Real-time stereo matching on CUDA using Fourier descriptors and dynamic programming

Abstract 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 neighboring pixels. Optimization and disparity calculation are done using dynamic programming, with a cost based on similarity between generalized Fourier descriptors using Euclidean distance. This local cost function is 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

Accelerating Fourier Descriptor for Image Recognition Using GPU

In the next few years, the rate of enhancement in GPUs (Graphics Processing Units) performance is expected to outshine that of CPUs (Central Processing Units), increasing the demand of the GPU as the processor chosen for image processing. In light of tremendous advance in computer vision research of recognition shape domain, we proposed a GPU technology of programming and computing to accelerate the Fourier descriptor technique invariant to color images classification. It is a simple and powerful technique to represent objects based on their shapes. It has attractive properties such as rotational, scale, and translational invariance. Since the heaviest part of Fourier descriptor computing time is the Fast Fourier Transform (FFT), we decided to bring it out on GPU. We used CUDA: Compute Unified Device Architecture, the specific programming language of GPU, and its CUFFT library to accelerate the computation of FFT. To showcase this implementation, we studied the performance of GPU versus a traditional implementation on CPU for single and double precision