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Velesaca, H. O., Suárez, P. L., Mira, R., & Sappa, A.D. (2021). Computer Vision based Food Grain Classification: a Comprehensive Survey. In Computers and Electronics in Agriculture Journal. (Article number 106287), Vol. 187.
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Roberto Jacome Galarza, Miguel-Andrés Realpe-Robalino, Chamba-Eras LuisAntonio, & Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco. (2019). Computer vision for image understanding. A comprehensive review. In International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador (pp. 248–259).
Abstract: Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction.
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Michael Teutsch, A. S. & R. H. (2021). Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches. Synthesis Lectures on Computer Vision, Vol. 10 No. 2, pp. 138.
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Patricia Súarez, H. V., Dario Carpio & Angel Sappa. (2023). Corn Kernel Classification From Few Training Samples. In journal Artificial Intelligence in Agriculture, Vol. 9, pp. 89–99.
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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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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2018). Cross-spectral image dehaze through a dense stacked conditional GAN based approach. In 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) (pp. 358–364).
Abstract: This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Cross-spectral Image Patch Similarity using Convolutional Neural Network. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) (pp. 1–5).
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Rafael Rivadeneira, H. V. & A. S. (2024). Cross-Spectral Image Registration: a Comparative Study and a New Benchmark Dataset. In In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024).
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Cristhian A. Aguilera, Angel D. Sappa, & Ricardo Toledo. (2017). Cross-Spectral Local Descriptors via Quadruplet Network. In Sensors Journal, Vol. 17, pp. 873.
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Patricia L. Suárez, A. D. S., Boris X. Vintimilla. (2021). Cycle generative adversarial network: towards a low-cost vegetation index estimation. In IEEE International Conference on Image Processing (ICIP 2021) (Vol. 2021-September, pp. 2783–2787).
Abstract: This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.
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