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landmarks.jpl.nasa.gov

Detecting Transient Surface Features via Dynamic Landmarking

Detecting Transient Surface Features via Dynamic Landmarking, an AISRP-funded research project

http://landmarks.jpl.nasa.gov/

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Detecting Transient Surface Features via Dynamic Landmarking | landmarks.jpl.nasa.gov Reviews
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Detecting Transient Surface Features via Dynamic Landmarking, an AISRP-funded research project
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Detecting Transient Surface Features via Dynamic Landmarking | landmarks.jpl.nasa.gov Reviews

https://landmarks.jpl.nasa.gov

Detecting Transient Surface Features via Dynamic Landmarking, an AISRP-funded research project

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landmarks.jpl.nasa.gov landmarks.jpl.nasa.gov
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Detecting Transient Surface Features via Dynamic Landmarking: People

https://landmarks.jpl.nasa.gov/results.shtml

California Institute of Technology. This page contains some example results of automated landmark detection and change detection. The following two images were taken two years apart on Mars. They were analyzed independently to identify landmarks (visually salient regions) without human intervention. The selected landmarks, which are outlined in red, include dark slope streaks, ridges, and steep slopes. MOC M16-00596, June 12, 2000. MOC E15-00356, April 12, 2002. MOC M16-00596, June 12, 2000. HiRISE PSP 0...

2

Detecting Transient Surface Features via Dynamic Landmarking: People

https://landmarks.jpl.nasa.gov/people.shtml

California Institute of Technology. Principal Investigator: Landmark Identification, Change Detection. Co-Investigator: Pixel-Based Change Detection, MatchGUI. Dr Norbert Schörghofer. Co-Investigator: Dark Slope Streaks. Graduate Student Researcher (Geological Sciences): Dust Devils. Graduate Student Researcher (Geological Sciences): Dust Devils. Undergraduate Student Researcher (Physics): Dark Slope Streaks. Undergraduate Student Researcher (Art): Dark Slope Streaks.

3

Detecting Transient Surface Features via Dynamic Landmarking: Publications

https://landmarks.jpl.nasa.gov/papers.shtml

California Institute of Technology. Kiri L. Wagstaff, Gary B. Doran, Ravi Kiran, Lukas Mandrake, Norbert Schorghofer, and Alice Stanboli. "Landmark Classification and Content-Based Search for Mars Orbital Imagery.". Proceedings of the Second Planetary Data Workshop. June 2015. Download PDF. Pdf, 523 KB, CL# 15-2272). ACM Transactions on Intelligent Systems and Technology. 2012 Access via ACM digital library. 42nd Lunar and Planetary Science Conference. March 2011. View abstract. Pdf, 436 KB, CL #11-0140).

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Detecting Transient Surface Features via Dynamic Landmarking

https://landmarks.jpl.nasa.gov/index.shtml

California Institute of Technology. Detecting Transient Surface Features with Dynamic Landmarking. In contrast, our approach focuses on the image content, not just the pixels. We first analyze each image to identify visually salient "landmarks", and then compare the detected landmarks between the images to highlight changes. No image registration is required. We proceed as follows:. Compute descriptive attributes for each landmark, such as size (surface area), shape, albedo, homogeneity, etc. Using a...

5

Detecting Transient Surface Features via Dynamic Landmarking: People

https://landmarks.jpl.nasa.gov/software.shtml

California Institute of Technology. The RockIT software was developed by the OASIS. Click the screenshot at right for a larger version. Contributors to this software include Ben Bornstein. Tim Stough, and Lucas Scharenbroich. Click the screenshot at right for a larger version. This software was developed by Adnan Ansar.

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Kiri L. Wagstaff

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You might be looking for. What I Learned Today. Special issues of Machine Learning:. Machine Learning for Science and Society. Machine Learning in Space. The Kiri L. Wagstaff AI/ML Scholarship. Networked tetris game with AI players). Contact: wkiri@jpl.nasa.gov. Kiri L. Wagstaff. PhD in Computer Science, Cornell University, 2002. MS in Geological Sciences, University of Southern California, 2008. Master of Library and Information Science, San Jose State University, in progress. My research projects at JPL.

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landmarks.jpl.nasa.gov landmarks.jpl.nasa.gov

Detecting Transient Surface Features via Dynamic Landmarking

California Institute of Technology. Detecting Transient Surface Features with Dynamic Landmarking. In contrast, our approach focuses on the image content, not just the pixels. We first analyze each image to identify visually salient "landmarks", and then compare the detected landmarks between the images to highlight changes. No image registration is required. We proceed as follows:. Compute descriptive attributes for each landmark, such as size (surface area), shape, albedo, homogeneity, etc. Using a...

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