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Author 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) Vancouver, 18-28 junio 2023 Abbreviated Journal  
  Volume (down) 2023-June Issue Pages 470 - 478  
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  ISSN 21607508 ISBN 979-835030249-3 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 210  
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Author Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich pdf  isbn
openurl 
  Title Multi-modal Aerial View Object Classification 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) Vancouver, 18-28 junio 2023 Abbreviated Journal  
  Volume (down) 2023-June Issue Pages 412 - 421  
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  ISSN 21607508 ISBN 979-835030249-3 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 212  
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Author 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 (down) 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 Rangnekar,Aneesha; Mulhollan,Zachary; Vodacek,Anthony; Hoffman,Matthew; Sappa,Angel D.; Yu,Jun et al. pdf  openurl
  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 (down) 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. pdf  url
openurl 
  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 (down) 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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  Call Number cidis @ cidis @ Serial 177  
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Author Silva Steven, Paillacho Dennys, Verdezoto Nervo, Hernandez Juan David url  openurl
  Title TOWARDS ONLINE SOCIALLY ACCEPTABLE ROBOT NAVIGATION Type Conference Article
  Year 2022 Publication IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, Abbreviated Journal  
  Volume (down) 2022-August Issue Pages 707-714  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 199  
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Author Benítez-Quintero J., Quevedo-Pinos O., Calderon, Fernanda pdf  openurl
  Title Notes on Sulfur Fluxes in Urban Areas with Industrial Activity Type Conference Article
  Year 2022 Publication 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022, Abbreviated Journal  
  Volume (down) 2022-July Issue Pages  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 201  
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Author Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla pdf  openurl
  Title Cycle generative adversarial network: towards a low-cost vegetation index estimation Type Conference Article
  Year 2021 Publication IEEE International Conference on Image Processing (ICIP 2021) Abbreviated Journal  
  Volume (down) 2021-September Issue Pages 2783-2787  
  Keywords CyclicGAN, NDVI, near infrared spectra, instance normalization.  
  Abstract This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.  
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  Call Number cidis @ cidis @ Serial 164  
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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 pdf  isbn
openurl 
  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 (down) 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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  Series Volume Series Issue Edition  
  ISSN 21607508 ISBN 978-172819360-1 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 123  
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Author Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa. pdf  isbn
openurl 
  Title Deep Learning based Corn Kernel Classification. Type Conference Article
  Year 2020 Publication The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020) Abbreviated Journal  
  Volume (down) 2020-June Issue 9150684 Pages 294-302  
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  Abstract This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based

approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered.

As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and

the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline.
 
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  Series Volume Series Issue Edition  
  ISSN 21607508 ISBN 978-172819360-1 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 124  
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