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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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Published on: August 30, 2013

Appearance modeling using a geometric transform.

Jian Li1, Shaohua Kevin Zhou, Rama Chellappa

  • 1Center for Automation Research, Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA. lij@cfar.umd.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 13, 2009
PubMed
Summary
This summary is machine-generated.

A novel geometric transform (GeT) models appearance within contours, unifying existing methods and creating invariant models. This approach shows promise for tasks like human recognition and segmentation, even with complex motion.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Geometric Modeling

Background:

  • Modeling appearance within image contours is crucial for various computer vision tasks.
  • Existing methods often struggle with variations in shape, deformation, and occlusion.

Purpose of the Study:

  • To propose a general geometric transform (GeT) for modeling appearance inside closed contours.
  • To unify existing transforms and develop novel GeT variations for enhanced image analysis.

Main Methods:

  • The geometric transform (GeT) is defined as a functional of image intensity and region indicator functions.
  • Five novel GeT variations were introduced, including level set-based, shape matching, feature curve-based, occlusion-invariant, and multiresolution (MRGeT).
  • These methods were applied to fingerprinting appearance inside contours.

Main Results:

  • The proposed GeT unifies Radon transform, trace transform, and image warpings.
  • Novel GeT applications demonstrated effectiveness in pedestrian identity recognition, human body part segmentation, and image synthesis.
  • Promising results were achieved in appearance fingerprinting of humans and body parts, handling nonrigid deformations and articulated motion.

Conclusions:

  • The geometric transform (GeT) offers a flexible and unified framework for appearance modeling within contours.
  • The developed GeT variations provide robust solutions for complex image analysis tasks, including recognition and segmentation.
  • GeT demonstrates significant potential for real-world applications involving deformable and articulated objects.