Image compensation for camera and lighting variability

With the current trend of integrating machine vision systems in industrial manufacturing and inspection applications comes the issue of camera and illumination stabilization. Unless each application is built around a particular camera and highly controlled lighting environment, the interchangeability of cameras of fluctuations in lighting become a problem as each camera usually has a different response. An empirical approach is proposed where color tile data is acquired using the camera of interest, and a mapping is developed to some predetermined reference image using neural networks. A similar analytical approach based on a rough analysis of the imaging systems is also considered for deriving a mapping between cameras. Once a mapping has been determined, all data from one camera is mapped to correspond to the images of the other prior to performing any processing on the data. Instead of writing separate image processing algorithms for the particular image data being received, the image data is adjusted based on each particular camera and lighting situation. All that is required when swapping cameras is the new mapping for the camera being inserted. The image processing algorithms can remain the same as the input data has been adjusted appropriately. The results of utilizing this technique are presented for an inspection application.