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Patricia L. Suárez, D. C., and Angel Sappa. (2021). Non-Homogeneous Haze Removal through a Multiple Attention Module Architecture. In 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science (Vol. 13018, pp. 178–190).
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Jacome-Galarza L.-R. (2021). Crop yield prediction utilizing multimodal deep learning. In 16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021.
Abstract: La agricultura de precisión es una práctica vital para
mejorar la producción de cosechas. El presente trabajo tiene
como objetivo desarrollar un modelo multimodal de aprendizaje
profundo que es capaz de producir un mapa de salud de
cosechas. El modelo recibe como entradas imágenes multiespectrales
y datos de sensores de campo (humedad,
temperatura, estado del suelo, etc.) y crea un mapa de
rendimiento de la cosecha. La utilización de datos multimodales
tiene como finalidad extraer patrones ocultos del estado de salud
de las cosechas y de esta manera obtener mejores resultados que
los obtenidos mediante los índices de vegetación.
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Angel D. Sappa, P. L. S., Henry O. Velesaca, Darío Carpio. (2022). Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. In 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2022), julio 20-22 (pp. 85–92).
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Rafael E. Rivadeneira, Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Thermal Image SuperResolution through Deep Convolutional Neural Network. In 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá (pp. 417–426).
Abstract: Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset
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