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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Learning to Colorize Infrared Images Type Conference Article
  Year 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent Systems Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 58  
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  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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  Call Number cidis @ cidis @ Serial 62  
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Author (up) 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 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 (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  Title Deep Learning based Single Image Dehazing Type Conference Article
  Year 2018 Publication 14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA Abbreviated Journal  
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  Abstract This paper proposes a novel approach to remove haze

degradations in RGB images using a stacked conditional

Generative Adversarial Network (GAN). It employs a triplet

of GAN to remove the haze on each color channel independently.

A multiple loss functions scheme, applied over a

conditional probabilistic model, is proposed. The proposed

GAN architecture learns to remove the haze, using as conditioned

entrance, the images with haze from which the clear

images will be obtained. Such formulation ensures a fast

model training convergence and a homogeneous model generalization.

Experiments showed that the proposed method

generates high-quality clear images.		  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 83  
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Author (up) Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  Title Near InfraRed Imagery Colorization Type Conference Article
  Year 2018 Publication 25 th IEEE International Conference on Image Processing, ICIP 2018 Abbreviated Journal  
  Volume Issue Pages 2237-2241  
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  Abstract This paper proposes a stacked conditional Generative

Adversarial Network-based method for Near InfraRed

(NIR) imagery colorization. We propose a variant architecture

of Generative Adversarial Network (GAN) that uses multiple

loss functions over a conditional probabilistic generative model.

We show that this new architecture/loss-function yields better

generalization and representation of the generated colored IR

images. The proposed approach is evaluated on a large test

dataset and compared to recent state of the art methods using

standard metrics.1

Index Terms—Convolutional Neural Networks (CNN), Generative

Adversarial Network (GAN), Infrared Imagery colorization.		  
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  Call Number gtsi @ user @ Serial 81  
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Author (up) 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 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 (up) Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla pdf  openurl
  Title Multi-Image Super-Resolution for Thermal Images. Type Conference Article
  Year 2022 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 Abbreviated Journal  
  Volume 4 Issue Pages 635 - 642  
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  Call Number cidis @ cidis @ Serial 181  
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Author (up) Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye pdf  isbn
openurl 
  Title Thermal Image Super-Resolution Challenge Results – PBVS 2023 Type Conference Article
  Year 2023 Publication 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 Abbreviated Journal  
  Volume 2023-June Issue Pages 470 - 478  
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  Series Volume Series Issue Edition  
  ISSN 21607508 ISBN 979-835030249-3 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 210  
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Author (up) Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Jin Kim, Dogun Kim et al. pdf  url
openurl 
  Title Thermal Image Super-Resolution Challenge Results- PBVS 2022. Type Conference Article
  Year 2022 Publication Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. Abbreviated Journal CONFERENCE  
  Volume 2022-June Issue Pages 349-357  
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  Abstract This paper presents results from the third Thermal Image

Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop.

The challenge uses the same thermal image dataset as the

first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was

kept aside for testing. The evaluation tasks were to measure

the PSNR and SSIM between the SR image and the ground

truth (HR thermal noisy image downsampled by four), and

also to measure the PSNR and SSIM between the SR image

and the semi-registered HR image (acquired with another

camera). The results outperformed those from last year’s

challenge, improving both evaluation metrics. This year,

almost 100 teams participants registered for the challenge,

showing the community’s interest in this hot topic.		  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 175  
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Author (up) Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla pdf  isbn
openurl 
  Title Thermal Image Super-Resolution: a Novel Architecture and Dataset Type Conference Article
  Year 2020 Publication The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 Abbreviated Journal  
  Volume 4 Issue Pages 111-119  
  Keywords Thermal images, Far Infrared, Dataset, Super-Resolution.  
  Abstract This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large

dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal

cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal

cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.

The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty

on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach

is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are

available.		  
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  Series Volume Series Issue Edition  
  ISSN ISBN 978-989758402-2 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 121  
Permanent link to this record
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