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Proceedings Paper

Neural network predicts carrot volume with only three images
Author(s): Federico Hahn; Sergio Sanchez
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Paper Abstract

A mechanism turned a vision camera around a fixed carrot taking 100 images of it. A 3D reconstruction finite element algorithm reproduced the volume using finite area triangles and morphological operations to optimize memory utilization. Volume from several carrots were calculated and correlated against real volume achieving a 98% success rate. Three images 120 degrees apart were acquired and the main features extracted. A neural network system was trained using the features, increasing the measuring speed and obtaining together with a regression algorithm an accuracy of 95% in predicting the real volume.

Paper Details

Date Published: 25 September 1998
PDF: 4 pages
Proc. SPIE 3545, International Symposium on Multispectral Image Processing (ISMIP'98), (25 September 1998); doi: 10.1117/12.323576
Show Author Affiliations
Federico Hahn, CIAD-Culiacan (Mexico)
Sergio Sanchez, CIAD-Culiacan (Mexico)

Published in SPIE Proceedings Vol. 3545:
International Symposium on Multispectral Image Processing (ISMIP'98)
Ji Zhou; Anil K. Jain; Tianxu Zhang; Yaoting Zhu; Mingyue Ding; Jianguo Liu, Editor(s)

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