Corner detection in images under different noise levels
Song, Yi, Shen, Yiping, Li, Shuxiao, Zhu, Chengfei, Zhang, Jinglan, & Chang, Hongxing (2014) Corner detection in images under different noise levels. In Proceedings of 22nd International Conference on Pattern Recognition (ICPR 2014), IEEE Computer Society, Stockholm, Sweden, pp. 906-911.
Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or keypoint detectors.
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|Item Type:||Conference Paper|
|Keywords:||corner detector, noisy image, signal-to-noise ratio, feature detection|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 IEEE|
|Deposited On:||11 Feb 2015 23:05|
|Last Modified:||13 Feb 2015 20:52|
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