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Remote Sens. 2017, 9(6), 601;

On-Board Detection and Matching of Feature Points

School of Precision Instrument & Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
GuangXi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology Guilin, Guangxi 541004, China
The Center for Remote Sensing, Tianjin University, Tianjin 300072, China
Author to whom correspondence should be addressed.
Received: 13 April 2017 / Revised: 24 May 2017 / Accepted: 9 June 2017 / Published: 13 June 2017
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This paper presents a FPGA-based method for on-board detection and matching of the feature points. With the proposed method, a parallel processing model and a pipeline structure are presented to ensure a high frame rate at processing speed, but with a low power consumption. To save the FPGA resources and increase the processing speed, a model which combines the modified SURF detector and a BRIEF descriptor, is presented as well. Three pairs of images with different land coverages are used to evaluate the performance of FPGA-based implementation. The experiment results demonstrate that (1) when the image pairs with artificial features (such as buildings and roads), the performance of FPGA-based implementation is better than those image pairs with natural features (such as woods); (2) the proposed FPGA-based method is capable of ensuring the processing speed at a high frame rate, such as the speed of can achieve 304 fps under a 100 MHz clock frequency. The speedup of the proposed implementation is about 27 times higher than that when using the PC-based implementation. View Full-Text
Keywords: onboard; detection; image matching; parallel processing; feature points onboard; detection; image matching; parallel processing; feature points

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Huang, J.; Zhou, G. On-Board Detection and Matching of Feature Points. Remote Sens. 2017, 9, 601.

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