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Proceedings Paper

Mining remote-image repositories with application to Mars Rover stereoscopic image datasets
Author(s): Andrew Willis; Waseem Shadid; Martha Cary Eppes
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Paper Abstract

As of December 2008, the two Mars rover spacecraft Spirit and Opportunity have collected more than 4 years worth of data from nine imaging instruments producing greater than 200k images which includes both raw image data from spacecraft instruments and images generated by post-processing algorithms developed by NASA's Multimission Image Processing Laboratory (MIPL). This paper describes a prototype software system that allows scientists to browse and data-mine the images produced from NASA's Mars Exploratory Rover (MER) missions with emphasis on the automatic detection of images containing rocks that are of interest for geological research. We highlight two aspects of our prototype system: (1) software design for mining remote data repositories, (2) a computationally efficient image search engine for detecting MER images that containing rocks. Datatype abstractions made at the software design level allow users to access and visualize the source data through a single simple-to-use interface when the underlying data may originate from a local or remote image repository. Data mining queries into the MER image data are specified over chronological intervals denoted (sols) as each interval is a solar day. As in other mining applications, an automatic detection and classification algorithm is used to compute a relevance score that represents how relevant a given recorded image is to the user-specified query. Query results are presented as list of records, sorted by their relevance score, which the user may then visualize and investigate to extract information of interest. Several standard image analysis tools are provided for investigation of 2D images (e.g., histogram equalization, edge detection, etc.) and, when available, stereoscopic data is integrated with the image data using multiple windows which show both the 2D image and 3D surface geometry. The combination of data mining and a high-quality visualization interface provides MER researchers unprecedented access to the recorded data.

Paper Details

Date Published: 2 February 2009
PDF: 10 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510M (2 February 2009); doi: 10.1117/12.806134
Show Author Affiliations
Andrew Willis, The Univ. of North Carolina at Charlotte (United States)
Waseem Shadid, The Univ. of North Carolina at Charlotte (United States)
Martha Cary Eppes, The Univ. of North Carolina at Charlotte (United States)

Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

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