SRI-SURF: A better SURF powered by scaled-RAM interpolator on FPGA

Jia, Xijie; Guo, Ke; Wang, Wenqiang; Wang, Yu; Yang, Huazhong

doi:10.1109/fpl.2016.7577363

Cited by 4 publications

(2 citation statements)

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“…However, GPU acceleration approach is very power consuming, and some robots, like a hover, may not be allowed to carry a GPU hosted in a PC or notebook computer. Other acceleration approaches are based on FPGA or ASIC have been proposed [12][13][14][15]. The design that implements SURF detection and description achieves a speed performance of 72FPS on Stratix III @1080P [14].…”

Section: Introductionmentioning

confidence: 99%

“…Other acceleration approaches are based on FPGA or ASIC have been proposed [12][13][14][15]. The design that implements SURF detection and description achieves a speed performance of 72FPS on Stratix III @1080P [14]. In [16], a complete hardware acceleration system which include FAST feature point detection, BRIEF description and matching reaches 308FPS on Zynq @640  480.But robustness of FAST feature points is weaker than that of SURF.…”

Section: Introductionmentioning

confidence: 99%

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Distribution-aware FAST feature point extraction and matching based on FPGA

Jin,

Wang,

Zhang

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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Image feature extraction and matching are fundamental but computation intensive tasks in machine vision. This paper proposes a novel FPGA-based embedded system to accelerate feature extraction and matching. It implements SURF feature point detection and BRIEF feature descriptor construction and matching. For binocular stereo vision, feature matching includes both tracking matching and stereo matching, which simultaneously provide feature point correspondences and parallax information. The proposed design can process binocular video at high frame rate (640 x 480 @ 162fps). Different from similar works, the proposed approach considers feature point distribution in hardware design, and can homogenlize feature points over the image on the fly, and impacts on following feature point matching as well as homography transform matrix calculation are also evaluated. Experiment results demonstrate that our approach can reduce feature point matching overhead, and has no much adverse effect on holography projection.

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