Corner detection for identification of man-made objects in noisy aerial images

Corner detection is an essential feature extraction step in many image understanding applications including aerial image analysis and manufactured part inspection. Available corner detectors require the user to set critical manual thresholds, degrade under significant noise levels, or introduce high computational complexity. We present a nonlinear corner detection algorithm that does not require prior image information or any threshold to be set by the user. It provides 100% correct corner detection and fewer than I false positive corner per image when the contrast to noise ratio of the image is 6 or more, under Gaussian white noise.