Noise sensitivity analysis of depth-from-defocus by a spatial-domain approach

Murali Subbarao; JennKwei Tyan

doi:10.1117/12.279778

7 July 1997 Noise sensitivity analysis of depth-from-defocus by a spatial-domain approach

Murali Subbarao, JennKwei Tyan

Proceedings Volume 3174, Videometrics V; (1997) https://doi.org/10.1117/12.279778
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

Depth-from-Defocus using the Spatial-Domain Convolution/Deconvolution Transform Method (STM) is a useful technique for 3D vision. STM involves simple local operations in the spatial domain on only two images recorded with different camera parameters (e.g. by changing lens position or changing aperture diameter). In this paper we provide a theoretical treatment of the noise sensitivity analysis of STM and verify the theoretical results with experiments. This fills an important gap in the current research literature wherein the noise sensitivity analysis of STM is limited to experimental observations. Given the image and noise characteristics, here we derive an expression for the Root Mean Square (RMS) error in lens position for focusing an object. This RMS error is useful in estimating the uncertainty in depth obtained by STM. We present the results of computer simulation experiments for different noise levels. The experiments validate the theoretical results.

Citation Download Citation

Murali Subbarao and JennKwei Tyan "Noise sensitivity analysis of depth-from-defocus by a spatial-domain approach", Proc. SPIE 3174, Videometrics V, (7 July 1997); https://doi.org/10.1117/12.279778

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

Includes PDF, HTML & Video, when available