Moment and Hypergeometric Filters for High Precision Computation ofFocus, Stereo and Optical Flow
International Journal of Computer Vision
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In a perfect world we would be able to use the many possible degrees of freedom in a camera system to do many useful things, such as accommodating for changes or differences in the scenes being imaged, correcting for camera behaviour that isn''t quite ideal, or measuring properties of the scene by noting how the scene''s image changes as the camera''s parameters are varied. Unfortunately the parameters that control the formation of the camera''s images often interact in complex and subtle ways that can cause unforeseen problems for machine vision tasks. To be able to effectively use multi degree of freedom camera systems we need to know how variations in the camera''s control parameters are going to cause changes in the produced images. For this we need to have good mathematical models describing the relationships between the control parameters and the parameters of the resulting images. Ideally we would like to base the form of the models on an understanding of the underlying physical processes involved, but in many cases these are either unknown or are just too complex to model. In these situations experimentation and generalized modeling techniques are necessary. To perform the experiments needed to develop and validate models and to obtain calibration data for the models we need precise automated imaging systems. In this report we describe the camera systems developed for Carnegie Mellon University''s Calibrated Imaging Lab and show how these systems have been used to develop methods for using computer-controlled cameras and lenses.