Open AccessAutomatic Leftover Weight Prediction in Tray Box Using Improved Image Segmentation Color Lighting Component
Author(s) -
Yuita Arum Sari,
Ratih Kartika Dewi,
Jaya Mahar Maligan,
Luthfi Maulana,
Sigit Adinugroho
Publication year - 2020
Publication title -
xi'nan jiaotong daxue xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 21
ISSN - 0258-2724
DOI - 10.35741/issn.0258-2724.55.1.32
Subject(s) - artificial intelligence , computer vision , ycbcr , segmentation , computer science , hsl and hsv , component (thermodynamics) , histogram equalization , color space , otsu's method , color image , tray , color balance , color histogram , image segmentation , mathematics , pattern recognition (psychology) , image processing , image (mathematics) , engineering , mechanical engineering , virus , physics , virology , biology , thermodynamics
The problem of food waste is experienced by many countries, including Indonesia. In the previous Comstock model, estimating food scraps required the expertise of the estimator, but this method has drawbacks because of subjective perspective of even skilled observers. Another weakness occurred when the observers were exhausted, which in turn negatively affected the measurement of leftover estimation. Therefore, in this paper, we propose an approach for automatic weight prediction using image processing in order to minimize the error forecasting caused by humans. Improved lighting component in image segmentation is also utilized. We apply this framework in the tray box images and estimate each compartment. Two types of tray box backgrounds are tested: gray and black backgrounds. The first part of the proposed method takes a lighting component from each color channel of LAB, HSV, YcbCr, YUV, and LUV. Each of those color channels are applied in contrast limited adaptive histogram equalization to adjust the contrast of each image. After that, the Otsu segmentation is applied, and some formulas to calculate leftover automatically are also presented. The result shows remarkable results when applied in the black background of the tray box with root mean square error around 6.67 using an L lighting component of LAB and Y lighting color component as well YcbCr and YUV. The proposed method is good for leftover forecasting since the estimation is not significantly different from one done by human observers.