Hierarchical Segmentation Based Upon Multi-resolution Approximations and the Watershed Transform

Abstract

Image segmentation is a classical problem in image processing, which aims at defining an image partition where each identified region corresponds to some object present in the scene. The watershed algorithm is a powerful tool from mathematical morphology to perform this specific task. When applied directly to the gradient of the image to be segmented, it usually yields an over-segmented image. To address this issue, one often uses markers that roughly correspond to the locations of the objects to be segmented. The main challenge associated to marker-controlled segmentation becomes thus the determination of the markers locations. In this article, we present a novel method to select markers for the watershed algorithm based upon multi-resolution approximations. The main principle of the method is to rely on the discrete decimated wavelet transform to obtain successive approximations of the image to be segmented. The minima of the gradient image of each coarse approximation are then propagated back to the original image space and selected as markers for the watershed transform, thus defining a hierarchical structure for the detected contours. The performance of the proposed approach is evaluated by comparing its results to manually segmented images from the Berkeley segmentation database.