[IPOL announce] new article: Stereo Disparity through Cost Aggregation with Guided Filter

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2014/78/

Pauline Tan, and Pascal Monasse,
Stereo Disparity through Cost Aggregation with Guided Filter,
Image Processing On Line, 4 (2014), pp. 252–275.
http://dx.doi.org/10.5201/ipol.2014.78


Abstract
Estimating the depth, or equivalently the disparity, of a stereo scene 
is a challenging problem in computer vision. The method proposed by 
Rhemann et al. in 2011 is based on a filtering of the cost volume, which 
gives for each pixel and for each hypothesized disparity a cost derived 
from pixel-by-pixel comparison. The filtering is performed by the guided 
filter proposed by He et al. in 2010. It computes a weighted local 
average of the costs. The weights are such that similar pixels tend to 
have similar costs. Eventually, a winner-take-all strategy selects the 
disparity with the minimal cost for each pixel. Non-consistent labels 
according to left-right consistency are rejected; a densification step 
can then be launched to fill the disparity map. The method can be used 
to solve other labeling problems (optical flow, segmentation) but this 
article focuses on the stereo matching problem.