Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)

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A Study of Lab Color Space and Its Visualization

Authors
Ida Ayu Putu Febri Imawati1, *, Made Sudarma2, I Ketut Gede Darma Putra3, I Putu Agung Bayupati3
1Study Program of Doctoral Engineering Science, Faculty of Engineering, Udayana University, Denpasar, Indonesia
2Department of Electrical Engineering, Udayana University, Denpasar, Indonesia
3Department of Information Technology, Udayana University, Denpasar, Indonesia
*Corresponding author. Email: imawati.2291011018@student.unud.ac.id
Corresponding Author
Ida Ayu Putu Febri Imawati
Available Online 13 May 2024.
DOI
10.2991/978-94-6463-413-6_3How to use a DOI?
Keywords
Color space; Cielab; Color space segmentation; Image processing; Preprocessing
Abstract

With the increasing need for digital images in everyday life, images are collected through various devices such as digital cameras, cell phone cameras, and scanners. This image data will be further processed, one of which is to segment objects from the background. The technique that can be used is segmentation using the LAB color space. This technique is done by converting the image color space into LAB color space so that the object or foreground can be separated from the background. This research uses 20 random images from 3 sources: The Oxford-IIIT Pet dataset, Github Real Python material, and DeepLontar dataset. The experimental results show that The Oxford-IIIT Pet dataset and Github Real Python material have a more extended range of minimum-maximum values of L, a*, and b* components compared to DeepLontar dataset. This extended minimum-maximum value range causes the object images in The Oxford-IIIT Pet dataset and Github Real Python materials to be more visually visible (segmented) than in the DeepLontar dataset.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
Series
Advances in Computer Science Research
Publication Date
13 May 2024
ISBN
978-94-6463-413-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-413-6_3How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ida Ayu Putu Febri Imawati
AU  - Made Sudarma
AU  - I Ketut Gede Darma Putra
AU  - I Putu Agung Bayupati
PY  - 2024
DA  - 2024/05/13
TI  - A Study of Lab Color Space and Its Visualization
BT  - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
PB  - Atlantis Press
SP  - 17
EP  - 28
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-413-6_3
DO  - 10.2991/978-94-6463-413-6_3
ID  - Imawati2024
ER  -