NDLI: Top-Down Segmentation of Non-rigid Visual Objects Using Derivative-Based Search on Sparse Manifolds

Content Provider	IEEE Xplore Digital Library
Author	Nascimento, J.C. Carneiro, G.
Copyright Year	2013
Description	Author affiliation: Inst. de Sist. e Robot., Inst. Super. Tecnico, Lisbon, Portugal (Nascimento, J.C.) \|\| Australian Centre for Visual Technol., Univ. of Adelaide, Adelaide, SA, Australia (Carneiro, G.)
Abstract	The solution for the top-down segmentation of non rigid visual objects using machine learning techniques is generally regarded as too complex to be solved in its full generality given the large dimensionality of the search space of the explicit representation of the segmentation contour. In order to reduce this complexity, the problem is usually divided into two stages: rigid detection and non-rigid segmentation. The rationale is based on the fact that the rigid detection can be run in a lower dimensionality space (i.e., less complex and faster) than the original contour space, and its result is then used to constrain the non-rigid segmentation. In this paper, we propose the use of sparse manifolds to reduce the dimensionality of the rigid detection search space of current state-of-the-art top-down segmentation methodologies. The main goals targeted by this smaller dimensionality search space are the decrease of the search running time complexity and the reduction of the training complexity of the rigid detector. These goals are attainable given that both the search and training complexities are function of the dimensionality of the rigid search space. We test our approach in the segmentation of the left ventricle from ultrasound images and lips from frontal face images. Compared to the performance of state-of-the-art non-rigid segmentation system, our experiments show that the use of sparse manifolds for the rigid detection leads to the two goals mentioned above.
Starting Page	1963
Ending Page	1970
File Size	1319457
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.256
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Training Image segmentation Complexity theory Visualization Vectors Search problems manifold learning Non-rigid top-down segmentation deep belief network
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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