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A Modified Harris Corner Detection for Breast IR Image

Author

Listed:
  • Chia-Yen Lee
  • Hao-Jen Wang
  • Chung-Ming Chen
  • Ching-Cheng Chuang
  • Yeun-Chung Chang
  • Nien-Shiang Chou

Abstract

Harris corner detectors, which depend on strong invariance and a local autocorrelation function, display poor detection performance for infrared (IR) images with low contrast and nonobvious edges. In addition, feature points detected by Harris corner detectors are clustered due to the numerous nonlocal maxima. This paper proposes a modified Harris corner detector that includes two unique steps for processing IR images in order to overcome the aforementioned problems. Image contrast enhancement based on a generalized form of histogram equalization (HE) combined with adjusting the intensity resolution causes false contours on IR images to acquire obvious edges. Adaptive nonmaximal suppression based on eliminating neighboring pixels avoids the clustered features. Preliminary results show that the proposed method can solve the clustering problem and successfully identify the representative feature points of IR breast images.

Suggested Citation

  • Chia-Yen Lee & Hao-Jen Wang & Chung-Ming Chen & Ching-Cheng Chuang & Yeun-Chung Chang & Nien-Shiang Chou, 2014. "A Modified Harris Corner Detection for Breast IR Image," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-12, July.
    • Handle: RePEc:hin:jnlmpe:902659
    DOI: 10.1155/2014/902659
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