Efficient disparity estimation from stereo images using hybrid-guided image filter

摘要

Stereo vision process involves capturing the pictures from a camera of the same scene from at least two different locations and calculating the three-dimensional information. Conventionally, these two versions of snapshots are called left and right views which yield the depth information of an object upon relative comparison of its location in two views. Although the stereo image and its applications are becoming increasingly prevalent, there has been very limited research on disparity estimation from stereo images. Most of the existing techniques suffer from the gradient reversal artefacts issue. Therefore, to handle this issue, we have proposed a hybrid-guided image filter for improving the disparity estimation from stereo images. The hybrid filter utilizes the features of guided image filter and Bayesian non-local means with edge aware constraint. Maximum likelihood and local area homogeneity analysis are used to generate the guidance image for the proposed filter. To enhance the quality of disparity estimation from stereo images, segmentation is also done using the modified mean shift technique. Experimental results show that the proposed technique can efficiently estimate the depth maps over the available techniques. One-way ANOVA analysis on experimental results validates that the hybrid filter-based stereo matching outperforms consistently over the state-of-art approaches.