G. Seetharaman, and
Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing),
Image matching has been a critical research topic in many computer vision applications, such as stereo vision, feature tracking, motion tracking, image registration and mosaicing, object recognition, and 3D reconstruction. Normalized Cross Correlation (NCC) is a template-based image matching approach which is invariant to linear brightness and contrast variations. As a first step in mosaicing, we use NCC to a great extent for matching images which is an expensive and time consuming operation. Thus, an attempt is made to implement NCC in GPU and multi-CPU to improve the execution time for real-time applications. We also show performance differences for our different parallelization techniques: dense and sparse NCC. Finally, we compare the enhancement in performance and efficiency in timing by switching NCC implementation from CPU to GPU.
author = "A. Nafis and E. Teters and R. Aktar and G. Seetharaman and K. Palaniappan",
title = "GPU and multi-threaded CPU enabled normalized cross correlation",
year = 2020,
journal = "Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing)",
month = "Apr",
doi = "10.1117/12.2561529",
url = "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11398/1139805/GPU-and-multi-threaded-CPU-enabled-normalized-cross-correlation/10.1117/12.2561529.short?SSO=1"
A. Nafis, E. Teters, R. Aktar, G. Seetharaman, and K. Palaniappan. GPU and multi-threaded CPU enabled normalized cross correlation. Proc. SPIE Conf. Geospatial InfoFusion X (Defense + Commercial Sensing), April 2020.