GPU and multi-threaded CPU enabled normalized cross correlation

Nafis Ahmed; Evan Teters; Rumana Aktar; Ismail Akturk; Guna Seetharaman; Kannappan Palaniappan

doi:10.1117/12.2561529

27 April 2020 GPU and multi-threaded CPU enabled normalized cross correlation

Nafis Ahmed, Evan Teters, Rumana Aktar, Ismail Akturk, Guna Seetharaman, Kannappan Palaniappan

Proceedings Volume 11398, Geospatial Informatics X; 1139805 (2020) https://doi.org/10.1117/12.2561529
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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, 3D reconstruction, etc. 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 in order to improve execution time for real time applications. Finally we compare the enhancement in performance and efficiency in timing by switching NCC implementation from CPU to GPU.

Conference Presentation

Citation Download Citation

Nafis Ahmed, Evan Teters, Rumana Aktar, Ismail Akturk, Guna Seetharaman, and Kannappan Palaniappan "GPU and multi-threaded CPU enabled normalized cross correlation", Proc. SPIE 11398, Geospatial Informatics X, 1139805 (27 April 2020); https://doi.org/10.1117/12.2561529

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