Feature-graph Based Image Segmentation and Disparity Propagation in Stereo Matching
Date Issued
2011
Date
2011
Author(s)
Lin, Hong-Shang
Abstract
In this paper, we propose a robust method for stereo matching. First,we use SIFT matching to generate initial sparse correspondence between images, and then use our feature graph-based image segmentation to discover more features and refine feature matching iteratively. To find outliers near objects’ boundary, we cluster the features into groups, and divide each feature group with minimum spanning tree construction. Finally, we generate disparity maps by propagating the disparity values of those sparse features to other near-by unmatched pixels. In the propagation process, we will determine each pixel’s neighbouring features via the feature-based region growing
on the minimum spanning tree. It contains two stages: (1) local feature disparity selection and (2) global propagation using energy minimization. Our method can be applied to a wide variety of cases, only few parameters need to be adjusted or specified. We evaluate our algorithm using the cases in the website Middlebury[SSZ01], and the results show that the average of bad pixels is about 10%.
Subjects
feature graph
feature group
disparity propagation
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-100-R98921035-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):18838ec220e24c691c536c2a99387fad