CAJ | 학술논문

针对面向唇读的水平集模型在嘴唇分割中存在边界过收敛和过早收敛的问题,文中提出了一种改进的基于先验知识的水平集模型(简称为PS-Level Set)来进行嘴唇几何形状的定位.PS-Level Set模型利用改进的差值能量函数引入嘴唇形状的先验信息.在曲线演化过程中,反复比较演化曲线和先验曲线的差距,使曲线的演化形状逐渐逼近先验模型形状,从而更精确地收敛于目标物体实际轮廓.实验表明,用PS-Level Set模型定位嘴唇几何形状的准确率比用水平集模型提高了8.38%.
침대면향진독적수평집모형재취진분할중존재변계과수렴화과조수렴적문제,문중제출료일충개진적기우선험지식적수평집모형(간칭위PS-Level Set)래진행취진궤하형상적정위.PS-Level Set모형이용개진적차치능량함수인입취진형상적선험신식.재곡선연화과정중,반복비교연화곡선화선험곡선적차거,사곡선적연화형상축점핍근선험모형형상,종이경정학지수렴우목표물체실제륜곽.실험표명,용PS-Level Set모형정위취진궤하형상적준학솔비용수평집모형제고료8.38%.
In order to overcome the overconvergence and the premature convergence of lip boundary caused by the level set model for geometric shape detection, an improved level set model based on the prior shape (PS-Level Set) is proposed. In this model, the prior shape information of lip is incorporated into an improved differential energy function, and the differences between the evolution shape curve and the prior shape curve are repeatedly compared during the curve-evolving process, which enables the evolution shape to gradually approach the prior one and to converge to the target object more accurately. Experimental results show that, as compared with the conventional level set model, the proposed model improves the detection accuracy by 8.38%.