eprintid: 1944
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/19/44
datestamp: 2023-11-09 15:50:07
lastmod: 2023-11-09 15:50:07
status_changed: 2023-11-09 15:41:40
type: article
metadata_visibility: show
creators_name: Malik, A.S.
creators_name: Song, T.L.
creators_name: Choi, T.-S.
title: Depth map estimation based on linear regression using image focus
ispublished: pub
keywords: 3DShape recovery; Best fit; Data sets; Depth Estimation; Depth Map; Depth map estimation; Image focus; Intersection points; Laplacian operator; Least squares methods; Linear regression models; Maximum values; Shape from focus; Two-line, Estimation; Laplace equation; Least squares approximations; Mathematical operators; Shape optimization; Three dimensional, Linear regression
note: cited By 2
abstract: This article presents a method for depth estimation using image focus based on the linear regression model. Two datasets are selected for each pixel based on the maximum value which is calculated using Laplacian operator. Then linear regression model is used to find lines that approximate these datasets. The best fit lines are found using least squares method. After approximating the two lines, their intersection point is calculated, and weights are assigned to calculate the new value for the depth map. The proposed method is compared with four depth estimation algorithms. Six different objects are selected for testing the proposed method. © 2011 Wiley Periodicals, Inc.
date: 2011
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052091881&doi=10.1002%2fima.20274&partnerID=40&md5=cc45dc47e6685570ecf9e4e3bbbb3dbd
id_number: 10.1002/ima.20274
full_text_status: none
publication: International Journal of Imaging Systems and Technology
volume: 21
number: 3
pagerange: 241-246
refereed: TRUE
issn: 08999457
citation:   Malik, A.S. and Song, T.L. and Choi, T.-S.  (2011) Depth map estimation based on linear regression using image focus.  International Journal of Imaging Systems and Technology, 21 (3).  pp. 241-246.  ISSN 08999457