Corner detection using random forests

We present a fast algorithm for corner detection, exploiting the local features (i.e. intensities of neighbourhood pixels) around a pixel. The proposed method is simple to implement but is efficient enough to give results comparable to that of the state-of-the-art corner detectors. The algorithm is shown to detect corners in a given image using a learning-based framework. The algorithm simply takes the differences of the intensities of candidate pixel and pixels around its neighbourhood and processes them further to make the similar pixels look even more similar and distinct pixels even more distinct. This task is achieved by effectively training a random forest in order to classify whether the candidate pixel is a corner or not. We compare the results with several state-of-the-art techniques for corner detection and show the effectiveness of the proposed method.