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Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla |
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Multi-Image Super-Resolution for Thermal Images. |
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Conference Article |
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2022 |
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 |
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4 |
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635 - 642 |
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cidis @ cidis @ |
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181 |
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Author |
Miguel A. Murillo, Julio E. Alvia, & Miguel Realpe |
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Title |
Beyond visual and radio line of sight UAVs monitoring system through open software in a simulated environment. |
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Conference Article |
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2021 |
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The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communications in Computer and Information Science |
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1388 |
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629-642 |
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Drone, Open Source, Internet, Web Application, Web Server, SITL, Line of sight, UAV. |
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The problem of loss of line of sight when operating drones has be-come a reality with adverse effects for professional and amateur drone opera-tors, since it brings technical problems such as loss of data collected by the de-vice in one or more instants of time during the flight and even misunderstand-ings of legal nature when the drone flies over prohibited or private places. This paper describes the implementation of a drone monitoring system using the In-ternet as a long-range communication network in order to avoid the problem of loss of communication between the ground station and the device. For this, a simulated environment is used through an appropriate open software tool. The operation of the system is based on a client that makes requests to a server, the latter in turn communicates with several servers, each of which has a drone connected to it. In the proposed system when a drone is ready to start a flight, its server informs the main server of the system, which in turn gives feedback to the client informing it that the device is ready to carry out the flight; this way customers can send a mission to the device and keep track of its progress in real time on the screen of their web application. |
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cidis @ cidis @ |
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186 |
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N. Onkarappa; Cristhian A. Aguilera; B. X. Vintimilla; Angel D. Sappa |
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Title |
Cross-spectral Stereo Correspondence using Dense Flow Fields |
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2014 |
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Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 |
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3 |
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613 - 617 |
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Cross-spectral Stereo Correspondence, Dense Optical Flow, Infrared and Visible Spectrum |
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This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach. |
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IEEE |
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2014 International Conference on Computer Vision Theory and Applications (VISAPP) |
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cidis @ cidis @ |
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27 |
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Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon |
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Title |
Open Source system for identification of corn leaf chlorophyll contents based on multispectral images |
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2019 |
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International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador |
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572-581 |
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It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images.
Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear
regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%.
It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests.
However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1]. |
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gtsi @ user @ |
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116 |
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