Stereo Image Matching Using Adaptive Morphological Correlation

Abstract

A stereo matching method based on adaptive morphological correlation is presented. The point correspondences of an input pair of stereo images are determined by matching locally adaptive image windows using the suggested morphological correlation that is optimal with respect to an introduced binary dissimilarity-to-matching ratio criterion. The proposed method is capable of determining the point correspondences in homogeneous image regions and at the edges of scene objects of input stereo images with high accuracy. Furthermore, unknown correspondences of occluded and not matched points in the scene can be successfully recovered using a simple proposed post-processing. The performance of the proposed method is exhaustively tested for stereo matching in terms of objective measures using known database images. In addition, the obtained results are discussed and compared with those of two similar state-of-the-art methods.

Restoration of Binocular Images Degraded by Optical Scattering through Estimation of Atmospheric Coefficients

“…This approach is based on estimating the scene depth and atmospheric parameters of the medium from a pair of binocular images degraded by optical scattering. The scene depth is obtained by triangulation using a disparity map computed through stereo matching [28]. Next, the atmospheric parameters of the medium are determined using an introduced robust estimator based on the Monte Carlo method.…”

“…The resultant rectified images are preprocessed to improve their visibility by applying a locally-adaptive contrast enhancement method [33]. Afterwards, the improved images are processed by stereo-matching to obtain estimates of the depth functions d i (x, y) of the scene by triangulation using the computed disparity map θ i (x, y) [28]. Next, the hazy images f i (x, y), estimated depth d i (x, y), and disparity map θ i (x, y) are used to estimate the atmospheric parameters β and A in a proposed algorithm based on the Monte Carlo method [34].…”

“…This is because the unknown disparity values can lead to undesirable artifacts in the resulting synthetic images degraded by optical scattering and introduce errors in the estimation of atmospheric parameters as well as image restoration. To remove these unknown values, the ground-truth disparities are preprocessed with the hole-filling method presented in [28], obtaining refined disparities as shown in Figure 3c, where the unknown disparities are removed. Next, the undegraded binocular images of the dataset and their corresponding refined disparities are utilized to compute the scene depth by solving Equation ( 11) as detailed in Section 3.1, by considering the camera parameters specified by the Middlebury dataset.…”

“…The disparities are computed using the stereo-matching method based on morphological correlation presented in Ref. [28]. Additionally, the performance of the existing stereo-vision methods proposed by Li et al [23] and Ding et al [25] are evaluated.…”

Three-dimensional object reconstruction using multi-ocular vision

“…In this work, we present a multi-ocular three-dimensional reconstruction approach based on Adaptive Morphological Correlation (AMC). 7 The scene's disparity map is estimated sequentially from a set of several rectified stereo images of the scene captured from different horizontal perspectives. First, a full dense disparity estimation is carried out for the camera pair with the shortest baseline distance.…”