Detecting Features using Random Sample Theory

This paper aims to detect features in 2-D and 3-D highly noisy images using random sample theory fast and with high detection performance. The proposed method yields faster results than standard feature detection algorithms, such as the Hough Transform (HT) or its variants, while keeping the the performance level of HT. Proposed method first finds possible feature areas by creating random hypothesis and testing them. Features are re-estimated by only searching these possible areas which reduces the total search space. The proposed algorithm is tested on both simulated and experimental subsurface Seismic and GPR images for searching linear features like pipes or tunnels. Results show that the proposed algorithm can detect features accurately and much faster than conventional methods.