Paper Info
Title
Region-based saliency estimation for 3D shape analysis and understanding.
Abstract
The detection of salient regions is an important pre-processing step for many 3D shape analysis and understanding tasks. This paper proposes a novel method for saliency detection in 3D free form shapes. Firstly, we smooth the surface normals by a bilateral filter. Such a method is capable of smoothing the surfaces and retaining the local details. Secondly, a novel method is proposed for the estimation of the saliency value of each vertex. To this end, two new features are defined: Retinex-based Importance Feature (RIF) and Relative Normal Distance (RND). They are based on the human visual perception characteristics and surface geometry respectively. Since the vertex based method cannot guarantee that the detected salient regions are semantically continuous and complete, we propose to refine such values based on surface patches. The detected saliency is finally used to guide the existing techniques for mesh simplification, interest point detection, and overlapping point cloud registration. The comparative studies based on real data from three publicly accessible databases show that the proposed method usually outperforms five selected state of the art ones both qualitatively and quantitatively for saliency detection and 3D shape analysis and understanding.
Year
DOI
Venue
2016
10.1016/j.neucom.2016.01.012
Neurocomputing
Keywords
Field
DocType
Saliency,3D surface,Retinex,Local detail,Global geometry
Computer science,Salience (neuroscience),Interest point detection,Artificial intelligence,Bilateral filter,Computer vision,Color constancy,Pattern recognition,Smoothing,Point cloud,Machine learning,Shape analysis (digital geometry),Salient
Journal
Volume
Issue
ISSN
197
C
0925-2312
Citations 
PageRank 
References 
4
0.38
26
Authors
7
Name
Order
Citations
PageRank
Yitian Zhao124633.15
Yonghuai Liu267561.65
Yongjun Wang391.06
baogang420929.51
Jian Yang528348.62
Y. Zhao627733.44
Yongtian Wang745673.00