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Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado |
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Title |
Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. |
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2021 |
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19th LACCEI International Multi-Conference for Engineering, Education, and Technology |
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processing, software engineering, deep learning, intelligent vision systems, cloud computing. |
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Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field
of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most
utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.
Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
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English |
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cidis @ cidis @ |
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154 |
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Author |
Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla |
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Title |
Cycle generative adversarial network: towards a low-cost vegetation index estimation |
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2021 |
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IEEE International Conference on Image Processing (ICIP 2021) |
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2021-September |
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2783-2787 |
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Keywords |
CyclicGAN, NDVI, near infrared spectra, instance normalization. |
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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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cidis @ cidis @ |
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164 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla |
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Title |
Multi-Image Super-Resolution for Thermal Images. |
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2022 |
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 |
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4 |
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635 - 642 |
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cidis @ cidis @ |
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181 |
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Author |
Angel D. Sappa, Patricia L. Suárez, Henry O. Velesaca, Darío Carpio |
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Title |
Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. |
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2022 |
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16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2022), julio 20-22 |
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85-92 |
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cidis @ cidis @ |
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182 |
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Author |
Henry O. Velesaca, Patricia L. Suarez, Dario Carpio, and Angel D. Sappa |
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Title |
Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy |
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Conference Article |
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2021 |
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16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science |
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13017 |
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131-143 |
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no |
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Call Number |
cidis @ cidis @ |
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163 |
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Author |
Patricia L. Suárez, Dario Carpio, and Angel Sappa |
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Title |
Non-Homogeneous Haze Removal through a Multiple Attention Module Architecture. |
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Conference Article |
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Year |
2021 |
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16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science |
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13018 |
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178-190 |
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cidis @ cidis @ |
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162 |
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Author |
Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Jin Kim, Dogun Kim et al. |
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Title |
Thermal Image Super-Resolution Challenge Results- PBVS 2022. |
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Conference Article |
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Year |
2022 |
Publication |
Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. |
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CONFERENCE |
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2022-June |
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349-357 |
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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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Call Number |
cidis @ cidis @ |
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175 |
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Author |
Rangnekar,Aneesha; Mulhollan,Zachary; Vodacek,Anthony; Hoffman,Matthew; Sappa,Angel D.; Yu,Jun et al. |
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Title |
Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. |
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Conference Article |
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2022 |
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Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. |
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CONFERENCE |
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2022-June |
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389-397 |
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cidis @ cidis @ |
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176 |
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Author |
Low S., Inkawhich N., Nina O., Sappa A. and Blasch E. |
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Title |
Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. |
Type |
Conference Article |
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Year |
2022 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. |
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CONFERENCE |
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2022-June |
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417-425 |
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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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cidis @ cidis @ |
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177 |
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Author |
Velez R., Paredes A., Silva S., Paillacho D., and Paillacho J. |
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Title |
Implementation of a UVC lights disinfection system for a diferential robot applying security methods in indoor. |
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Conference Article |
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Year |
2022 |
Publication |
Communications in Computer and Information Science, International Conference on Applied Technologies (ICAT 2021), octubre 27-29 |
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CONFERENCE |
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Volume |
1535 |
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319-331 |
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cidis @ cidis @ |
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178 |
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