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Proceedings Paper

Fruit bruise detection based on 3D meshes and machine learning technologies
Author(s): Zilong Hu; Jinshan Tang; Ping Zhang
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Paper Abstract

This paper studies bruise detection in apples using 3-D imaging. Bruise detection based on 3-D imaging overcomes many limitations of bruise detection based on 2-D imaging, such as low accuracy, sensitive to light condition, and so on. In this paper, apple bruise detection is divided into two parts: feature extraction and classification. For feature extraction, we use a framework that can directly extract local binary patterns from mesh data. For classification, we studies support vector machine. Bruise detection using 3-D imaging is compared with bruise detection using 2-D imaging. 10-fold cross validation is used to evaluate the performance of the two systems. Experimental results show that bruise detection using 3-D imaging can achieve better classification accuracy than bruise detection based on 2-D imaging.

Paper Details

Date Published: 19 May 2016
PDF: 8 pages
Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016, 98690A (19 May 2016); doi: 10.1117/12.2223336
Show Author Affiliations
Zilong Hu, Michigan Technological Univ. (United States)
Jinshan Tang, Michigan Technological Univ. (United States)
Ping Zhang, Alcorn State Univ. (United States)


Published in SPIE Proceedings Vol. 9869:
Mobile Multimedia/Image Processing, Security, and Applications 2016
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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