generalized Fourier descriptors, stereo matching, dynamic programming, CUDA
Computation of stereoscopic depth and disparity map extraction are dynamic research topics. A large variety of algorithms has been developed, among which we cite feature matching, moment extraction, and image representation using descriptors to determine a disparity map. This paper proposes a new method for stereo matching based on Fourier descriptors. The robustness of these descriptors under photometric and geometric transformations provides a better representation of a template or a local region in the image. In our work, we specifically use generalized Fourier descriptors to compute a robust cost function. Then, a box filter is applied for cost aggregation to enforce a smoothness constraint between neighboringpixels. Optimization and disparity calculation are done using dynamic programming, with a cost based on similarity between generalized Fourier descriptorsusing Euclidean distance. This local cost functionis used to optimize correspondences. Our stereo matching algorithm is evaluated using the Middlebury stereo benchmark; our approach has been implemented on parallel high-performance graphics hardware using CUDA to accelerate our algorithm, giving a real-time implementation.
Tsinghua University Press
Mohamed Hallek, Fethi Smach, Mohamed Atri. Real-time stereo matching on CUDA using Fourier descriptors and dynamic programming. Computational Visual Media 2019, 5(1): 59-71.