Publication

Consistent depth video segmentation using adaptive surface models

Journal Article (2015)

Journal

IEEE Transactions on Cybernetics

Pages

266-278

Volume

45

Number

2

Doc link

http://dx.doi.org/10.1109/TCYB.2014.2324815

File

Download the digital copy of the doc pdf document

Abstract

We propose a new approach for the segmentation of 3-D point clouds into geometric surfaces using adaptive surface models. Starting from an initial configuration, the algorithm converges to a stable segmentation through a new iterative split-and-merge procedure, which includes an adaptive mechanism for the creation and removal of segments. This allows the segmentation to adjust to changing input data along the movie, leading to stable, temporally coherent, and traceable segments. We tested the method on a large variety of data acquired with different range imaging devices, including a structured-light sensor and a time-of-flight camera, and successfully segmented the videos into surface segments. We further demonstrated the feasibility of the approach using quantitative evaluations based on ground-truth data.

Categories

computer vision.

Author keywords

motion, range data, segmentation, shape, surface fitting

Scientific reference

F. Husain, B. Dellen and C. Torras. Consistent depth video segmentation using adaptive surface models. IEEE Transactions on Cybernetics, 45(2): 266-278, 2015.