A neural implementation of the Hough transform and the advantages of explaining away

The Hough transform (HT) is widely used for feature extraction and object detection. However, during the HT individual image elements vote for many possible parameter values. This results in a dense accumulator array and problems identifying the parameter values that correspond to image features. This article proposes a new method for implementing the voting process in the HT. This method employs a competitive neural network algorithm to perform a form of probabilistic inference known as “explaining away”. This results in a sparse accumulator array in which the parameter values of image features can be more accurately identified. The proposed method is initially demonstrated using the simple, prototypical, task of straight line detection in synthetic images. In this task it is shown to more accurately identify straight lines, and the parameter of those lines, compared to the standard Hough voting process. The proposed method is further assessed using a version of the implicit shape model (ISM) algorithm applied to car detection in natural images. In this application it is shown to more accurately identify cars, compared to using the standard Hough voting process in the same algorithm, and compared to the original ISM algorithm.