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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Infrared Image Colorization based on a Triplet DCGAN Architecture. | Type | Conference Article | ||
| Year | 2017 | Publication | 13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) | Abbreviated Journal | |
| Volume | 2017-July | Issue | Pages | 212-217 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 62 | ||
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| Author | Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa | ||||
| Title | Deep learning based camera pose estimation in multi-view environment. | Type | Conference Article | ||
| Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
| Volume | Issue | Pages | 224-228 | ||
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| Abstract | This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. | ||||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 93 | ||
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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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| 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 | Angel Morera; Angel Sánchez; Angel D. Sappa; José F. Vélez | ||||
| Title | Robust Detection of Outdoor Urban Advertising Panels in Static Images. | Type | Conference Article | ||
| Year | 2019 | Publication | 17th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2019); Ávila, España. Communications in Computer and Information Science | Abbreviated Journal | |
| Volume | 1047 | Issue | Pages | 246-256 | |
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| Abstract | One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 107 | ||
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| Author | Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. | ||||
| 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 | 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 | ||||
| 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 | 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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| Call Number | gtsi @ user @ | Serial | 112 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| 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 | 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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 115 | ||
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| Author | Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A. | ||||
| Title | Special Issue on Autonomous Driving and Driver Assistance Systems | Type | Journal Article | ||
| Year | 2019 | Publication | In Robotics and Autonomous Systems | Abbreviated Journal | |
| Volume | 121 | Issue | Pages | ||
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| Call Number | gtsi @ user @ | Serial | 119 | ||
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| Author | Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati; Ardakani Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi | ||||
| Title | Thermal Image Super-Resolution Challenge – PBVS 2020 | Type | Conference Article | ||
| Year | 2020 | Publication | The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) | Abbreviated Journal | |
| Volume | 2020-June | Issue | 9151059 | Pages | 432-439 |
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| Abstract | This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR) which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase. |
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| Language | English | Summary Language | Original Title | ||
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| ISSN | 21607508 | ISBN | 978-172819360-1 | Medium | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 123 | ||
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