Year: 2019
Type: article
Abstract: Stereo matching methods have achieved remarkable improvements by exploiting various attempts. However, most stereo matching algorithms still suffer from problems such as ambiguous region and inherent ambiguities. In particular, some problems affecting cost aggregation step have the greatest impact on depth results. To resolve the above-mentioned problems, we propose a new cost aggregation method using the modified total generalized variation with fusion tensor. First, two kinds of diffusion tensors are extracted from the guidance color image and the guidance depth map. They are incorporated into an energy functional to obtain the total generalized variation. After formulating the final energy functional, it is optimized via a primal-dual energy minimization method. The performance of the proposed method is experimentally verified by qualitatively and quantitatively comparing the results to those of other algorithms.
Source: IEEE Access
Authors: Eu-Tteum Baek, Hyung Jeong Yang
Institution: Chonnam National University
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,750
Awards: G2658208423, G8416873545
OpenAlex ID: W2972481069