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Author Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa
Title (up) Fine-tuning based deep covolutional networks for lepidopterous genus recognition Type Conference Article
Year 2016 Publication XXI IberoAmerican Congress on Pattern Recognition Abbreviated Journal
Volume Issue Pages 1-9
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Abstract This paper describes an image classi cation approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio
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Call Number cidis @ cidis @ Serial 53
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Author Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa
Title (up) Fine-tuning deep convolutional networks for lepidopterous genus recognition Type Journal Article
Year 2017 Publication Lecture Notes in Computer Science Abbreviated Journal
Volume Vol. 10125 LNCS Issue Pages pp. 467-475
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Call Number gtsi @ user @ Serial 63
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Author Sebastián Fuenzalida; Keyla Toapanta; Jonathan S. Paillacho Corredores; Dennys Paillacho
Title (up) Forward and Inverse Kinematics of a Humanoid Robot Head for Social Human Robot-Interaction Type Conference Article
Year 2019 Publication IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador Abbreviated Journal
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Abstract This paper presents an analysis of forward and inverse kinematics for a humanoid robotic head. The robotic head is used for the study of social human-robot interaction, such as a support tool to maintain the attention of patients with Autism Spectrum Disorder. The design of a parallel robot that emulates human head movements through a closed structure is presented. The position and orientation in this space is controlled by three servomotors. For this, the solutions made for the kinematic problem are encompassed by a geometric analysis of a mobile base. This article describes a non-systematic method,

called the geometric method, and compares some of the most popular existing methods considering reliability and computational cost. The geometric method avoids the use of changing reference systems, and instead uses geometric

relationships to directly obtain the position based on joint variables; and the other way around. Therefore, it converges in a few iterations and has a low computational cost.
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Call Number gtsi @ user @ Serial 113
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Author Luis Chuquimarca, Renzo Pacheco, Paula Gonzalez, Boris Vintimilla & Sergio Velastin
Title (up) Fruit defect detection using CNN models with real and virtual data. Type Conference Article
Year 2023 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2023 Abbreviated Journal
Volume Issue Pages 272 - 279
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Call Number cidis @ cidis @ Serial 203
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Author Henry Velesaca Lara, Patricia Suarez, Darío Carpio & Angel Sappa
Title (up) Fruit Grading based on Deep Learning and Active Vision System Type Conference Article
Year 2024 Publication Accepted in CIIA – II International Conference of Applied Industrial Engineering Abbreviated Journal
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Call Number cidis @ cidis @ Serial 241
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Author Patricia Suarez & Angel D. Sappa
Title (up) Haze-Free Imaging through Haze-Aware Transformer Adaptations Type Conference Article
Year 2024 Publication In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) Abbreviated Journal
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Call Number cidis @ cidis @ Serial 236
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Author Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, Henry O. Velesaca.
Title (up) Human Body Pose Estimation in Multi-view Environments. Type Book Chapter
Year 2022 Publication ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series Abbreviated Journal BOOK
Volume 224 Issue Pages 79-99
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Call Number cidis @ cidis @ Serial 197
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Author Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla
Title (up) Human Pose Estimation through A Novel Multi-View Scheme 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 5 Issue Pages 855-862
Keywords Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach
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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Call Number cidis @ cidis @ Serial 169
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Author Angel D. Sappa.
Title (up) ICT Applications for Smart Cities Type Book Chapter
Year 2022 Publication Intelligent Systems Reference Library Abbreviated Journal BOOK
Volume 224 Issue Pages
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Call Number cidis @ cidis @ Serial 198
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title (up) 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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Call Number gtsi @ user @ Serial 115
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