비초점화 영상에서 정칙화법을 이용한 깊이 및 거리 계측

On the Measurement of the Depth and Distance from the Defocused Imagesusing the Regularization Method

One of the ways to measure the distance in the computer vision is to use the focus and defocus. There are two methods in this way. The first method is caculating the distance from the focused images in a point (MMDFP: the method measuring the distance to the focal plane). The second method is to measure the distance from the difference of the camera parameters, in other words, the apertures of the focal planes, of two images with having the different parameters (MMDCI: the method to measure the distance by comparing two images). The problem of the existing methods in MMDFP is to decide the thresholding vaue on detecting the most optimally focused object in the defocused image. In this case, it could be solved by comparing only the error energy in 3x3 window between two images. In MMDCI, the difficulty is the influence of the deflection effect. Therefor, to minimize its influence, we utilize two differently focused images instead of different aperture images in this paper. At the first, the amount of defocusing between two images is measured through the introduction of regularization and then the distance from the camera to the objects is caculated by the new equation measuring the distance. In the results of simulation, we see the fact to be able to measure the distance from two differently defocused images, and for our approach to be robuster than the method using the different aperture in the noisy image.