Depth map estimation: a region-based approach

This work has been performed in the project PANORAMA, co-funded by grants from Belgium, Italy, France, the Netherlands, and the United Kingdom, and the ENIAC Joint Undertaking.This paper presents an approach to the estimation of depth from stereo images which exploits correspondences of image segments. A key motivation is to bypass pixel correspondences which are ambiguous in texture-less regions. One of the main difficulty though is to come up to an equivalent partitioning of both stereo images that facilitates region matching. The reference image which the depth is estimated for is first segmented using the watershed algorithm. Regions are then transferred to the other image of the stereo pair according to a correlation analysis of region contours. This transfer yields regional disparities which are significant as long as the contours for a given region are not due to an occlusion. Based on that observation, we propose an algorithm that detects occlusion contours along a region and that rectifies disparity accordingly

A top-down methodology to depth map estimation controlled by morphological segmentation

Given a pair of stereo images and the transformation existing between the corresponding camera coordinate systems, the depth of a scene point can be computed from its projections on both images. Despite the difficulties related to the matching of such projections across homogeneous regions and the occlusion phenomenon, state of the art methods have already produced accurate results on classical stereo datasets. This article proposes a new way of approaching depth estimation. Instead of searching for dense pixel correspondences, a gross estimation of the disparities is initially performed at the region level, resulting in a regional disparity map which highlights the principal depth layers of the image. The disparity map is then systematically refined by considering finer partitions of the image. To this end, the watershed of the image colour gradient is selected in order to compute the image partitions alongside a meaningful hierarchy. We show that the ability to be driven by labelled markers enables the watershed algorithm to generate a co-segmentation of both stereo images given the regional disparities, which constitutes the main contribution of this paper. This co-segmentation allows one to reliably compute the disparities of pixels along the region contours. Finally, the contour disparities are transferred to the concerned regions after a careful analysis of their occlusion state with respect to each adjacent region. Though approximate, we show that the proposed method yields regional disparity maps which are close enough to ground truths in the view of performing the desired refinements. We also expose the perspectives of this methodology with respect to challenging stereo imagery, i.e. which is affected by noise or which contains a considerable amount of homogeneous regions

Morphological processing of stereoscopic image superimpositions for disparity map estimation

This paper deals with the problem of depth map computation from a pair of rectified stereo images and presents a novel solution based on the morphological processing of disparity space volumes. The reader is guided through the four steps composing the proposed method: the segmentation of stereo images, the diffusion of superimposition costs controlled by the segmentation, the resulting generation of a sparse disparity map which finally drives the estimation of the dense disparity map. An objective evaluation of the algorithm's features and qualities is provided and is accompanied by the results obtained on Middlebury's 2014 stereo database

Data Fusion in a Hierarchical Segmentation Context: The Case of Building Roof Description

Automatic mapping of urban areas from aerial images is a challenging task for scientists an