Fast SIFT design for real-time visual feature extraction

Liang Chi Chiu, Tian-Sheuan Chang, Jiun Yen Chen, Nelson Yen Chung Chang

研究成果: Article同行評審

80 引文 斯高帕斯(Scopus)

摘要

Visual feature extraction with scale invariant feature transform (SIFT) is widely used for object recognition. However, its real-time implementation suffers from long latency, heavy computation, and high memory storage because of its frame level computation with iterated Gaussian blur operations. Thus, this paper proposes a layer parallel SIFT (LPSIFT) with integral image, and its parallel hardware design with an on-the-fly feature extraction flow for real-time application needs. Compared with the original SIFT algorithm, the proposed approach reduces the computational amount by 90% and memory usage by 95%. The final implementation uses 580-K gate count with 90-nm CMOS technology, and offers 6000 feature points/frame for VGA images at 30 frames/s and ∼2000 feature points/frame for 1920×1080 images at 30 frames/s at the clock rate of 100 MHz.

原文English
文章編號6507640
頁(從 - 到)3158-3167
頁數10
期刊IEEE Transactions on Image Processing
22
發行號8
DOIs
出版狀態Published - 21 六月 2013

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