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Proceedings Paper

Invariant unsupervised segmentation of dismounts in depth images
Author(s): Nathan S. Butler; Richard L. Tutwiler
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Paper Abstract

This paper will describe a scene invariant method for the unsupervised segmentation of dismounts in depth images. This method can be broken into two parts: ground plane detection and spatial segmentation. The former is accomplished by using RANSAC (RANdom SAmple Consensus) to identify a ground plane in the scene. After performing contrast enhancement the Image is "sliced" into regions. Each classified region is processed by a Robert's edge detector in order to separate each object. Each output is further processed by a block of shape filters that extract the human form.

Paper Details

Date Published: 23 May 2013
PDF: 14 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451B (23 May 2013); doi: 10.1117/12.2018187
Show Author Affiliations
Nathan S. Butler, The Pennsylvania State Univ. (United States)
Richard L. Tutwiler, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)

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