research-article

Ruler Detection for Automatic Scaling of Fish Images

Authors:

D. A. Konovalov,

J. A. Domingos,

D. R. JerryAuthors Info & Claims

ICAIP '17: Proceedings of the International Conference on Advances in Image Processing

Pages 90 - 95

https://doi.org/10.1145/3133264.3133271

Published: 25 August 2017 Publication History

Abstract

Fast and low-cost image collection and processing is often required in aquaculture farms for quality/size attributes and breeding programs. For example, the absolute physical dimensions of fish (in millimeters or inches) could be estimated from electronic images. The absolute scale of the photographed fish is often unknown or requires additional hardware, data-collection and/or management overheads. One cost and time effective solution is to capture the absolute scale (in pixels-per-millimeter or dots-per-inch) by including a measuring ruler in the photographed scene. To assist that type of workflow, this paper presents a relatively simple image-processing algorithm that automatically located a sufficiently large section of the ruler in a given image. The algorithm utilized the Fast Fourier Transform and was designed to be free from adjustable parameters and therefore did not require training. The algorithm was tested on 445 images of Barramundi (Asian sea bass, Lates calcarifer), where a millimeter-graded ruler was included in each image. The algorithm achieved precision of 98% (on the original, 10, 20, 70, 80 90 degree rotated images) and 95-96% on 40, 50, 60 degree rotated images. The test Barramundi images were released to public domain (on this publication) via https://github.com/dmitryako/BarraRulerDataset445.

References

[1]

Hong, H., Yang, X., You, Z., and Cheng, F. 2014. Visual quality detection of aquatic products using machine vision. Aquacult. Eng. 63, 62--71. DOI= http://dx.doi.org/10.1016/j.aquaeng.2014.10.003.

[2]

Miranda, J. M. and Romero, M. 2017. A prototype to measure rainbow trout's length using image processing. Aquacult. Eng. 76, 41--49. DOI= http://dx.doi.org/10.1016/j.aquaeng.2017.01.003.

[3]

Zion, B., Shklyar, A., and Karplus, I. 2000. In-vivo fish sorting by computer vision. Aquacult. Eng. 22, 165-- 179. DOI= http://dx.doi.org/10.1016/S0144-8609(99)00037-0.

[4]

Saberioon, M., Gholizadeh, A., Cisar, P., Pautsina, A., and Urban, J. 2016. Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues. Rev. Aquacult. 0, 1--19. DOI= http://dx.doi.org/10.1111/raq.12143.

[5]

Zion, B. 2012. The use of computer vision technologies in aquaculture a review. Comput. Electron. Agr. 88, 125--132. DOI= http://dx.doi.org/10.1016/j.compag.2012.07.010.

Digital Library

[6]

Costa, C., Antonucci, F., Boglione, C., Menesatti, P., Van- deputte, M., and Chatain, B. 2013. Automated sorting for size, sex and skeletal anomalies of cultured seabass using external shape analysis. Aquacult. Eng. 52, 58--64. DOI= http://dx.doi.org/10.1016/j.aquaeng.2012.09.001.

[7]

Viazzi, S., Van Hoestenberghe, S., Goddeeris, B.M., and Berckmans, D. 2015. Automatic mass estimation of Jade perch Scortum barcoo by computer vision. Aquacult. Eng. 64, 42--48. DOI= http://dx.doi.org/10.1016/j.aquaeng.2014.11.003.

[8]

Bhalerao, A. and Reynolds, G. 2014. Ruler detection for autoscaling forensic images. Int. J. Digit. Crime Forensics, 6, 9--27. DOI= http://dx.doi.org/10.4018/ijdcf.2014010102.

Digital Library

[9]

Ueda, K., Baba, T., Nakagawa, Y., and Amano, K. 2005. Detection of scale intervals in digital images, 21st Int. Conf. Data Engineering Workshops. ICDEW'05. Tokyo, Japan, 1232--1232. DOI= http://dx.doi.org/10.1109/ICDE.2005.211.

Digital Library

[10]

Zambanini, S., Herrmann, M., and Kampel, M. 2013. An Automatic Method to Determine the Diameter of Historical Coins in Images. Scientific Computing and Cultural Heritage, Springer, Berlin, Heidelberg, 99--106. DOI= http://dx.doi.org/10.1007/978-3-642-28021-4_11.

[11]

Harris, F. J. 1978. On the use of windows for harmonic analysis with the discrete Fourier transform. P. IEEE 66, 51--83. DOI= http://dx.doi.org/10.1109/PROC.1978.10837.

[12]

Carson, C., Belongie, S., Greenspan, H. and Malik, J. 2002. Blobworld: image segmentation using expectation- maximization and its application to image querying. IEEE T. Pattern Anal. 24, 1026--1038. DOI= http://dx.doi.org/10.1109/TPAMI.2002.1023800.

Digital Library

[13]

Schneider, C. A., Rasband, W. S., and Eliceiri, K. W. 2012. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods, 9, 671--675. DOI= http://dx.doi.org/10.1038/nmeth.2089.

[14]

Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861--874. DOI= http://dx.doi.org/10.1016/j.patrec.2005.10.010.

Digital Library

[15]

Konovalov, D. A. Llewellyn, L.E., Vander Heyden, Y., and Coomans, D. 2008. Robust cross-validation of linear regression QSAR models. J. Chem. Inf. Model. 48, 2081--2094. DOI= http://dx.doi.org/10.1021/ci800209k.

Cited By

Zariful MTashim NLim TFakhrurroja HChuprat SPutra SLiu PBasri A(2024)Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)10.1109/IPEC61310.2024.00105(581-586)Online publication date: 12-Apr-2024
https://doi.org/10.1109/IPEC61310.2024.00105
Matuzevičius D(2023)Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep LearningElectronics10.3390/electronics1224492412:24(4924)Online publication date: 7-Dec-2023
https://doi.org/10.3390/electronics12244924
Xue ZYu KPearlman PChen THua CKang CChien CTsai MWang CChaturvedi AAntani S(2022)Extraction of Ruler Markings For Estimating Physical Size of Oral Lesions2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956251(4241-4247)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956251
Show More Cited By

Index Terms

Ruler Detection for Automatic Scaling of Fish Images
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

Recommendations

Automatic Scaling of Fish Images
ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing

In aquaculture breeding programs where large numbers of fish need to be rapidly phenotyped, the absolute physical dimensions of fish (in millimeters or inches) are often required to be extracted from electronic images in order to measure the size of the ...
Ruler Detection for Autoscaling Forensic Images

The assessment of forensic photographs often requires the calibration of the resolution of the image so that accurate measurements can be taken of crime-scene exhibits or latent marks. In the case of latent marks, such as fingerprints, image calibration ...
Image dehazing using two‐dimensional canonical correlation analysis

Image dehazing is an important issue that interests both image processing and computer vision. In this study, image dehazing is modelled as an example‐based learning problem, and a novel dehazing algorithm using two‐dimensional (2D) canonical correlation ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICAIP '17: Proceedings of the International Conference on Advances in Image Processing

August 2017

223 pages

ISBN:9781450352956

DOI:10.1145/3133264

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Sultan Qaboos University: Sultan Qaboos University
USM: Universiti Sains Malaysia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICAIP 2017

ICAIP 2017: 2017 International Conference on Advances in Image Processing

August 25 - 27, 2017

Bangkok, Thailand

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
148
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zariful MTashim NLim TFakhrurroja HChuprat SPutra SLiu PBasri A(2024)Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)10.1109/IPEC61310.2024.00105(581-586)Online publication date: 12-Apr-2024
https://doi.org/10.1109/IPEC61310.2024.00105
Matuzevičius D(2023)Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep LearningElectronics10.3390/electronics1224492412:24(4924)Online publication date: 7-Dec-2023
https://doi.org/10.3390/electronics12244924
Xue ZYu KPearlman PChen THua CKang CChien CTsai MWang CChaturvedi AAntani S(2022)Extraction of Ruler Markings For Estimating Physical Size of Oral Lesions2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956251(4241-4247)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956251
Yang YXue BJesson LWylie MZhang MWellenreuther M(2021)Deep Convolutional Neural Networks for Fish Weight Prediction from Images2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ54163.2021.9653412(1-6)Online publication date: 9-Dec-2021
https://doi.org/10.1109/IVCNZ54163.2021.9653412
Sharaff AUkey KChoure RCouradiya ASinha G(2021)Automatic Dimension Detection of Fish ImagesData Driven Approach Towards Disruptive Technologies10.1007/978-981-15-9873-9_5(49-59)Online publication date: 7-Apr-2021
https://doi.org/10.1007/978-981-15-9873-9_5
Hu WWu HZhan JHsieh P(2020)Developing Intelligent Feeding Systems based on Deep LearningProceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications10.1145/3440943.3444343(1-2)Online publication date: 12-Dec-2020
https://dl.acm.org/doi/10.1145/3440943.3444343
Monkman GHyder KKaiser MVidal F(2019)Using machine vision to estimate fish length from images using regional convolutional neural networksMethods in Ecology and Evolution10.1111/2041-210X.1328210:12(2045-2056)Online publication date: 6-Nov-2019
https://doi.org/10.1111/2041-210X.13282
Konovalov DSaleh AEfremova DDomingos JJerry D(2019)Automatic Weight Estimation of Harvested Fish from Images2019 Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA47822.2019.8945971(1-7)Online publication date: Dec-2019
https://doi.org/10.1109/DICTA47822.2019.8945971
Lapico ASankupellay MCianciullo LMyers TKonovalov DJerry DToole PJones DZenger K(2019)Using Image Processing to Automatically Measure Pearl Oyster Size for Selective Breeding2019 Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA47822.2019.8945902(1-8)Online publication date: Dec-2019
https://doi.org/10.1109/DICTA47822.2019.8945902
Konovalov DDomingos JWhite RJerry D(2018)Automatic Scaling of Fish ImagesProceedings of the 2nd International Conference on Advances in Image Processing10.1145/3239576.3239595(48-53)Online publication date: 16-Jun-2018
https://dl.acm.org/doi/10.1145/3239576.3239595

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten