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Frontiers of Optoelectronics

ISSN 2095-2759

ISSN 2095-2767(Online)

CN 10-1029/TN

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Front Optoelec    2012, Vol. 5 Issue (4) : 395-399    https://doi.org/10.1007/s12200-012-0278-6
RESERACH ARTICLE
An ellipse detection method for 3D head image fusion based on color-coded mark points
Zhen GUO, Xu LIU(), Han WANG, Zhenrong ZHENG
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China
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Abstract

In this paper, we proposed a new kind of mark points coded by color and a new quasi-ellipse detector on pixel level. This method is especially applicable to three-dimensional (3D) head panoramic reconstruction. Images of adjacent perspectives can be stitched by matching pasted color-coded mark points in overlap area to calculate the transformation matrix. This paper focuses on how the color-coded mark points work and how to detect and match corresponding points from different perspectives. Tests are performed to show the efficiency and accuracy of this method based on the original data obtained by structured light projection.
Keywords color-coded mark points      quasi-ellipse point detection      mark points matching      three-dimensional (3D) multi-perspective image fusion     
Corresponding Author(s): LIU Xu,Email:liuxu@zju.edu.cn   
Issue Date: 05 December 2012
 Cite this article:   
Zhen GUO,Xu LIU,Han WANG, et al. An ellipse detection method for 3D head image fusion based on color-coded mark points[J]. Front Optoelec, 2012, 5(4): 395-399.
 URL:  
https://academic.hep.com.cn/foe/EN/10.1007/s12200-012-0278-6
https://academic.hep.com.cn/foe/EN/Y2012/V5/I4/395
Fig.1  A common kind of coded mark points
pointvalues of RGBexamplepointvalues of RGBexample
type 10 255 0type 30 255 255
type 20 0 255type 4255 0 255
Actual size: 5.0 mm
Tab.1  Parameters of color-coded mark points
Fig.2  Flow chart for distinguishing mark points
Fig.3  Original texture images from two adjacent perspectives. (a) Perspective 1; (b) perspective 2
Fig.4  Edge images of two perspectives. (a) Perspective 1; (b) perspective 2
perspective 1perspective 2
(565,263,233)(454,262,151)
(566,286,238)(454,285,163)
(617,363,204)(502,362,192)
(643,356,169)(539,355,208)
(575,477,191)(473,473,140)
(550,481,207)(419,383,42)
Tab.2  Detected corresponding mark points in each perspective
Fig.5  Reconstruction head model
perspective 1perspective 2 before transformationperspective 2 after transformationtransforming error/%
(565,263,233)(454,262,151)(558.7992,267.7609,234.4135)1.1167
(566,286,238)(454,285,163)(571.3145,289.9258,236.5492)1.1067
(617,363,204)(502,362,192)(612.2411,371.3796,209.5037)2.1167
(643,356,169)(539,355,208)(646.4414,348.9348,176.1394)2.1167
(575,477,191)(473,473,140)(576.1719,490.5505,196.7524)1.8500
(550,481,207)(419,383,42)(542.5946,484.5465,208.3486)0.7867
Tab.3  Comparison of mark point coordinates before and after transformation
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