title: Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling.
abstract: In this paper, we formulate a stereo matching algorithm with careful handling of disparity, discontinuity and occlusion. The algorithm works with a global matching stereo model based on an energy-minimization framework. The global energy contains two terms, the data term and the smoothness term. The data term is first approximated by a color-weighted correlation, then refined in occluded and low-texture areas in a repeated application of a hierarchical loopy belief propagation algorithm. The experimental results are evaluated on the Middlebury data sets, showing that our algorithm is the top performer among all the algorithms listed there.
authors.affiliation: University of Illinois at Urbana Champaign, Urbana, IL 61801, USA. quang6@uiuc.edu
authors.forename: Qingxiong
authors.initials: Q
authors.lastname: Yang
authors.forename: Liang
authors.initials: L
authors.lastname: Wang
authors.forename: Ruigang
authors.initials: R
authors.lastname: Yang
authors.forename: Henrik
authors.initials: H
authors.lastname: Stewénius
authors.forename: David
authors.initials: D
authors.lastname: Nistér
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010780:Photogrammetry / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D013382:Subtraction Technique