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High Performance Image Processing Algorithms for Current and Future Mastcam Imagers

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX16CP38P
Agency Tracking Number: 150223
Amount: $124,999.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: T8.01
Solicitation Number: N/A
Solicitation Year: 2016
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-06-10
Award End Date (Contract End Date): 2017-06-09
Small Business Information
9605 Medical Center Drive, Suite 113E
Rockville, MD 20850-3563
United States
DUNS: 000000000
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Chiman Kwan
 Principal Investigator
 (240) 505-2641
Business Contact
 Chiman Kwan
Title: Business Official
Phone: (240) 505-2641
Research Institution
 University of Tennessee
 Chiman Kwan
Min H. Kao Building, Suite 304; 1520 Middle Drive
Knoxville, TN 37996-2250
United States

 (240) 505-2641
 Domestic Nonprofit Research Organization

We propose high performance image processing algorithms that will support current and future Mastcam imagers. The algorithms fuses the acquired Mastcam stereo images at different wavelengths to generate multispectral image cubes which can then be used for both anomaly detection and rough composition estimation from relatively longer distances when compared to LIBS instrument. To address the challenge in the stereo image alignment, we propose a two-step image registration approach. The first step consists of using the well-known RANSAC (Random Sample Consensus) technique for an initial image registration. The second step uses this roughly aligned image with RANSAC and the left camera image and applies a Diffeomorphic registration process. Diffeomorphic registration is formulated as a constrained optimization problem which is solved with a step-then-correct strategy. This second step allows to reduce the registration errors to subpixel levels and makes it possible to conduct reliable anomaly detection and composition estimation analyses with the constructed multispectral image cubes. Finally, in this framework, we provide a set of both conventional and state-of-the-art anomaly detection and composition estimation techniques to be applied to the generated Mastcam multispectral image cubes for guiding the Mars rover to interesting locations.

* Information listed above is at the time of submission. *

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