Identification and Classification of Sashimi Food Using Multispectral Technology

Ismail Parewai, Mansur As, Tsunenori Mine, Mario Koeppen

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Food quality inspection is an essential factor in our daily lives. Food inspection is analyzing heterogeneous food data from different sources for perception, recognition, judgment, and monitoring. This study aims to provide an accurate system in image processing techniques for the inspection and classification of sashimi food damage based on detecting external data. The external texture was identified based on the visible and invisible system that was acquired using multispectral technology. We proposed the Grey Level Co-occurrence Matrix (GLCM) model for analysis of the texture features of images and the classification process was performed using Artificial Neural Network (ANN) method. This study showed that multispectral technology is a useful system for the assessment of sashimi food and the experimental also indicates that the invisible channels have the potential in the classification model, since the hidden texture features that are not clearly visible to the human eye.

本文言語英語
ホスト出版物のタイトルAPIT 2020 - 2020 2nd Asia Pacific Information Technology Conference
出版社Association for Computing Machinery
ページ66-72
ページ数7
ISBN(電子版)9781450376853
DOI
出版ステータス出版済み - 1 17 2020
イベント2nd Asia Pacific Information Technology Conference, APIT 2020 - Bali Island, インドネシア
継続期間: 1 17 20201 19 2020

出版物シリーズ

名前ACM International Conference Proceeding Series

会議

会議2nd Asia Pacific Information Technology Conference, APIT 2020
国/地域インドネシア
CityBali Island
Period1/17/201/19/20

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

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