Feature Detection in Highly Noisy Images using Random Sample Theory

A novel feature detection algorithm utilizing random sample theory is proposed for 2D and 3D images. The proposed method works on both binary and gray-scale images and yields faster results than standard feature detection algorithms, such as the Hough Transform (HT), while keeping the performance level of HT. The proposed method creates random hypothesis features and tests them to select candidate features in the image. The selected candidate features are then re-estimated within a smaller search space around the candidate feature. The proposed algorithm has been tested on both simulated and experimental subsurface seismic and GPR images to locate linear features like pipes or tunnels. Results show that the proposed algorithm can detect features accurately and much faster than conventional methods.