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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

Rotationally invariant descriptors using intensity order pooling.

Bin Fan1, Fuchao Wu, Zhanyi Hu

  • 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Automation Building, 95# Zhongguancun East Road, Beijing 100190, China. bfan@nlpr.ia.ac.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for image description using intensity order pooling, creating rotation-invariant descriptors. These novel descriptors, MROGH and MRRID, improve image matching and object recognition.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Interest region description is crucial for image analysis tasks like matching and recognition.
  • Existing methods often struggle with rotation and intensity variations, leading to errors.
  • Scale Invariant Feature Transform (SIFT), SURF, and DAISY are common but have limitations.

Purpose of the Study:

  • To propose a novel method for interest region description.
  • To develop descriptors invariant to rotation and monotonic intensity changes.
  • To enhance performance in image matching and object recognition tasks.

Main Methods:

  • A new pooling scheme based on intensity orders within multiple support regions is proposed.
  • Two descriptors are derived: Multisupport Region Order-Based Gradient Histogram (MROGH) and Multisupport Region Rotation and Intensity Monotonic Invariant Descriptor (MRRID).
  • The method achieves rotation invariance without explicit reference orientation estimation.

Main Results:

  • The proposed descriptors demonstrate rotation invariance, a significant advantage over methods like SIFT and SURF.
  • Experimental results show the effectiveness of MROGH and MRRID in image matching.
  • The descriptors also prove effective for object recognition tasks.

Conclusions:

  • The novel intensity order pooling method offers robust interest region description.
  • The developed MROGH and MRRID descriptors outperform state-of-the-art methods in key computer vision applications.
  • This approach provides a more reliable alternative for rotation-invariant feature description.