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Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa |
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PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization |
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2019 |
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IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador |
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1-6 |
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This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different
communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic. |
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gtsi @ user @ |
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112 |
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Rangnekar,Aneesha; Mulhollan,Zachary; Vodacek,Anthony; Hoffman,Matthew; Sappa,Angel D.; Yu,Jun et al. |
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Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. |
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2022 |
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Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. |
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CONFERENCE |
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2022-June |
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389-397 |
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cidis @ cidis @ |
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176 |
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Rafael Rivadeneira, Henry Velesaca & Angel Sappa |
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Title |
Cross-Spectral Image Registration: a Comparative Study and a New Benchmark Dataset |
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2024 |
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In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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cidis @ cidis @ |
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237 |
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Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. |
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Thermal Image SuperResolution through Deep Convolutional Neural Network. |
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2019 |
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16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá |
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417-426 |
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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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gtsi @ user @ |
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103 |
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