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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  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 (down) Issue Pages 1014-1021  
  Keywords  
  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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  Call Number gtsi @ user @ Serial 106  
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Author Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. pdf  openurl
  Title Thermal Image SuperResolution through Deep Convolutional Neural Network. Type Conference Article
  Year 2019 Publication 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá Abbreviated Journal  
  Volume (down) Issue Pages 417-426  
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  Abstract 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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  Call Number gtsi @ user @ Serial 103  
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Author Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa pdf  openurl
  Title PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization Type Conference Article
  Year 2019 Publication IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador Abbreviated Journal  
  Volume (down) Issue Pages 1-6  
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  Abstract 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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  Notes Approved no  
  Call Number gtsi @ user @ Serial 112  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Image patch similarity through a meta-learning metric based approach Type Conference Article
  Year 2019 Publication 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia Abbreviated Journal  
  Volume (down) Issue Pages 511-517  
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  Abstract Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results

have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results.
 
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  Call Number gtsi @ user @ Serial 115  
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Author Xavier Soria; Edgar Riba; Angel D. Sappa pdf  isbn
openurl 
  Title Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection Type Conference Article
  Year 2020 Publication 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) Abbreviated Journal  
  Volume (down) Issue 9093290 Pages 1912-1921  
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  Abstract This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered.  
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  ISSN ISBN 978-172816553-0 Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 126  
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Author Angel D. Sappa, Patricia L. Suárez, Henry O. Velesaca, Darío Carpio pdf  openurl
  Title Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. Type Conference Article
  Year 2022 Publication 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2022), julio 20-22 Abbreviated Journal  
  Volume (down) Issue Pages 85-92  
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  Call Number cidis @ cidis @ Serial 182  
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Author Patricia L. Suarez, Dario Carpio, Angel D. Sappa and Henry O. Velesaca url  openurl
  Title Transformer based Image Dehazing. Type Conference Article
  Year 2022 Publication 16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. Abbreviated Journal  
  Volume (down) Issue Pages 148-154  
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  Call Number cidis @ cidis @ Serial 195  
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Author Gisel Bastidas-Guacho, Patricio Moreno-Vallejo, Boris Vintimilla, Angel D. Sappa pdf  isbn
openurl 
  Title Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches Type Conference Article
  Year 2023 Publication IEEE 13th International Conference on Pattern Recognition Systems ICPRS 2023, julio 4-7 Abbreviated Journal  
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  ISSN ISBN 979-835033337-4 Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 213  
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Author Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa openurl 
  Title Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach Type Conference Article
  Year 2023 Publication 17th International Conference On Signal Image Technology & Internet Based System Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 224  
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Author Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa pdf  openurl
  Title Tiny and Efficient Model for the Edge Detection Generalization Type Conference Article
  Year 2023 Publication Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 Abbreviated Journal  
  Volume (down) Issue Pages 1356 - 1365  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 229  
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