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Author Sebastián Fuenzalida; Keyla Toapanta; Jonathan S. Paillacho Corredores; Dennys Paillacho
Title 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
Volume Issue Pages
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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 Stalin Francis Quinde
Title Un nuevo modelo BM3D-RNCA para mejorar la estimación de la imagen libre de ruido producida por el método BM3D. (Ph.D. Angel Sappa, Director.). M.Sc. thesis Type Book Chapter
Year 2019 Publication Ediciones FIEC-ESPOL Abbreviated Journal
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Corporate Author Ph.D. Angel Sappa, Director. Thesis Master's thesis
Publisher Place of Publication Editor
Language Español Summary Language Original Title
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Notes Approved no
Call Number gtsi @ user @ Serial 117
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Author Shendry Rosero Vásquez
Title Reconocimiento facial: técnicas tradicionales y técnicas de aprendizaje profundo, un análisis. (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). M.Sc. thesis Type Book Chapter
Year 2019 Publication Ediciones FIEC-ESPOL Abbreviated Journal
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Corporate Author Ph.D. Angel Sappa, Director de tesis & Ph.D. Boris Vintimilla, Codirector Thesis Master's thesis
Publisher Place of Publication Editor
Language Español Summary Language Original Title
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Notes Approved yes
Call Number gtsi @ user @ Serial 114
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title 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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Author Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon
Title Open Source system for identification of corn leaf chlorophyll contents based on multispectral images Type Conference Article
Year 2019 Publication International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador Abbreviated Journal
Volume Issue Pages 572-581
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Abstract It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images.

Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear

regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%.

It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests.

However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1].
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Call Number gtsi @ user @ Serial 116
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Author Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A.
Title Special Issue on Autonomous Driving and Driver Assistance Systems Type Journal Article
Year 2019 Publication In Robotics and Autonomous Systems Abbreviated Journal
Volume 121 Issue Pages
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Call Number gtsi @ user @ Serial 119
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Author Suárez P.
Title Processing and Representation of Multispectral Images Using Deep Learning Techniques Type Magazine Article
Year 2021 Publication In Electronic Letters on Computer Vision and Image Analysis Abbreviated Journal
Volume Vol. 19 Issue Issue 2 Pages pp. 5-8
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Corporate Author Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector Thesis Master's thesis
Publisher Place of Publication Editor
Language Español Summary Language Original Title
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Notes Approved yes
Call Number cidis @ cidis @ Serial 122
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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
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 2020-June Issue 9151059 Pages 432-439
Keywords (up)
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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Language English Summary Language Original Title
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ISSN 21607508 ISBN 978-172819360-1 Medium
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Notes Approved no
Call Number cidis @ cidis @ Serial 123
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Author Xavier Soria; Edgar Riba; Angel D. Sappa
Title Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection Type Conference Article
Year 2020 Publication 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) Abbreviated Journal
Volume Issue 9093290 Pages 1912-1921
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Abstract This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered.
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ISSN ISBN 978-172816553-0 Medium
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Notes Approved no
Call Number cidis @ cidis @ Serial 126
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Author Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa.
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 2020-June Issue 9150684 Pages 294-302
Keywords (up)
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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Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN 21607508 ISBN 978-172819360-1 Medium
Area Expedition Conference
Notes Approved no
Call Number cidis @ cidis @ Serial 124
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