Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142793
Title: Area-time efficient streaming architecture for FAST and BRIEF detector
Authors: Lam, Siew-Kei
Jiang, Guiyuan
Wu, Meiqing
Cao, Bin
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Lam, S.-K., Jiang, G., Wu, M., & Cao, B. (2019). Area-time efficient streaming architecture for FAST and BRIEF detector. IEEE Transactions on Circuits and Systems—II: Express Briefs, 66(2), 282-286. doi:10.1109/TCSII.2018.2846683
Journal: IEEE Transactions on Circuits and Systems—II: Express Briefs
Abstract: The combination of features from an accelerated segment test (FAST) corners and binary robust independent elementary feature (BRIEF) descriptors provide highly robust image features. We present a novel detector for computing the FAST-BRIEF features from streaming images. To reduce the complexity of the BRIEF descriptor, we employ an optimized adder tree to perform summation by accumulation on streaming pixels for the smoothing operation. Since the window buffer used in existing designs for computing the BRIEF point-pairs are often poorly utilized, we propose an efficient sampling scheme that exploits register reuse to minimize the number of registers. Synthesis results based on 65-nm CMOS technology show that the proposed FAST-BRIEF core achieves over 40% reduction in area-delay product compared to the baseline design. In addition, we show that the proposed architecture can achieve 1.4× higher throughput than the baseline architecture with slightly lower energy consumption.
URI: https://hdl.handle.net/10356/142793
ISSN: 1549-7747
DOI: 10.1109/TCSII.2018.2846683
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCSII.2018.2846683.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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