Weighted Model Components for Gradient Direction Matching in Overhead Images

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Gradient direction matching (GDM) is the main target identification algorithm used in the Image Content Engine project at Lawrence Livermore National Laboratory. GDM is a 3D solid model-based edge-matching algorithm which does not require explicit edge extraction from the source image. The GDM algorithm is presented, identifying areas where performance enhancement seems possible. Improving the process of producing model gradient directions from the solid model by assigning different weights to different parts of the model is an extension tested in the current study. Given a simple geometric model, we attempt to determine, without obvious semantic clues, if different weight values ... continued below

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Grant, C W; Nikolaev, S & Paglieroni, D W March 17, 2006.

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Gradient direction matching (GDM) is the main target identification algorithm used in the Image Content Engine project at Lawrence Livermore National Laboratory. GDM is a 3D solid model-based edge-matching algorithm which does not require explicit edge extraction from the source image. The GDM algorithm is presented, identifying areas where performance enhancement seems possible. Improving the process of producing model gradient directions from the solid model by assigning different weights to different parts of the model is an extension tested in the current study. Given a simple geometric model, we attempt to determine, without obvious semantic clues, if different weight values produce significantly better matching accuracy, and how those weights should be assigned to produce the best matching accuracy. Two simple candidate strategies for assigning weights are proposed--pixel-weighted and edge-weighted. We adjust the weights of the components in a simple model of a tractor/semi-trailer using relevance feedback to produce an optimal set of weights for this model and a particular test image. The optimal weights are then compared with pixel and edge-weighting strategies to determine which is most suitable and under what circumstances.

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PDF-file: 12 pages; size: 2 Mbytes

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  • Presented at: SPIE Defense & Security Symposium, Orlando, FL, United States, Apr 17 - Apr 21, 2006

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  • Report No.: UCRL-CONF-220030
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 891057
  • Archival Resource Key: ark:/67531/metadc884419

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  • March 17, 2006

Added to The UNT Digital Library

  • Sept. 21, 2016, 2:29 a.m.

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  • Dec. 9, 2016, 1:07 p.m.

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Grant, C W; Nikolaev, S & Paglieroni, D W. Weighted Model Components for Gradient Direction Matching in Overhead Images, article, March 17, 2006; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc884419/: accessed December 15, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.