Range estimation from camera blur by regularized adaptive identification

Lee F. Holeva

doi:10.1117/12.141765

11 March 1993 Range estimation from camera blur by regularized adaptive identification

Lee F. Holeva

Author Affiliations +

Proceedings Volume 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics; (1993) https://doi.org/10.1117/12.141765
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

Lee F. Holeva "Range estimation from camera blur by regularized adaptive identification", Proc. SPIE 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics, (11 March 1993); https://doi.org/10.1117/12.141765

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE