Artificial Vision Techniques to Optimize Strawberry's Industrial Classification.
This research presents novel artificial vision techniques applied to the detection of features for strawberries used in the food industry. For this purpose, a computer vision system based in artificial neural networks is used, organized as a deep architecture and trained with noise compensated learn...
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oai:localhost:28000-38662017-04-13T08:02:22Z Artificial Vision Techniques to Optimize Strawberry's Industrial Classification. Chang Tortolero, Oscar Guillermo WEBCAMS COLOR NEURAL NETWORKS This research presents novel artificial vision techniques applied to the detection of features for strawberries used in the food industry. For this purpose, a computer vision system based in artificial neural networks is used, organized as a deep architecture and trained with noise compensated learning. This combination originates a strong network - object relations which makes possible the recognition of complex strawberry features under changing conditions of lightning, size and orientation. The programming uses OpenCV libraries and fruits databases captured with a webcam. The images used to train the Artificial Neural Network are defined with canny edge detection and a moving region of interest (ROI). After training, the network recognizes important features such as shape, color and anomalies. The system has been tested in real time with real images. 2017-04-12T17:59:33Z 2017-04-12T17:59:33Z 2016 article Constante. P. et al. (2016). Artificial Vision Techniques to Optimize Strawberry's Industrial Classification. IEEE Latin America Transactions. Vol. 14. 1548-0992 http://repositorio.educacionsuperior.gob.ec/handle/28000/3866 eng DOI;10.1109/TLA.2016.7555221 closedAccess |
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WEBCAMS COLOR NEURAL NETWORKS |
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WEBCAMS COLOR NEURAL NETWORKS Chang Tortolero, Oscar Guillermo Artificial Vision Techniques to Optimize Strawberry's Industrial Classification. |
description |
This research presents novel artificial vision techniques applied to the detection of features for strawberries used in the food industry. For this purpose, a computer vision system based in artificial neural networks is used, organized as a deep architecture and trained with noise compensated learning. This combination originates a strong network - object relations which makes possible the recognition of complex strawberry features under changing conditions of lightning, size and orientation. The programming uses OpenCV libraries and fruits databases captured with a webcam. The images used to train the Artificial Neural Network are defined with canny edge detection and a moving region of interest (ROI). After training, the network recognizes important features such as shape, color and anomalies. The system has been tested in real time with real images. |
author |
Chang Tortolero, Oscar Guillermo |
author_facet |
Chang Tortolero, Oscar Guillermo |
author_sort |
Chang Tortolero, Oscar Guillermo |
title |
Artificial Vision Techniques to Optimize Strawberry's Industrial Classification. |
title_short |
Artificial Vision Techniques to Optimize Strawberry's Industrial Classification. |
title_full |
Artificial Vision Techniques to Optimize Strawberry's Industrial Classification. |
title_fullStr |
Artificial Vision Techniques to Optimize Strawberry's Industrial Classification. |
title_full_unstemmed |
Artificial Vision Techniques to Optimize Strawberry's Industrial Classification. |
title_sort |
artificial vision techniques to optimize strawberry's industrial classification. |
publishDate |
2017 |
url |
http://repositorio.educacionsuperior.gob.ec/handle/28000/3866 |
_version_ |
1634995197546856448 |
score |
11,871979 |