Reclustering Techniques Improve Early Vision Feature Maps

Abstract

We propose a recursive post-processing algorithm to improve feature-maps, like disparity- or motion-maps, computed by early vision modules. The statistical distribution of the features is computed from the original feature-map, and from this the most likely candidate for a correct feature is determined for every pixel. This process is performed automatically by a clustering algorithm which determines the feature candidates as the cluster centres in the distribution. After determining the feature candidates, a cost function is computed for every pixel, and a candidate will only replace the original feature if the cost is reduced. In this way, a new feature-map is generated which, in the next iteration, serves as the basis for the computation of the updated feature distribution. Iterations are stopped if the total cost reduction is less than a pre-defined threshold. In general, our technique is albe to reduce two of the most common problems that affect feature-maps, the sparseness, i.e. the presence of areas where the algorithm is not able to give meaningful measurements, and the blur. To show the efficacy of our approach, we apply the reclustering algorithm to several examples of increasing complexity, showing results for synthetic and natural images.