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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 | Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez | ||||
| Title | SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. | Type | Journal Article | ||
| Year | 2020 | Publication | Abbreviated Journal | In Sensors | |
| Volume | Vol. 2020-August | Issue | 16 | Pages | pp. 1-23 |
| Keywords | object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities | ||||
| Abstract | This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images o ers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the diculty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with di erent types of semantic segmentation networks and using the same evaluation metrics is also included. |
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| Language | English | Summary Language | English | Original Title | |
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14248220 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 133 | ||
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| Author | Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco | ||||
| Title | Computer vision for image understanding. A comprehensive review | Type | Conference Article | ||
| Year | 2019 | Publication | International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador | Abbreviated Journal | |
| Volume | Issue | Pages | 248-259 | ||
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| Abstract | 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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 97 | ||
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| Author | Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa | ||||
| Title | Melamine faced panels defect classification beyond the visible spectrum. | Type | Journal Article | ||
| Year | 2018 | Publication | In Sensors 2018 | Abbreviated Journal | |
| Volume | Vol. 11 | Issue | Issue 11 | Pages | |
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| Abstract | In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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| Call Number | gtsi @ user @ | Serial | 89 | ||
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| Author | Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa | ||||
| Title | 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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 63 | ||
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| Author | Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo | ||||
| Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication | In Sensors Journal | Abbreviated Journal | |
| Volume | Vol. 17 | Issue | Pages | pp. 873 | |
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| Call Number | gtsi @ user @ | Serial | 64 | ||
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| Author | Victor Santos; Angel D. Sappa; Miguel Oliveira | ||||
| Title | Special Issue on Autonomous Driving an Driver Assistance Systems | Type | Journal Article | ||
| Year | 2017 | Publication | In Robotics and Autonomous Systems Journal | Abbreviated Journal | |
| Volume | Vol. 91 | Issue | Pages | pp. 208-209 | |
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| Call Number | gtsi @ user @ | Serial | 65 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture | Type | Conference Article | ||
| Year | 2017 | Publication | 19th International Conference on Image Analysis and Processing. | Abbreviated Journal | |
| Volume | Issue | Pages | 287-297 | ||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 66 | ||
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| Author | Byron Lima; Ricardo Cajo; Victor Huilcapi; Wilton Agila | ||||
| Title | Modeling and comparative study of linear and nonlinear controllers for rotary inverted pendulum | Type | Conference Article | ||
| Year | 2017 | Publication | Journal of Physics: Conference Series | Abbreviated Journal | |
| Volume | 783 | Issue | Pages | ||
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| Abstract | The rotary inverted pendulum (RIP) is a problem difficult to control, several studies have been conducted where different control techniques have been applied. Literature reports that, although problem is nonlinear, classical PID controllers presents appropriate performances when applied to the system. In this paper, a comparative study of the performances of linear and nonlinear PID structures is carried out. The control algorithms are evaluated in the RIP system, using indices of performance and power consumption, which allow the categorization of control strategies according to their performance. This article also presents the modeling system, which has been estimated some of the parameters involved in the RIP system, using computer-aided design tools (CAD) and experimental methods or techniques proposed by several authors attended. The results indicate a better performance of the nonlinear controller with an increase in the robustness and faster response than the linear controller | ||||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 69 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Learning Image Vegetation Index through a Conditional Generative Adversarial Network | Type | Conference Article | ||
| Year | 2017 | Publication | 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 70 | ||
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