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

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.

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

“…There are different segmentation techniques that produce different results. [14,15]), while the hybrid techniques consist of a combination of classical segmentation methods and the AI techniques (for example, [14,16]). The following section describes the four bio-inspired algorithms investigated in this study.…”

“…The region-based techniques include the histogram threshold, clustering, region growing, and split-and-merge methods. The AI segmentation techniques are mainly based on the ANN algorithm (for example,[14,15]), while the hybrid techniques consist of a combination of classical segmentation methods and the AI techniques (for example,[14,16]). The following section describes the four bio-inspired algorithms investigated in this study.…”

Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System

“…The Lucas-Kanade method can be applied just when the object displacement between the two frames is small enough for the differential equation of the optical flow to hold. This is often less than pixel spacing [23]. Subsequently, when the flow vector exceeds such a certain limit, for example, in warped document registration or stereo matching, it is possible for the Lucas-Kanade method to be still utilized to refine some coarse estimate of that image.…”

“…However, the run time rose depending on the iteration number. Then, the method converged when the algorithm was stable [23].…”