12 August 1992 Precision imaging and control for machine vision research at Carnegie Mellon University
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Abstract
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 paper we describe the camera systems developed for Carnegie Mellon University''s Calibrated Imaging Lab
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Reg G. Willson, Steven A. Shafer, "Precision imaging and control for machine vision research at Carnegie Mellon University", Proc. SPIE 1656, High-Resolution Sensors and Hybrid Systems, (12 August 1992); doi: 10.1117/12.135913; http://dx.doi.org/10.1117/12.135913
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KEYWORDS
Cameras

Imaging systems

Systems modeling

Calibration

Data modeling

Sensors

Visual process modeling

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