NDLI: Towards reduction of the training and search running time complexities for non-rigid object segmentation

Content Provider	IEEE Xplore Digital Library
Author	Nascimento, J.C. Carneiro, G.
Copyright Year	2015
Description	Author affiliation: Australian Centre for Visual Technol., Univ. of Adelaide, Adelaide, SA, Australia (Carneiro, G.) \|\| Inst. Super. Tecnico, Inst. de Sist. e Robot., Lisbon, Portugal (Nascimento, J.C.)
Abstract	The problem of non-rigid object segmentation is formulated in a two-stage approach in Machine Learning based methodologies. In the first stage, the automatic initialization problem is solved by the estimation of a rigid shape of the object. In the second stage, the non-rigid segmentation is performed. The rational behind this strategy, is that the rigid detection can be performed at lower dimensional space than the original contour space. In this paper, we explore this idea and propose the use of manifolds to reduce even more the dimensionality of the rigid transformation space (first stage) of current state-of-the-art top-down segmentation methodologies. Also, we propose the use of deep belief networks to allow for a training process capable to produce robust appearance models. Experiments in lips segmentation from frontal face images are conducted to testify the performance of the proposed algorithm.
Starting Page	4713
Ending Page	4717
File Size	9349421
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351701
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Manifolds Image segmentation Training data Complexity theory Visualization Robustness
Content Type	Text
Resource Type	Article

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