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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Corner detection and classification using anisotropic directional derivative representations.

Peng-Lang Shui1, Wei-Chuan Zhang

  • 1National Lab of Radar Signal Processing, Xidian University, Xiàn 710071, China. plshui@xidian.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 8, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel corner detector and classifier using anisotropic directional derivative (ANDD) representations. The ANDD method enhances corner detection accuracy and repeatability, outperforming existing techniques.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Corner detection is crucial for image analysis and computer vision tasks.
  • Existing methods like He & Yung and CPDA have limitations in detection capability and repeatability.
  • Anisotropic directional derivative (ANDD) representations offer a novel way to characterize local image variations.

Purpose of the Study:

  • To propose a new corner detector and classifier based on ANDD representations.
  • To evaluate the performance of the proposed detector against state-of-the-art methods.
  • To assess the classifier's ability to distinguish different types of corners.

Main Methods:

  • Utilizing Canny edge detection to obtain edge maps and extract contours.
  • Calculating normalized ANDD representations at contour pixels to form a corner measure.
  • Applying non-maximum suppression and thresholding for corner identification.
  • Developing a corner classifier based on the peak number of ANDD representations.

Main Results:

  • The proposed corner detector demonstrates competitive detection capability compared to He & Yung and CPDA detectors.
  • The ANDD-based detector exhibits superior repeatability under affine transformations.
  • The corner classifier effectively differentiates between simple, Y-type, and higher-order corners.

Conclusions:

  • The proposed ANDD-based corner detector offers a robust and repeatable solution for image analysis.
  • The ANDD representation provides a powerful tool for both corner detection and classification.
  • This approach advances the field of feature detection in computer vision.