Invariant unsupervised segmentation of dismounts in depth images

Nathan S. Butler; Richard L. Tutwiler

doi:10.1117/12.2018187

23 May 2013 Invariant unsupervised segmentation of dismounts in depth images

Nathan S. Butler, Richard L. Tutwiler

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 87451B (2013) https://doi.org/10.1117/12.2018187
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

Citation Download Citation

Nathan S. Butler and Richard L. Tutwiler "Invariant unsupervised segmentation of dismounts in depth images", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451B (23 May 2013); https://doi.org/10.1117/12.2018187

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