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Contour based object detection using part bundles.

ChengEn Lu1, Nagesh Adluru2, Haibin Ling3

  • 1Dept. of Computer and Information Science, Temple University, 324 Wachman Hall, 1805 N Broad St., Philadelphia, PA 19122, USA; Dept. of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Div Commun and Intelligent Networks, Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China.

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Summary
This summary is machine-generated.

This study introduces a new framework for contour-based object detection in cluttered scenes. It efficiently identifies objects by grouping contour fragments and using shape similarity for accurate detection, even with texture variations.

Keywords:
Object detectionPart bundleShape context

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Object detection in cluttered environments remains a significant challenge.
  • Existing methods struggle with occlusions and variations in object appearance.

Purpose of the Study:

  • To propose a novel framework for contour-based object detection in cluttered environments.
  • To improve detection accuracy by utilizing hierarchical contour decomposition and part-based matching.

Main Methods:

  • Hierarchical decomposition of contour models into fragments and part bundles.
  • An efficient voting method using local shape similarity for candidate part configuration generation.
  • Global shape similarity for selecting optimal configurations.
  • Integration of appearance information to enhance detection for textured objects.

Main Results:

  • The proposed framework generates high-quality candidate part configurations.
  • Optimal configurations are identified using global shape similarity.
  • Appearance information improves detection for objects with distinctive textures and deformations.

Conclusions:

  • The novel framework offers an effective approach for contour-based object detection in complex scenes.
  • Combining contour and appearance information enhances robustness to object deformation and clutter.