P^2: a robust and rotationally invariant shape descriptor with applications to mesh saliency  

作　　者：LIU Xian-yong MA Li-zhuang LIU Li-gang 

机构地区：[1]School of Computer Science and Technology, Shanghai Jiao Tong University

出　　处：《Applied Mathematics(A Journal of Chinese Universities)》2016年第1期53-67,共15页高校应用数学学报（英文版）（B辑）

基　　金：Supported by China Scholarship Council(201206230015);China NSFC Key Project(61133009);the National 973 Program of China(2011CB302203)

摘　　要：This work presents a robust and rotationally invariant shape descriptor, namely perception pronouncement （called p2）, to mathematically model the eye fixations, p2 takes two criteria - the local consideration of surface curvature and the global consideration of view- independent visibility - into account. Differing from existing works that often computed the intrinsic surface property of visibility in imaging space, a novel approach is proposed to approxi- mate the attribute in object space using Gauss map and Ray tracing. With the presented shape descriptor, mesh saliency detection, which refers to reasoning about which regions or points of a surface axe important, is more sensible, especially when 3D models fall into two categories： （1） the models possess significant interior/exterior structures; （2） the models contain regions where the contrast in visibility is high. For the models that are out of the categories, saliencies achieved by our approach are comparable to or even better than those of state-of-the-axt methods.This work presents a robust and rotationally invariant shape descriptor, namely perception pronouncement （called p2）, to mathematically model the eye fixations, p2 takes two criteria - the local consideration of surface curvature and the global consideration of view- independent visibility - into account. Differing from existing works that often computed the intrinsic surface property of visibility in imaging space, a novel approach is proposed to approxi- mate the attribute in object space using Gauss map and Ray tracing. With the presented shape descriptor, mesh saliency detection, which refers to reasoning about which regions or points of a surface axe important, is more sensible, especially when 3D models fall into two categories： （1） the models possess significant interior/exterior structures; （2） the models contain regions where the contrast in visibility is high. For the models that are out of the categories, saliencies achieved by our approach are comparable to or even better than those of state-of-the-axt methods.

关 键 词：Human visual system mesh saliency shape descriptor bilateral filtering visibility. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]