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| Author | Morocho-Cayamcela, M.E. | ||||
| Title | Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling | Type | Journal Article | ||
| Year | 2020 | Publication  |
Electronic Letters on Computer Vision and Image Analysis (ELCVIA) | Abbreviated Journal | |
| Volume | Vol. 19 | Issue | Issue 2 | Pages | pp. 17-21 |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 140 | ||
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| Author | Patricia L. Suarez | ||||
| Title | Procesamiento y representación de imágenes multiespectrales usando técnicas de aprendizaje profundo (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). Ph.D. thesis. | Type | Book Chapter | ||
| Year | 2020 | Publication |
Ediciones FIEC-ESPOL. | Abbreviated Journal | |
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| Corporate Author | Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector. | Thesis | |||
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| Language | Español | Summary Language | Original Title | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 144 | ||
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| Author | Stalin Francis Quinde | ||||
| Title | Un nuevo modelo BM3D-RNCA para mejorar la estimación de la imagen libre de ruido producida por el método BM3D. (Ph.D. Angel Sappa, Director.). M.Sc. thesis | Type | Book Chapter | ||
| Year | 2019 | Publication |
Ediciones FIEC-ESPOL | Abbreviated Journal | |
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| Corporate Author | Ph.D. Angel Sappa, Director. | Thesis | Master's thesis | ||
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| Language | Español | Summary Language | Original Title | ||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 117 | ||
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| Author | Shendry Rosero Vásquez | ||||
| Title | Reconocimiento facial: técnicas tradicionales y técnicas de aprendizaje profundo, un análisis. (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). M.Sc. thesis | Type | Book Chapter | ||
| Year | 2019 | Publication |
Ediciones FIEC-ESPOL | Abbreviated Journal | |
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| Corporate Author | Ph.D. Angel Sappa, Director de tesis & Ph.D. Boris Vintimilla, Codirector | Thesis | Master's thesis | ||
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| Language | Español | Summary Language | Original Title | ||
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| Notes | Approved | yes | |||
| Call Number | gtsi @ user @ | Serial | 114 | ||
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| Author | Roberto Jacome Galarza. | ||||
| Title | Multimodal deep learning for crop yield prediction. | Type | Conference Article | ||
| Year | 2022 | Publication |
Doctoral Symposium on Information and Communication Technologies –DSICT 2022. Octubre 12-14. | Abbreviated Journal | |
| Volume | 1647 | Issue | Communicationsin Computer and Infor | Pages | 106-117 |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 193 | ||
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| Author | Rangnekar,Aneesha; Mulhollan,Zachary; Vodacek,Anthony; Hoffman,Matthew; Sappa,Angel D.; Yu,Jun et al. | ||||
| Title | Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. | Type | Conference Article | ||
| Year | 2022 | Publication |
Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. | Abbreviated Journal | CONFERENCE |
| Volume | 2022-June | Issue | Pages | 389-397 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 176 | ||
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| Author | Low S., Inkawhich N., Nina O., Sappa A. and Blasch E. | ||||
| Title | Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. | Type | Conference Article | ||
| Year | 2022 | Publication |
Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. | Abbreviated Journal | CONFERENCE |
| Volume | 2022-June | Issue | Pages | 417-425 | |
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| Abstract | This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. This year’s MAVOC challenge is the second iteration. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) input modalities. Teams are encouraged/challenged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal models. Using the same UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two techniques, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform our baseline. For SAR classification, the top team showed a 129% improvement over our baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021. |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 177 | ||
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| Author | Armin Mehri; Angel D. Sappa | ||||
| Title | Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples | Type | Conference Article | ||
| Year | 2019 | Publication |
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States | Abbreviated Journal | |
| Volume | Issue | Pages | 971-979 | ||
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| Abstract | This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation metrics—and qualitatively evaluated showing considerable improvements with respect to the state of the art |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 105 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Image Vegetation Index through a Cycle Generative Adversarial Network | Type | Conference Article | ||
| Year | 2019 | Publication |
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States | Abbreviated Journal | |
| Volume | Issue | Pages | 1014-1021 | ||
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| Abstract | This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 106 | ||
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| Author | A. Amato; F. Lumbreras; Angel D. Sappa | ||||
| Title | A general-purpose crowdsourcing platform for mobile devices | Type | Conference Article | ||
| Year | 2014 | Publication |
Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 | Abbreviated Journal | |
| Volume | 3 | Issue | Pages | 211-215 | |
| Keywords | Crowdsourcing Platform, Mobile Crowdsourcing | ||||
| Abstract | This paper presents details of a general purpose micro-taskon-demand platform based on the crowdsourcing philosophy. This platformwas specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquityand iii) embedded sensors.The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks.Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and task- solver).Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way.Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications.Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform. | ||||
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| Publisher | IEEE | Place of Publication | Lisbon, Portugal | Editor | |
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | Computer Vision Theory and Applications (VISAPP), 2014 International Conference on | ||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 25 | ||
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