Stereo Matching with Spatiotemporal Disparity Refinement Using Simple Linear Iterative Clustering Segmentation

Hui-Yu Huang,Zhe-Hao Liu

doi:10.3390/electronics10060717

Abstract

Stereo matching is a challenging problem, especially for computer vision, e.g., three-dimensional television (3DTV) or 3D visualization. The disparity maps from the video streams must be estimated. However, the estimated disparity sequences may cause undesirable flickering errors. These errors result in poor visual quality for the synthesized video and reduce the video coding information. In order to solve this problem, we here propose a spatiotemporal disparity refinement method for local stereo matching using the simple linear iterative clustering (SLIC) segmentation strategy, outlier detection, and refinements of the temporal and spatial domains. In the outlier detection, the segmented region in the initial disparity is used to distinguish errors in the binocular disparity. Based on the color similarity and disparity difference, we recalculate the aggregated cost to determine adaptive disparities to recover the disparity errors in disparity sequences. The flickering errors are also effectively removed, and the object boundaries are well preserved. Experiments using public datasets demonstrated that our proposed method creates high-quality disparity maps and obtains a high peak signal-to-noise ratio compared to state-of-the-art methods.

Highlights

Over the past several decades, disparity estimation is a problem in stereo vision, which has been investigated, and is still an active research topic in the field of computer vision [1]
The global methods produce more accurate disparity maps, which are typically derived from an energy minimization framework that allows for the express integration of disparity smoothness constraints, and are able to regularize the solution in weakly textured areas
The final disparities are selected from the cost volume by going through its values and selecting the disparities associated with the minimum matching costs for every pixel of the reference image

Summary

Introduction

Over the past several decades, disparity estimation is a problem in stereo vision, which has been investigated, and is still an active research topic in the field of computer vision [1]. As a result of minimization, they use an iterative strategy or graph cuts, which require a high computing cost These methods are often quite slow, and unsuitable for processing a large amount of data. In the WTA framework, local stereo methods consider a range of disparity hypotheses and compute a cost volume using various pixel-wise dissimilarity metrics between the reference image and the matched image at every considered disparity value. Considering the computational cost, we used a local method with the WTA strategy in this study

Methods

Results

Discussion

Conclusion