Registration of Translated and Rotated Images Using Finite Fourier Transforms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Grayscale template-matching invariant to rotation, scale, translation, brightness and contrast
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
ORB: An efficient alternative to SIFT or SURF
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper presents highly optimized implementation of image registration method that is invariant to rotation scale and translation. Image registration method using FFT works with comparable accuracy as similar methods proposed in the literature, but practical applications seldom use this technique because of high computational requirement. However, this method is highly parallelizable and offloading it to the commodity graphics cards increases its performance drastically. We are proposing the parallel implementation of FFT based registration method on CUDA and OpenCL. Performance analysis of this implementation suggests that the parallel version can be used for real time image registration even for image size up to 2k x 2k. We have achieved significant speed up of up to 345x on NVIDIA GTX 580 using CUDA and up to 116x on AMD HD 6950 using OpenCL. Comparison of our implementation with other GPU based registration method reveals that our implementation performs better compared to other implementations.