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.
We describe the challenges and capabilities of implementing a fast, efficient georegistration system on low-power embedded systems. The input to this are 2-D images and refined camera metadata sensor measurements, the output are registered aerial images that can be used in image tracking and 3D reconstruction. We propose future application in real-time on-device processing, given initial speeds on embedded systems. Because the method used doesn’t rely on image-to-image feature correspondences as in other methods, the computation is significantly faster and improved upon further by GPU programming.
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