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This paper has proposed an architecture of optimised SIFT (scale invariant feature transform) feature detection for an FPGA implementation of an image matcher. In order for SIFT based image matcher to be implemented on an FPGA efficiently, in terms of speed and hardware resource usage, the original SIFT algorithm has been significantly optimised in the following aspects: 1) upsampling has been replaced...
Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by Biomimetic Pattern Recognition on information geometry. Biomimetic Pattern Recognition is a new model of Pattern Recognition based on “matter cognition” instead of “matter classification”. This new model is much closer to the function of human being, than traditional statistical...
Biomimetic Pattern Recognition is a new model of Pattern Recognition based on ldquomatter cognitionrdquo instead of ldquomatter classificationrdquo. This new model is much closer to the function of human being, than traditional statistical Pattern Recognition using ldquooptimal separatingrdquo as its main principle.But it has been investigated in 2D sample space, In this paper, we extend to space...
This paper parallelizes and characterizes an important computer vision application -Scale Invariant Feature Transform (SIFT) both on a Symmetric Multiprocessor (SMP) platform and a large scale Chip Multiprocessor (CMP) simulator. SIFT is an approach for extracting distinctive invariant features from images and has been widely applied. In many computer vision problems, a real-time or even super-real-time...
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