A review of light intensity control and quick optimum search in machine vision

Optimum searching methods were applied to find optimal illumination of a single color to enhance the quality of a monochrome image and reduce searching time. The problem was defined as 1D searching optimization between light intensity and image sharpness. The image sharpness is the degree of distinctness and is a non-linear function of the input voltage, which can be used to adjust the light intensity. We considered conventional optimum search methods, such as steepest descent, conjugate gradient, Newton´s method, bisection, and golden section. These derivative methods and direct searches were tested for a sample pattern using single color lights under coaxial illumination. The iteration for the optimal condition was 6.7% of full scanning in average. The test results show that the steepest descent and golden search are recommended. We checked the possibility of applying these search methods to automatic lighting in machine vision.