Automatic image segmentation using sobel operator and k-means clustering: A case study in volume measurement system for food products

Joko Siswantoro, Anton Satria Prabuwono, Azizi Abdullah, Bahari Idrus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Image segmentation is one of important step in visual inspection of food product using computer vision system. However, segmentation of food product image is not easily performed if the image has low contrast with its background or the background in acquired image is not homogeneous. This paper proposes k-means clustering combined with Sobel operator for automatic food product image segmentation. Sobel operator was used to determine region of interest (ROI) and k-means clustering was then employed to separate object and background in ROI. The area outside ROI was considered as background. The proposed method has been validated using 100 images of food product from ten different types. The validation results show that the proposed segmentation method achieves good segmentation result.

Original languageEnglish
Title of host publicationProceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages13-18
Number of pages6
ISBN (Print)9781479983865
DOIs
Publication statusPublished - 16 Feb 2016
EventInternational Conference on Science in Information Technology, ICSITech 2015 - Yogyakarta, Indonesia
Duration: 27 Oct 201528 Oct 2015

Other

OtherInternational Conference on Science in Information Technology, ICSITech 2015
CountryIndonesia
CityYogyakarta
Period27/10/1528/10/15

Fingerprint

Volume measurement
Image segmentation
food
Computer vision
Inspection
segmentation

Keywords

  • food product
  • k-means clustering
  • segmentation
  • Sobel operator

ASJC Scopus subject areas

  • Education
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems

Cite this

Siswantoro, J., Prabuwono, A. S., Abdullah, A., & Idrus, B. (2016). Automatic image segmentation using sobel operator and k-means clustering: A case study in volume measurement system for food products. In Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015 (pp. 13-18). [7407769] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSITech.2015.7407769

Automatic image segmentation using sobel operator and k-means clustering : A case study in volume measurement system for food products. / Siswantoro, Joko; Prabuwono, Anton Satria; Abdullah, Azizi; Idrus, Bahari.

Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 13-18 7407769.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Siswantoro, J, Prabuwono, AS, Abdullah, A & Idrus, B 2016, Automatic image segmentation using sobel operator and k-means clustering: A case study in volume measurement system for food products. in Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015., 7407769, Institute of Electrical and Electronics Engineers Inc., pp. 13-18, International Conference on Science in Information Technology, ICSITech 2015, Yogyakarta, Indonesia, 27/10/15. https://doi.org/10.1109/ICSITech.2015.7407769
Siswantoro J, Prabuwono AS, Abdullah A, Idrus B. Automatic image segmentation using sobel operator and k-means clustering: A case study in volume measurement system for food products. In Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 13-18. 7407769 https://doi.org/10.1109/ICSITech.2015.7407769
Siswantoro, Joko ; Prabuwono, Anton Satria ; Abdullah, Azizi ; Idrus, Bahari. / Automatic image segmentation using sobel operator and k-means clustering : A case study in volume measurement system for food products. Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 13-18
@inproceedings{75880763572d4bdbb759234eb4ad695f,
title = "Automatic image segmentation using sobel operator and k-means clustering: A case study in volume measurement system for food products",
abstract = "Image segmentation is one of important step in visual inspection of food product using computer vision system. However, segmentation of food product image is not easily performed if the image has low contrast with its background or the background in acquired image is not homogeneous. This paper proposes k-means clustering combined with Sobel operator for automatic food product image segmentation. Sobel operator was used to determine region of interest (ROI) and k-means clustering was then employed to separate object and background in ROI. The area outside ROI was considered as background. The proposed method has been validated using 100 images of food product from ten different types. The validation results show that the proposed segmentation method achieves good segmentation result.",
keywords = "food product, k-means clustering, segmentation, Sobel operator",
author = "Joko Siswantoro and Prabuwono, {Anton Satria} and Azizi Abdullah and Bahari Idrus",
year = "2016",
month = "2",
day = "16",
doi = "10.1109/ICSITech.2015.7407769",
language = "English",
isbn = "9781479983865",
pages = "13--18",
booktitle = "Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Automatic image segmentation using sobel operator and k-means clustering

T2 - A case study in volume measurement system for food products

AU - Siswantoro, Joko

AU - Prabuwono, Anton Satria

AU - Abdullah, Azizi

AU - Idrus, Bahari

PY - 2016/2/16

Y1 - 2016/2/16

N2 - Image segmentation is one of important step in visual inspection of food product using computer vision system. However, segmentation of food product image is not easily performed if the image has low contrast with its background or the background in acquired image is not homogeneous. This paper proposes k-means clustering combined with Sobel operator for automatic food product image segmentation. Sobel operator was used to determine region of interest (ROI) and k-means clustering was then employed to separate object and background in ROI. The area outside ROI was considered as background. The proposed method has been validated using 100 images of food product from ten different types. The validation results show that the proposed segmentation method achieves good segmentation result.

AB - Image segmentation is one of important step in visual inspection of food product using computer vision system. However, segmentation of food product image is not easily performed if the image has low contrast with its background or the background in acquired image is not homogeneous. This paper proposes k-means clustering combined with Sobel operator for automatic food product image segmentation. Sobel operator was used to determine region of interest (ROI) and k-means clustering was then employed to separate object and background in ROI. The area outside ROI was considered as background. The proposed method has been validated using 100 images of food product from ten different types. The validation results show that the proposed segmentation method achieves good segmentation result.

KW - food product

KW - k-means clustering

KW - segmentation

KW - Sobel operator

UR - http://www.scopus.com/inward/record.url?scp=84966508495&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84966508495&partnerID=8YFLogxK

U2 - 10.1109/ICSITech.2015.7407769

DO - 10.1109/ICSITech.2015.7407769

M3 - Conference contribution

AN - SCOPUS:84966508495

SN - 9781479983865

SP - 13

EP - 18

BT - Proceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -