Full Text:   <3999>

CLC number: TP751

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2013-09-16

Cited: 1

Clicked: 8355

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.10 P.777-784

http://doi.org/10.1631/jzus.C1300056


Curve length estimation based on cubic spline interpolation in gray-scale images


Author(s):  Zhen-xin Wang, Ji-hong Ouyang

Affiliation(s):  College of Computer Science and Technology, Jilin University, Changchun 130012, China; more

Corresponding email(s):   wzhx_cc07@mails.jlu.edu.cn, ouyj@jlu.edu.cn

Key Words:  Arc length estimation, Cubic spline interpolation, Gray-scale image, Local algorithm


Zhen-xin Wang, Ji-hong Ouyang. Curve length estimation based on cubic spline interpolation in gray-scale images[J]. Journal of Zhejiang University Science C, 2013, 14(10): 777-784.

@article{title="Curve length estimation based on cubic spline interpolation in gray-scale images",
author="Zhen-xin Wang, Ji-hong Ouyang",
journal="Journal of Zhejiang University Science C",
volume="14",
number="10",
pages="777-784",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300056"
}

%0 Journal Article
%T Curve length estimation based on cubic spline interpolation in gray-scale images
%A Zhen-xin Wang
%A Ji-hong Ouyang
%J Journal of Zhejiang University SCIENCE C
%V 14
%N 10
%P 777-784
%@ 1869-1951
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300056

TY - JOUR
T1 - Curve length estimation based on cubic spline interpolation in gray-scale images
A1 - Zhen-xin Wang
A1 - Ji-hong Ouyang
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 10
SP - 777
EP - 784
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300056


Abstract: 
This paper deals with a novel local arc length estimator for curves in gray-scale images. The method first estimates a cubic spline curve fit for the boundary points using the gray-level information of the nearby pixels, and then computes the sum of the spline segments’ lengths. In this model, the second derivatives and y coordinates at the knots are required in the computation; the spline polynomial coefficients need not be computed explicitly. We provide the algorithm pseudo code for estimation and preprocessing, both taking linear time. Implementation shows that the proposed model gains a smaller relative error than other state-of-the-art methods.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Cai, J., Walker, R., 2010. Height estimation from monocular image sequences using dynamic programming with explicit occlusions. IET Comput. Vis., 4(3):149-161.

[2]Coeurjolly, D., Klette, R., 2004. A comparative evaluation of length estimators of digital curves. IEEE Trans. Pattern Anal. Mach. Intell., 26(2):252-258.

[3]Deng, H., Himed, B., Wicks, M.C., 2006. Image feature based space-time processing for ground moving target detection. IEEE Signal Process. Lett., 13(4):216-219.

[4]Dyer, S.A., Dyer, J.S., 2001. Cubic-spline interpolation. 1. IEEE Instrum. Meas. Mag., 4(1):44-46.

[5]Foteinopoulos, P., 2009. Cubic spline interpolation to develop contours of large reservoirs and evaluate area and volume. Adv. Eng. Softw., 40(1):23-29.

[6]Freeman, H., 1961. On the encoding of arbitrary geometric configurations. IRE Trans. Electron. Comput., 10(2):260-268.

[7]Klette, R., Kovalevsky, V.V., Yip, B., 1999. Length estimation of digital curves. SPIE, 3811:117-128.

[8]Lachaud, J.O., Provençal, X., 2011. Two linear-time algorithms for computing the minimum length polygon of a digital contour. Discr. Appl. Math., 159(18):2229-2250.

[9]Sladoje, N., Lindblad, J., 2009. High-precision boundary length estimation by utilizing gray-level information. IEEE Trans. Pattern Anal. Mach. Intell., 31(2):357-363.

[10]Suhadolnik, A., Petrišič, J., Kosel, F., 2010. Digital curve length calculation by using B-spline. J. Math. Imag. Vis., 38(2):132-138.

[11]Tadrous, P.J., 2010. On the concept of objectivity in digital image analysis in pathology. Pathology, 42(3):207-211.

[12]Zhang, M., Mou, X., Zhang, L., 2011. Non-shift edge based ratio (NSER): an image quality assessment metric based on early vision features. IEEE Signal Process. Lett., 18(5):315-318.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE