Artificial Vision Techniques to Optimize Strawberry's Industrial Classification
| Autor: | Edwin P. Pruna, Fausto Acuna, Ivón Escobar, Patricia Constante, Oscar Chang, Andres Gordon |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
General Computer Science
Artificial neural network Computer science Orientation (computer vision) Machine vision business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing 04 agricultural and veterinary sciences 02 engineering and technology Real image 040401 food science 0404 agricultural biotechnology Feature (computer vision) Region of interest 0202 electrical engineering electronic engineering information engineering Canny edge detector 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | IEEE Latin America Transactions. 14:2576-2581 |
| ISSN: | 1548-0992 |
| DOI: | 10.1109/tla.2016.7555221 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|