Fitting objects with implicit polynomials by deep neural network

作     者：LIU Jingyi YU Lina SUN Linjun TONG Yuerong WU Min LI Weijun LIU Jingyi;YU Lina;SUN Linjun;TONG Yuerong;WU Min;LI Weijun

作者机构：Institute of SemiconductorsChinese Academy of SciencesBeijing100083China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing TechnologyBeijing100083China School of Integrated CircuitsUniversity of Chinese Academy of SciencesBeijing100049China Shenzhen DAPU Microelectronics Co.Ltd.Shenzhen518116China 

出 版 物：《Optoelectronics Letters》 (光电子快报（英文版）)

年 卷 期：2023年第19卷第1期

页      面：60-64页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 0702[理学-物理学] 

基　　金：supported by the Key-Area Research and Development Program of Guangdong Province(No.2019B010107001) the Fund of Guangdong Support Program(No.2019TY05X071). 

主　　题：continuity constraint polynomials 

摘      要：Implicit polynomials(IPs)are considered as a powerful tool for object curve fitting tasks due to their simplicity and fewer parameters.The traditional linear methods,such as 3L,Min Var,and Min Max,often achieve good performances in fitting simple objects,but usually work poorly or even fail to obtain closed curves of complex object contours.To handle the complex fitting issues,taking the advantages of deep neural networks,we designed a neural network model continuity-sparsity constrained network(CSC-Net)with encoder and decoder structure to learn the coefficients of IPs.Further,the continuity constraint is added to ensure the obtained curves are closed,and the sparseness constraint is added to reduce the spurious zero sets of the fitted curves.The experimental results show that better performances have been obtained on both simple and complex object fitting tasks.