Improving Image Quality Using Color Intensity Modification to Determine the Ripeness of Avocado

DOI

10.2991/aer.k.211129.015How to use a DOI?

Keywords

adaptive histogram equalization; contrast stretching; GLCM; texture feature

Abstract

Image with better quality will make the next processing easier. This study aims to analyze the result of image quality improvement using color intensity modification to determine the ripeness of avocado. Color intensity modification used two methods, i.e. Histogram Equalization (HE) and Contrast Stretching (CS). The test data was 96 images 100x100 pixel in JPEG format. Image feature extraction used Gray Level Co-occurrence Matrix (GLCM) by selecting texture feature. The results of K-means clustering with two centroids showed that there is error, ranging from 2.08% to 4.17%. After improving image quality, determination of the ripeness of avocado became better. Both techniques can improve the quality or clarity of image object. The research result could be used for consideration in selecting image pre-processing method.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Budi Hartono
AU  - Yunus Anis
AU  - Veronica Lusiana
PY  - 2021
DA  - 2021/11/30
TI  - Improving Image Quality Using Color Intensity Modification to Determine the Ripeness of Avocado
BT  - Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020)
PB  - Atlantis Press
SP  - 66
EP  - 70
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.211129.015
DO  - 10.2991/aer.k.211129.015
ID  - Hartono2021
ER  -