Depth estimation in integral images by anchoring optimization techniques
This paper presents two algorithms for estimating depth from integral images, which capture a scene by using multiple lenses, offering anaglyph depictions. The first algorithm involves the 3-D integral imaging grid formed by casting rays inversely through the lenses used to capture the integral image. In this formulation, depth estimation is equivalent to finding correspondences on the ray-crossing points. The second algorithm follows the depth-through disparity approach. In this case, a stereo-like minimization problem is formulated which is handled by the graph cuts method. The novelty of the proposed paper lies in constraining the optimization procedures with the “anchor points”. This results in enhanced estimation accuracy, while eliminating the optimization complexity. Anchor points is a set of reliable reference points, detected by applying a robust local image descriptor to viewpoint images, called self-similarity descriptor. The performance of both algorithms is evaluated on a synthetic integral image database in comparison with another state-of-the-art algorithm.