计算机科学
最大化
结构光
编码(社会科学)
相(物质)
算法
GSM演进的增强数据速率
噪音(视频)
模式识别(心理学)
光学
人工智能
数学
图像(数学)
物理
数学优化
统计
量子力学
作者
Yongchang Wang,Kai Liu,Daniel L. Lau,Qi Hao,Laurence G. Hassebrook
标识
DOI:10.1364/josaa.27.001962
摘要
Structured light illumination by means of phase shifting patterns is a widely employed method for three-dimensional (3-D) image acquisition that is robust to ambient light and object albedo but may be especially susceptible to sensor and environment noise. In this paper, we study the specific technique of phase measuring profilometry (PMP) and the maximization of a pattern's signal to noise ratio (SNR). By treating the design of an N-pattern PMP process as placing points in an N-dimensional coding space, we define a pattern's SNR in terms of a pattern set's computational length and the number of coded phase periods in the projected patterns. Then, without introducing phase ambiguities, we propose a so-called edge pattern strategy that maximizes the computational length and number of periods. Theoretically, the edge pattern technique improves the SNR by 1.2381 times when using three component patterns and by 15.5421 times when using five patterns. Experimental results further demonstrate the improved SNR of the proposed edge pattern technique such that more accurate 3-D results are achieved using fewer component patterns.
科研通智能强力驱动
Strongly Powered by AbleSci AI