Skip to Main content Skip to Navigation
Conference papers

A Complete Real-Time Feature Extraction and Matching System Based on Semantic Kernels Binarized

Abstract : Feature extraction and matching is an important step in many current image and video processing algorithms. In this work, we designed and implemented an efficient feature extraction and matching system for sparse point correspondence search in stereo video. Our system is based on the recently proposed Semantic Kernels Binarized (SKB) algorithm, which showed superior performance with respect to other algorithms in our evaluation. The feature extraction stage has been prototyped in 180 nm technology and the complete system with two feature extraction pipelines (left and right view) together with the matching unit have been implemented on a Stratix IV FPGA where it delivers a performance of up to 42 frames per second on 720p video. Especially due to the high throughput of up to 25 k matched descriptors per frame, our system compares favourably with recent hardware implementations of similar algorithms.
Document type :
Conference papers
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Wednesday, October 12, 2016 - 5:40:46 PM
Last modification on : Thursday, March 5, 2020 - 5:40:11 PM
Long-term archiving on: : Saturday, February 4, 2017 - 8:32:07 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Michael Schaffner, P. A. Hager, L. Cavigelli, Z. Fang, P. Greisen, et al.. A Complete Real-Time Feature Extraction and Matching System Based on Semantic Kernels Binarized. 21th IFIP/IEEE International Conference on Very Large Scale Integration - System on a Chip (VLSI-SoC), Oct 2013, Istanbul, Turkey. pp.144-167, ⟨10.1007/978-3-319-23799-2_7⟩. ⟨hal-01380302⟩



Record views


Files downloads