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Jorge L. Charco, A. D. S., Boris X. Vintimilla. (2022). Human Pose Estimation through A Novel Multi-View Scheme. In Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 (Vol. 5, pp. 855–862).
Abstract: This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human
pose estimation problem. The proposed approach first obtains the human body joints of a set of images,
which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from
another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed
for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and
comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements
in the accuracy of body joints estimations.
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Xavier Soria, G. P. - J. & A. S. (2022). LDC: Lightweight Dense CNN for Edge Detection. IEEE Access journal, Vol. 10, pp. 68281–68290.
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Rivadeneira, R. E., & Sappa, A. D. and V. B. X. (2022). Thermal Image Super-Resolution: A Novel Unsupervised Approach. In Communications in Computer and Information Science, 15th International Communications in Computer and Information Science Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Vol. 1474, pp. 495–506).
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Rafael E. Rivadeneira, A. D. S., Boris X. Vintimilla, Jin Kim, Dogun Kim et al. (2022). Thermal Image Super-Resolution Challenge Results- PBVS 2022. In Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 349–357).
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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Rangnekar, A., Mulhollan, Z., Vodacek, A., Hoffman, M., Sappa, A. D., & Yu, J. et al. (2022). Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. In Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 389–397).
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Low S., I. N., Nina O., Sappa A. and Blasch E. (2022). Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. In Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 417–425).
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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Velez R., P. A., Silva S., Paillacho D., and Paillacho J. (2022). Implementation of a UVC lights disinfection system for a diferential robot applying security methods in indoor. In Communications in Computer and Information Science, International Conference on Applied Technologies (ICAT 2021), octubre 27-29 (Vol. 1535, pp. 319–331).
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Nayeth I. Solorzano, L. C. H., Leslie del R. Lima, Dennys F. Paillacho & Jonathan S. Paillacho. (2022). Visual Metrics for Educational Videogames Linked to Socially Assistive Robots in an Inclusive Education Framework. In Smart Innovation, Systems and Technologies. International Conference in Information Technology & Education (ICITED 21), julio 15-17 (Vol. 256, pp. 119–132).
Abstract: In gamification, the development of "visual metrics for educational
video games linked to social assistance robots in the framework of inclusive education" seeks to provide support, not only to regular children but also to children with specific psychosocial disabilities, such as those diagnosed with autism spectrum disorder (ASD). However, personalizing each child's experiences represents a limitation, especially for those with atypical behaviors. 'LOLY,' a social assistance robot, works together with mobile applications associated with the family of educational video game series called 'MIDI-AM,' forming a social robotic platform. This platform offers the user curricular digital content to reinforce the teaching-learning processes and motivate regular children and those with ASD. In the present study, technical, programmatic experiments and focus groups were carried out, using open-source facial recognition algorithms to monitor and evaluate the degree of user attention throughout the interaction. The objective is to evaluate the management of a social robot linked to educational video games
through established metrics, which allow monitoring the user's facial expressions
during its use and define a scenario that ensures consistency in the results for its applicability in therapies and reinforcement in the teaching process, mainly
adaptable for inclusive early childhood education.
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Roberto Jacome Galarza. (2022). Multimodal deep learning for crop yield prediction. In Doctoral Symposium on Information and Communication Technologies –DSICT 2022. Octubre 12-14. (Vol. 1647, pp. 106–117).
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Patricia Suarez, H. V., Dario Carpio, Angel Sappa, Patricia Urdiales, Francisca Burgos. (2022). Deep Learning based Shrimp Classification. In 17th International Symposium on Visual Computing, San Diego, USA, Octubre 3-5. Lecture Notes in Computer Science (LNCS) (Vol. 13598 LNCS, pp. 36–45).
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