RANGE ESTIMATION FROM CAMERA BLUR BY REGULARIZED ADAPTIVE IDENTIFICATION

Abstract

One of the fundamental problems of machine vision is the estimation of object depth from perceived images. This paper describes both an apparatus and the corresponding algorithms for the passive extraction of object depth. Here passive extraction implies the processing of images acquired using only the existing illumination, in this case roughly uniform white light. Depth from defocused algorithms are extremely sensitive to image variations. Regularization, the application of a priori constraints, is employed to improve the accuracy of the range measurements. When the camera’s point spread function is shift invariant, an adaptive algorithm is developed in the frequency domain. The constraints imposed upon the solution power spectrum vector vary temporally. When the camera’s point spread function is shift varying, an adaptive algorithm is developed in the spatial domain. The constraints imposed upon the solution point spread vector vary spatially. Data is acquired from line scan cameras. Only a single range measurement or a single depth profile is extracted. By relying upon the motion of the observed object on a conveyor belt, a complete range image may be generated.