Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions

Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features

A Depth Based Approach to Glaucoma Detection Using Retinal Fundus Images

Qualitative evaluation of stereo retinal fundus images by experts is a widely accepted method for optic nerve head evaluation (ONH) in glaucoma. The quantitative evaluation using stereo involves depth estimation of the ONH and thresholding of depth to extract optic cup. In this paper, we attempt the reverse, by estimating the disc depth using supervised and unsupervised techniques on a single optic disc image. Our depth estimation approach is evaluated on the INSPIRE-stereo dataset by using single images from the stereo pairs, and is compared with the OCT based depth ground truths. We extract spatial and intensity features from the depth maps, and perform classification of images into glaucomatous and normal. Our approach is evaluated on a dataset of 100 images and achieves an AUC of 0.888 with a sensitivity of 83% at specificity 83%. Experiments indicate that our approach can reliably estimate depth, and provide valuable information for glaucoma detection and for monitoring its progression

A data-fusion approach to motion-stereo

This paper introduces a novel method for performing motion--stereo, based on dynamic integration of depth (or its proxy) measures obtained by pairwise stereo matching of video frames. The focus is on the data fusion issue raised by the motion--stereo approach, which is solved within a Kalman filtering framework. Integration occurs along the temporal and spatial dimension, so that the final measure for a pixel results from the combination of measures of the same pixel in time and whose of its neighbors. The method has been validated on both synthetic and natural images, using the simplest stereo matching strategy and a range of different confidence measures, and has been compared to baseline and optimal strategies