Abstract
In this study, we integrate confidence into efficient large-scale stereo (ELAS) matching to produce a more accurate approach to binocular stereo for high-resolution image matching. Elas ensures goo...
Highlights
The estimation of disparity maps from binocular imagery has played a fundamental role in computer vision and multimedia processing for decades
The first column depicts the left camera images, the second column is the disparity map estimated by efficient large-scale stereo (ELAS), and the last column is that estimated by confidence-based iterative ELAS (CI_ELAS)
Conclusions and future work We propose an improved stereo matching method based on ELAS
Summary
The estimation of disparity maps from binocular imagery has played a fundamental role in computer vision and multimedia processing for decades. Disparity maps are estimated in a pixel-wise fashion by comparing features over a support region (a concatenation of the features of pixels in a window centered in the pixel) of the reference and target images. Optimization of the resulting MRF based energy function is generally considered to be NP-hard Many techniques such as graph cut (Boykov et al, 1998; Kolmogorov & Zabih, 2001; Taniai, Matsushita, & Naemura, 2014) or belief propagation (Felzenszwalb & Huttenlocher, 2006) have been proposed to solve them effectively, but these algorithms are all too slow for images of a reasonable size (Szeliski et al, 2008)
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