Detection of Distorted Meat Image for Pork Grading System
DOI:
https://doi.org/10.4186/ej.2020.24.5.237 Full articleAbstract
This paper proposes a method that detects optical distorted areas (aka bubble) in pork images. By correctly identifying and discarding the images containing the unwanted bubbles, a significant improvement of pork image classification (or pork grading) in terms of accuracy has been achieved. The proposed bubble detection method relies on a particular set of image pre-processing techniques followed by morphological and region segmentation operations and is designed to attain the highest bubble detection accuracy for the detection of distorted images. Combining the proposed method with a typical pork image classification technique, the overall classification accuracy has been obtained as high as 96%.
Keywords:
image classification, pork grading, meat grading systemAffiliations
- Bangkok University
- Bangkok University
- Bangkok University
Corresponding author: Daisy Sarma, sarma.daisy2000@gmail.com
Downloads
How to Cite
Citations
Published
Issue
Section
License
Copyright (c) 2020 Engineering Journal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with Engineering Journal agree to transfer all copyright rights in and to the above work to the Engineering Journal (EJ)'s Editorial Board so that EJ's Editorial Board shall have the right to publish the work for nonprofit use in any media or form. In return, authors retain: (1) all proprietary rights other than copyright; (2) re-use of all or part of the above paper in their other work; (3) right to reproduce or authorize others to reproduce the above paper for authors' personal use or for company use if the source and EJ's copyright notice is indicated, and if the reproduction is not made for the purpose of sale.
