Robust edge and corner detection using noise identification and adaptive thresholding techniques

This paper presents a robust, two-step method for edge and corner detection in noisy images. First it identifies the type of noise using a new pattern classification approach and then restores the image using a good restoration technique suitable for the type of noise identified. The types of noise considered here include uniform white, Gaussian white, speckle, and salt-and-pepper noise. From the restored image, edge and corner strengths are determined using gradient based techniques, and finally, a fuzzy k-means clustering algorithm is used to find adaptive thresholds for detecting the edge and corner points. Results of some simulation studies are presented here and they seem to indicate that the proposed algorithm works very well.