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
Authors
Projects associated
SGR ROBÒTICA: Grup de recerca consolidat - Grup de Robòtica
IntellAct: Intelligent observation and execution of Actions and manipulations
CINNOVA: Modelos cinemáticos y técnicas de aprendizaje para robots de estructura innovadora
PAU+: Perception and Action in Robotics Problems with Large State Spaces
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.
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