Image reconstruction from geometric moments via cascaded digital filters

Mohd Fikree Hassan; Raveendran Paramesran

doi:10.1007/978-981-16-6018-4_11

Image reconstruction from geometric moments via cascaded digital filters

Mohd Fikree Hassan, Raveendran Paramesran

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

Abstract

Geometric moments belong to the family of non-orthogonal moments that do not have the ability to reconstruct the original image directly from it. Because of this, indirect methods are used to reconstruct images from their geometric moments. In this paper, we introduce a direct reconstruction method that uses cascaded digital filters where each digital filter operates as a subtractor. The proposed method has a less intensive computational process and has a great advantage as it can deal with geometric moments computed from any coordinate format. The proposed method is tested on N× N binary and gray-scale images, and the performance of the proposed reconstruction process is compared with the existing methods. The results show that the proposed method takes less computational time and reconstruction error when compared with the two current methods.

Original language	English
Title of host publication	Mathematical Modelling and Computational Intelligence Techniques - ICMMCIT-2021, Gandhigram, India February 10–12
Editors	P. Balasubramaniam, Kuru Ratnavelu, Grienggrai Rajchakit, G. Nagamani
Place of Publication	Singapore Singapore
Publisher	Springer-Praxis
Pages	163-178
Number of pages	16
ISBN (Electronic)	9789811660184
ISBN (Print)	9789811660177
DOIs	https://doi.org/10.1007/978-981-16-6018-4_11
Publication status	Published - 2021
Externally published	Yes
Event	International Conference on Mathematical Modelling and Computational Intelligence Techniques 2021 - Gandhigram, India Duration: 10 Feb 2021 → 12 Feb 2021 https://link.springer.com/book/10.1007/978-981-16-6018-4 (Proceedings) https://sites.google.com/view/icmmcit-2021/home (Website)

Publication series

Name	Springer Proceedings in Mathematics and Statistics
Publisher	Springer
Volume	376
ISSN (Print)	2194-1009
ISSN (Electronic)	2194-1017

Conference

Conference	International Conference on Mathematical Modelling and Computational Intelligence Techniques 2021
Abbreviated title	ICMMCIT 2021
Country/Territory	India
City	Gandhigram
Period	10/02/21 → 12/02/21
Internet address	https://link.springer.com/book/10.1007/978-981-16-6018-4 (Proceedings) https://sites.google.com/view/icmmcit-2021/home (Website)

Keywords

Digital filter
Geometric moment
Image reconstruction

Access to Document

10.1007/978-981-16-6018-4_11

Cite this

Hassan, M. F., & Paramesran, R. (2021). Image reconstruction from geometric moments via cascaded digital filters. In P. Balasubramaniam, K. Ratnavelu, G. Rajchakit, & G. Nagamani (Eds.), Mathematical Modelling and Computational Intelligence Techniques - ICMMCIT-2021, Gandhigram, India February 10–12 (pp. 163-178). (Springer Proceedings in Mathematics and Statistics; Vol. 376). Springer-Praxis. https://doi.org/10.1007/978-981-16-6018-4_11

Hassan, Mohd Fikree ; Paramesran, Raveendran. / Image reconstruction from geometric moments via cascaded digital filters. Mathematical Modelling and Computational Intelligence Techniques - ICMMCIT-2021, Gandhigram, India February 10–12. editor / P. Balasubramaniam ; Kuru Ratnavelu ; Grienggrai Rajchakit ; G. Nagamani. Singapore Singapore : Springer-Praxis, 2021. pp. 163-178 (Springer Proceedings in Mathematics and Statistics).

@inproceedings{eb8b42aa096f414aa80f59c1410a4a64,

title = "Image reconstruction from geometric moments via cascaded digital filters",

abstract = "Geometric moments belong to the family of non-orthogonal moments that do not have the ability to reconstruct the original image directly from it. Because of this, indirect methods are used to reconstruct images from their geometric moments. In this paper, we introduce a direct reconstruction method that uses cascaded digital filters where each digital filter operates as a subtractor. The proposed method has a less intensive computational process and has a great advantage as it can deal with geometric moments computed from any coordinate format. The proposed method is tested on N× N binary and gray-scale images, and the performance of the proposed reconstruction process is compared with the existing methods. The results show that the proposed method takes less computational time and reconstruction error when compared with the two current methods.",

keywords = "Digital filter, Geometric moment, Image reconstruction",

author = "Hassan, {Mohd Fikree} and Raveendran Paramesran",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Mathematical Modelling and Computational Intelligence Techniques 2021, ICMMCIT 2021 ; Conference date: 10-02-2021 Through 12-02-2021",

year = "2021",

doi = "10.1007/978-981-16-6018-4_11",

language = "English",

isbn = "9789811660177",

series = "Springer Proceedings in Mathematics and Statistics",

publisher = "Springer-Praxis",

pages = "163--178",

editor = "P. Balasubramaniam and Kuru Ratnavelu and Grienggrai Rajchakit and G. Nagamani",

booktitle = "Mathematical Modelling and Computational Intelligence Techniques - ICMMCIT-2021, Gandhigram, India February 10–12",

url = "https://link.springer.com/book/10.1007/978-981-16-6018-4, https://sites.google.com/view/icmmcit-2021/home",

}

Hassan, MF & Paramesran, R 2021, Image reconstruction from geometric moments via cascaded digital filters. in P Balasubramaniam, K Ratnavelu, G Rajchakit & G Nagamani (eds), Mathematical Modelling and Computational Intelligence Techniques - ICMMCIT-2021, Gandhigram, India February 10–12. Springer Proceedings in Mathematics and Statistics, vol. 376, Springer-Praxis, Singapore Singapore, pp. 163-178, International Conference on Mathematical Modelling and Computational Intelligence Techniques 2021, Gandhigram, India, 10/02/21. https://doi.org/10.1007/978-981-16-6018-4_11

Image reconstruction from geometric moments via cascaded digital filters. / Hassan, Mohd Fikree ; Paramesran, Raveendran.
Mathematical Modelling and Computational Intelligence Techniques - ICMMCIT-2021, Gandhigram, India February 10–12. ed. / P. Balasubramaniam; Kuru Ratnavelu; Grienggrai Rajchakit; G. Nagamani. Singapore Singapore: Springer-Praxis, 2021. p. 163-178 (Springer Proceedings in Mathematics and Statistics; Vol. 376).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - Image reconstruction from geometric moments via cascaded digital filters

AU - Hassan, Mohd Fikree

AU - Paramesran, Raveendran

PY - 2021

Y1 - 2021

N2 - Geometric moments belong to the family of non-orthogonal moments that do not have the ability to reconstruct the original image directly from it. Because of this, indirect methods are used to reconstruct images from their geometric moments. In this paper, we introduce a direct reconstruction method that uses cascaded digital filters where each digital filter operates as a subtractor. The proposed method has a less intensive computational process and has a great advantage as it can deal with geometric moments computed from any coordinate format. The proposed method is tested on N× N binary and gray-scale images, and the performance of the proposed reconstruction process is compared with the existing methods. The results show that the proposed method takes less computational time and reconstruction error when compared with the two current methods.

AB - Geometric moments belong to the family of non-orthogonal moments that do not have the ability to reconstruct the original image directly from it. Because of this, indirect methods are used to reconstruct images from their geometric moments. In this paper, we introduce a direct reconstruction method that uses cascaded digital filters where each digital filter operates as a subtractor. The proposed method has a less intensive computational process and has a great advantage as it can deal with geometric moments computed from any coordinate format. The proposed method is tested on N× N binary and gray-scale images, and the performance of the proposed reconstruction process is compared with the existing methods. The results show that the proposed method takes less computational time and reconstruction error when compared with the two current methods.

KW - Digital filter

KW - Geometric moment

KW - Image reconstruction

UR - http://www.scopus.com/inward/record.url?scp=85124282721&partnerID=8YFLogxK

U2 - 10.1007/978-981-16-6018-4_11

DO - 10.1007/978-981-16-6018-4_11

M3 - Conference Paper

AN - SCOPUS:85124282721

SN - 9789811660177

T3 - Springer Proceedings in Mathematics and Statistics

SP - 163

EP - 178

BT - Mathematical Modelling and Computational Intelligence Techniques - ICMMCIT-2021, Gandhigram, India February 10–12

A2 - Balasubramaniam, P.

A2 - Ratnavelu, Kuru

A2 - Rajchakit, Grienggrai

A2 - Nagamani, G.

PB - Springer-Praxis

CY - Singapore Singapore

T2 - International Conference on Mathematical Modelling and Computational Intelligence Techniques 2021

Y2 - 10 February 2021 through 12 February 2021

ER -

Hassan MF , Paramesran R. Image reconstruction from geometric moments via cascaded digital filters. In Balasubramaniam P, Ratnavelu K, Rajchakit G, Nagamani G, editors, Mathematical Modelling and Computational Intelligence Techniques - ICMMCIT-2021, Gandhigram, India February 10–12. Singapore Singapore: Springer-Praxis. 2021. p. 163-178. (Springer Proceedings in Mathematics and Statistics). doi: 10.1007/978-981-16-6018-4_11

Image reconstruction from geometric moments via cascaded digital filters

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Cite this