Fitting objects with implicit polynomials by deep neural network |
| |
Authors: | LIU Jingyi YU Lin SUN Linjun TONG Yuerong WU Min LI Weijun |
| |
Affiliation: | Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing 100083, China;School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China,Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing 100083, China;School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China,Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing 100083, China;School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China,Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing 100083, China;School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China,Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing 100083, China;School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China and Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing 100083, China;School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China;Shenzhen DAPU Microelectronics Co., Ltd., Shenzhen 518116, China |
| |
Abstract: | 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, MinVar, and MinMax, 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. |
| |
Keywords: | |
|
| 点击此处可从《光电子.激光》浏览原始摘要信息 |
|
点击此处可从《光电子.激光》下载全文 |
|