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Spencer Low, O. N., Angel D. Sappa, Erik Blasch, Nathan Inkawhich. (2023). Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results – PBVS 2023. In 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition (CVPR 2023) Vancouver, 18-28 junio 2023 (Vol. 2023-June, pp. 515–523).
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Rafael E. Rivadeneira, A. D. S., Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye. (2023). Thermal Image Super-Resolution Challenge Results – PBVS 2023. In 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition (CVPR 2023) Vancouver, 18-28 junio 2023 (Vol. 2023-June, pp. 470–478).
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Spencer Low, O. N., Angel D. Sappa, Erik Blasch, Nathan Inkawhich. (2023). Multi-modal Aerial View Object Classification Challenge Results – PBVS 2023. In 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition (CVPR 2023) Vancouver, 18-28 junio 2023 (Vol. 2023-June, pp. 412–421).
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture. In 19th International Conference on Image Analysis and Processing. (pp. 287–297).
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Patricia Suarez, A. D. S. (2024). A Generative Model for Guided Thermal Image Super-Resolution. In 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2024 Rome 27 – 29 Febraury 2024 (Vol. Vol. 3: VISAPP, pp. 765–771).
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Patricia Suarez Riofrio & Angel D. Sappa. (2024). Thermal Image Synthesis: Bridging the Gap between Visible and Infrared Spectrum. In 19th International Symposium on Visual Computing 2024.
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Luis Jacome-Galarza, M. V. - C., Miguel Realpe-Robalino, Jose Benavides-Maldonado. (2021). Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. In 19th LACCEI International Multi-Conference for Engineering, Education, and Technology.
Abstract: Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field
of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most
utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.
Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud.
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Jácome Galarza, L. R. (2024). Estimation of Corn Crop Yield using Multimodal Deep Learning from Multispectral Images and Environmental Sensors. In 19ª Conferência Ibérica de Sistemas e Tecnologias de Informação; CISTI'2024.
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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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Angel J. Valencia, Roger M. Idrovo, Angel D. Sappa, Douglas Plaza G., & Daniel Ochoa. (2017). A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) (pp. 1–6).
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