Carnegie Mellon Robotics Institute
|This paper studies the problem of obtaining depth information from focusing and defocusing, which have long been noticed as important sources of depth information for human and machine vision. In depth from focusing, we try to eliminate the local maxima problem which is the main source of inaccuracy in focusing; in depth from defocusing, a new computational model is proposed to achieve higher accuracy.
The major contributions of this paper are: (1) In depth from focusing, instead of the popular Fibonacci search which is often trapped in local maxima, we propose the combination of Fibonacci search and curve fitting, which leads to an unprecedentedly accurate result; (2) New model of the blurring effect which takes the geometric blurring as well as the imaging blurring into consideration, and the calibration of the blurring model; (3) In spectrogram-based depth from defocusing, an iterative estimation method is proposed to decrease or eliminate the window effect.
This paper reports focus ranging with less than 1/1000 error and the defocus ranging with about 1/200 error. With this precision, depth from focus ranging is becoming competitive with stereo vision for reconstructing 3D depth information.
Sponsor: USAF-Wright Patterson
Grant ID: F33615-90-C-1465, ARPA Order No. 7597
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): Calibrated Imaging Lab
Associated Project(s): Depth From Focus and Defocus
Number of pages: 23
|Yalin Xiong and Steven Shafer, "Depth from Focusing and Defocusing," tech. report CMU-RI-TR-93-07, Robotics Institute, Carnegie Mellon University, March, 1993|
author = "Yalin Xiong and Steven Shafer",
title = "Depth from Focusing and Defocusing",
booktitle = "",
institution = "Robotics Institute",
month = "March",
year = "1993",
address= "Pittsburgh, PA",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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