Issue No. 11 - November (2011 vol. 33)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.69
Li Tang , University of Iowa, Iowa City
Mona K. Garvin , University of Iowa, Iowa City
Kyungmoo Lee , University of Iowa, Iowa City
Wallace L.M. Alward , University of Iowa, Iowa City
Young H. Kwon , University of Iowa, Iowa City
Michael D. Abràmoff , University of Iowa, Iowa City
A robust multiscale stereo matching algorithm is proposed to find reliable correspondences between low contrast and weakly textured retinal image pairs with radiometric differences. Existing algorithms designed to deal with piecewise planar surfaces with distinct features and Lambertian reflectance do not apply in applications such as 3D reconstruction of medical images including stereo retinal images. In this paper, robust pixel feature vectors are formulated to extract discriminative features in the presence of noise in scale space, through which the response of low-frequency mechanisms alter and interact with the response of high-frequency mechanisms. The deep structures of the scene are represented with the evolution of disparity estimates in scale space, which distributes the matching ambiguity along the scale dimension to obtain globally coherent reconstructions. The performance is verified both qualitatively by face validity and quantitatively on our collection of stereo fundus image sets with ground truth, which have been made publicly available as an extension of standard test images for performance evaluation.
Depth from stereo, radiometric differences, pixel feature vector, fundus image, scale space.
Li Tang, Mona K. Garvin, Kyungmoo Lee, Wallace L.M. Alward, Young H. Kwon, Michael D. Abràmoff, "Robust Multiscale Stereo Matching from Fundus Images with Radiometric Differences", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 2245-2258, November 2011, doi:10.1109/TPAMI.2011.69