Feature Detection in Images by Adaptive Random Sampling

Random sample theory is an effective tool for detecting features in images. This paper presents an adaptive random sampling scheme that clusters random samples into candidate features. The required trial number is reduced by adaptive sampling, thereby reducing the run time of the algorithm. The proposed method quickly finds rough regions in the image that may include features using adaptive random sampling and re-estimates the features using the Hough Transform (HT) within the smaller regions. The proposed algorithm is tested on both simulated and experimental subsurface seismic and GPR images to search for linear features like pipes or tunnels. Faster results are obtained as compared to standard feature detection algorithms, such as the HT or its variants, while maintaining the similar performance level as the HT.