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27 April 2020 GPU and multi-threaded CPU enabled normalized cross correlation
Nafis Ahmed, Evan Teters, Rumana Aktar, Ismail Akturk, Guna Seetharaman, Kannappan Palaniappan
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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.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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KEYWORDS
3D vision

3D image reconstruction

3D modeling

Computer vision technology

Image registration

Machine vision

Object recognition

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