NDLI: Depth estimation from image defocus using fuzzy logic

Content Provider	IEEE Xplore Digital Library
Author	Swain, C. Peters, A. Kawamura, K.
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. Eng., Vanderbilt Univ., Nashville, TN, USA (Swain, C.; Peters, A.; Kawamura, K.)
Abstract	A method for improving the accuracy of depth-from-defocus is presented. Fuzzy logic is combined with a depth-from-defocus technique to correct for uncertainty and imprecision in depth estimation. Two inputs to the fuzzy algorithm are the focus quality and the focal error. Focus quality is a measure of the amount of defocus in an image. Focal error is the difference in focus between corresponding points in images with different apertures. The output is the depth estimation for objects in images that may be either blurred or in focus. Experiments show that fuzzy logic significantly improves depth estimation compared to the nonfuzzy depth-from-defocus method. The estimation error using fuzzy logic is less than 1.5% over an object distance from 7 to 11 feet. Therefore, this method improves the accuracy of the depth-from-defocus method, while maintaining simplicity. This method was implemented using a standard camera lens and an ANDROX imaging board.<>
Starting Page	94
Ending Page	99
File Size	473731
Page Count	6
File Format	PDF
ISBN	078031896X
DOI	10.1109/FUZZY.1994.343711
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Focusing Cameras Lenses Stereo vision Design for disassembly Computer errors Computer vision Uncertainty Humans
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Novel Technique of Image-Based Camera Calibration in Depth-from-Defocus

Depth from defocus vs. stereo: how different really are they?

Maximum-likelihood depth-from-defocus for active vision

Depth Estimation from Defocus Images Based on Oriented Heat-Flows

Global depth from defocus with fixed camera parameters

The Reverse Projection Correlation Principle for Depth from Defocus

Depth recovery using defocus blur at infinity

A block shift-variant blur model for recovering depth from defocused images

An efficient method for monocular depth from defocus

Depth estimation from image defocus using fuzzy logic

Similar Documents

A Novel Technique of Image-Based Camera Calibration in Depth-from-Defocus

Depth from defocus vs. stereo: how different really are they?

Maximum-likelihood depth-from-defocus for active vision

Depth Estimation from Defocus Images Based on Oriented Heat-Flows

Global depth from defocus with fixed camera parameters

The Reverse Projection Correlation Principle for Depth from Defocus

Depth recovery using defocus blur at infinity

A block shift-variant blur model for recovering depth from defocused images

An efficient method for monocular depth from defocus

Depth estimation from image defocus using fuzzy logic