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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 |
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Title |
Thermal Image Super-Resolution Challenge – PBVS 2020 |
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Conference Article |
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Year |
2020 |
Publication |
The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) |
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2020-June |
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9151059 |
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432-439 |
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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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21607508 |
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978-172819360-1 |
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Call Number |
cidis @ cidis @ |
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123 |
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Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa. |
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Title |
Deep Learning based Corn Kernel Classification. |
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Conference Article |
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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) |
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2020-June |
Issue |
9150684 |
Pages |
294-302 |
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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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21607508 |
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978-172819360-1 |
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cidis @ cidis @ |
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124 |
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Author |
Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
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Title |
Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results – PBVS 2023 |
Type |
Conference Article |
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Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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2023-June |
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515 - 523 |
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21607508 |
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979-835030249-3 |
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cidis @ cidis @ |
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211 |
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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 |
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Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2023 |
Type |
Conference Article |
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Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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2023-June |
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470 - 478 |
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21607508 |
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979-835030249-3 |
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no |
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cidis @ cidis @ |
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210 |
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Author |
Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
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Title |
Multi-modal Aerial View Object Classification Challenge Results – PBVS 2023 |
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Conference Article |
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Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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2023-June |
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412 - 421 |
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21607508 |
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979-835030249-3 |
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no |
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Call Number |
cidis @ cidis @ |
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212 |
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Henry O. Velesaca, Steven Araujo, Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa |
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Title |
Off-the-Shelf Based System for Urban Environment Video Analytics. |
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Conference Article |
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Year |
2020 |
Publication |
The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) |
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2020-July |
Issue |
9145121 |
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459-464 |
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Greenhouse gases, carbon footprint, object detection, object tracking, website framework, off-the-shelf video analytics. |
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Abstract |
This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary
information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to
evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach. |
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21578672 |
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978-172817539-3 |
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no |
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cidis @ cidis @ |
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125 |
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Author |
Dennys Paillacho, Nayeth Solórzano, Michael Arce, María Plues & Edwin Eras |
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Title |
Advanced metrics to evaluate autistic children's attention and emotions from facial characteristics using a human robot-game interface |
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Conference Article |
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Year |
2023 |
Publication |
Communications in Computer and Information Science. 11th Conferencia Ecuatoriana de Tecnologías de la Información y Comunicación TICEC 2023 |
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1885 CCIS |
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234 - 247 |
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18650929 |
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978-303145437-0 |
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cidis @ cidis @ |
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221 |
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Author |
Steven Silva, Nervo Verdezoto, Dennys Paillacho, Samuel Millan-Norman & Juan David Hernandez |
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Title |
Online Social Robot Navigation in Indoor, Large and Crowded Environments. |
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Conference Article |
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2023 |
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IEEE International Conference on Robotics and Automation (ICRA 2023) |
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2023-May |
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9749 - 9756 |
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10504729 |
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979-835032365-8 |
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Call Number |
cidis @ cidis @ |
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206 |
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Author |
Suarez Patricia; Carpio Dario; Sappa Angel D. |
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A Deep Learning Based Approach for Synthesizing Realistic Depth Maps |
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Conference Article |
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2023 |
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 22nd International Conference on Image Analysis and Processing, ICIAP 2023 Udine 11 – 15 September 2023 |
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14234 LNCS |
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369 - 380 |
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03029743 |
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978-303143152-4 |
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cidis @ cidis @ |
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231 |
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Author |
Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco |
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Title |
Computer vision for image understanding. A comprehensive review |
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Conference Article |
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2019 |
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International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador |
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248-259 |
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Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction. |
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gtsi @ user @ |
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