and object recognition and categorization. We propose a generalized formulation for boundary detection, with closed-form solution, applicable to the localization of different types of boundaries, such as object edges in natural images and occlusion boundaries from video. Our generalized boundary detection method (Gb) simultaneously combines low-level and mid-level image representations in a single eigenvalue problem and solves for the optimal continuous boundary orientation and strength. The closed-form solution to boundary detection enables our algorithm to achieve state-of-the-art results at a significantly lower computational cost than current methods. We also propose two complementary novel components that can seamlessly be combined with Gb: first, we introduce a soft-segmentation procedure that provides region input layers to our boundary detection algorithm for a significant improvement in accuracy, at negligible computational cost; second, we present an efficient method for contour grouping and reasoning, which when applied as a final post-processing stage, further increases the boundary detection performance.
Original languageEnglish
Pages (from-to)1312-1324
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume36
Issue number7
DOIs
Publication statusPublished - 2014

Subject classification (UKÄ)

  • Mathematics

Keywords

  • Edge
  • boundary and contour detection
  • occlusion boundaries
  • soft image
  • segmentation
  • computer vision

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