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Sensors 2016, 16(1), 54; doi:10.3390/s16010054

PIMR: Parallel and Integrated Matching for Raw Data

1
Key Laboratory for Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
2
Chongqing Academy of Science and Technology, Chongqing 401123, China
3
Beijing Institute of Environmental Features, Beijing 100854, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 14 November 2015 / Revised: 29 December 2015 / Accepted: 30 December 2015 / Published: 2 January 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [8888 KB, uploaded 2 January 2016]   |  

Abstract

With the trend of high-resolution imaging, computational costs of image matching have substantially increased. In order to find the compromise between accuracy and computation in real-time applications, we bring forward a fast and robust matching algorithm, named parallel and integrated matching for raw data (PIMR). This algorithm not only effectively utilizes the color information of raw data, but also designs a parallel and integrated framework to shorten the time-cost in the demosaicing stage. Experiments show that compared to existing state-of-the-art methods, the proposed algorithm yields a comparable recognition rate, while the total time-cost of imaging and matching is significantly reduced. View Full-Text
Keywords: image sensor; raw data; image matching; image analysis image sensor; raw data; image matching; image analysis
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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Li, Z.; Yang, J.; Zhao, J.; Han, P.; Chai, Z. PIMR: Parallel and Integrated Matching for Raw Data. Sensors 2016, 16, 54.

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