Learning-based constitutive parameters estimation in an image sensing system with multiple mirrors
A sensing system sometimes requires a complicated optical unit consisting of multiple mirrors, in which case it is important to estimate accurately constitutive parameters of the optical unit to enhance its sensing capability. However, the parameters include generally uncertainties since the optical unit cannot avoid the fixing and aligning errors and the manufacturing tolerance of its components. Accordingly, it should construct a projective model of the complicated sensing system accurately and build up an estimation method of tangled parameters. However, it is not easy to estimate complicated constitutive parameters from an accurate model of an optical unit with multiple mirrors, and moreover, they are sometimes changed during operation due to unexpected disturbance or intermittent adjustments such as computer control zoom, auto focus, and mirror relocation. Due to these operational circumstances, it is not easy to take apart components of the assembled system and directly measure the components. Therefore, an indirect and adaptive estimation method, taking all the components into simultaneous consideration without disassembling the sensing system, is needed for calibrating the uncertain and changeable constitutive parameters. In this paper, we propose not only a generalized projective model for an optical sensing system consisting of n-mirrors and a camera with a collecting lens, but also a learning-based process using the model to estimate recursively the uncertain constitutive parameters of the optical sensing system. We also show its feasibility through a series of calibration of an optical system.
estimation image sensing system multiple mirror uncertainty learning algorithm