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Proceedings Paper

Range estimation from camera blur by regularized adaptive identification
Author(s): Lee F. Holeva
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Paper 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 uniform white light. Depth from defocus 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. When the camera's point spread function is shift varying, an adaptive algorithm is developed in the spatial domain. Data is acquired from line scan cameras. Only a single range measurement or a single depth profile is extracted.

Paper Details

Date Published: 11 March 1993
PDF: 14 pages
Proc. SPIE 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics, (11 March 1993); doi: 10.1117/12.141765
Show Author Affiliations
Lee F. Holeva, United Parcel Service Research & Development (United States)


Published in SPIE Proceedings Vol. 1964:
Applications of Artificial Intelligence 1993: Machine Vision and Robotics
Kim L. Boyer; Louise Stark, Editor(s)

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