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—Feature detection and extraction are essential in computer vision applications such as image matching and object recognition. The Scale-Invariant Feature Transform (SIFT) algorithm is one of the most robust approaches to detect and extract distinctive invariant features from images. However, high computational complexity makes it difficult to apply the(More)
Emerging mobile applications, such as augmented reality, demand robust feature detection at high frame rates. We present an implementation of the popular Scale-Invariant Feature Transform (SIFT) feature detection algorithm that incorporates the powerful graphics processing unit (GPU) in mobile devices. Where the usual GPU methods are inefficient on mobile(More)
Exposed-datapath architectures yield small, low-power processors that trade instruction word length for aggressive compile-time scheduling and a high degree of instruction-level parallelism. In this paper, we present a general-purpose parallel accelerator consisting of a main processor and eight symmetric clusters, all in a single core. Use of a lightweight(More)
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