Scene analysis using semi-supervised clustering

Peter J. Dobbins; Joseph N. Wilson

doi:10.1117/12.2304302

30 April 2018 Scene analysis using semi-supervised clustering

Peter J. Dobbins, Joseph N. Wilson

Proceedings Volume 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII; 106280A (2018) https://doi.org/10.1117/12.2304302
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

This work performs scene analysis in order to represent and understand the elements contained in a defined area under the ground. Elements of interest are the ground layer, sub-surface layers, explosive hazards, and non-explosive (clutter) objects. The scene is composed of data collected by hand-held and vehicular-mounted ground penetrating radar (GPR) devices. In previous work, we segmented scenes into super-voxels and used a Markov Random Field (MRF) to combine super-voxels into layer regions. Here, we provide users with a training tool to annotate exemplar regions in sample data. Annotations associate must-link and cannot-link regions. Semi-supervised clustering is used to implement the Probability-Based Training Realignment (PBTR) algorithm. PBTR influences region labeling and increases the accuracy of scene representation.

Conference Presentation

Citation Download Citation

Peter J. Dobbins and Joseph N. Wilson "Scene analysis using semi-supervised clustering", Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 106280A (30 April 2018); https://doi.org/10.1117/12.2304302

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