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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 | W. Agila; Gomer Rubio; L. Miranda; D. Sanaguano | ||||
Title | Open Control Architecture for the Characterization and Control of the PEM Fuel Cell | Type | Conference Article | ||
Year | 2019 | Publication | IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador | Abbreviated Journal | |
Volume | Issue | Pages | 1-5 | ||
Keywords | PEM fuel cell, Experimental System, Control Engineering. | ||||
Abstract | Proton exchange membrane (PEM) fuel cells, are an efficient and clean source of electrical energy. The analysis of its operation requires experimental work, which allows measuring, modeling and optimizing PEM fuel cells electrical behavior under different operating conditions. Therefore, having an experimentation platform that allows to easily carry out its study and control is essential. This research presents the design and development of an open instrumental system that allows measuring, controlling and determining the operating parameters of a PEM fuel cell. As results, the polarization curves, voltage-current, obtained by the system itself in different experimental conditions are shown. These curves are a very useful tool to evaluate the electrical behavior of the PEM battery. | ||||
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Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 118 | ||
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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 | Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca | ||||
Title | Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem | Type | Conference Article | ||
Year | 2020 | Publication | The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 | Abbreviated Journal | |
Volume | 4 | Issue | Pages | 498-505 | |
Keywords | Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters. | ||||
Abstract | This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consist of first training the network using pairs of images from the virtual-world scenario considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on the training due to the reduced number of pairs of real-images on most of the public data sets. |
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ISSN | ISBN | 978-989758402-2 | Medium | ||
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Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 120 | ||
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Author | Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Thermal Image Super-Resolution: a Novel Architecture and Dataset | Type | Conference Article | ||
Year | 2020 | Publication | The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 | Abbreviated Journal | |
Volume | 4 | Issue | Pages | 111-119 | |
Keywords | Thermal images, Far Infrared, Dataset, Super-Resolution. | ||||
Abstract | This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem. The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available. |
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ISSN | ISBN | 978-989758402-2 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 121 | ||
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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 | ||
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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 |
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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 | |
Area | Expedition | Conference | |||
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 |
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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 | ||
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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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Author | Henry O. Velesaca, Steven Araujo, Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa | ||||
Title | Off-the-Shelf Based System for Urban Environment Video Analytics. | Type | Conference Article | ||
Year | 2020 | Publication | The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) | Abbreviated Journal | |
Volume | 2020-July | Issue | 9145121 | Pages | 459-464 |
Keywords | Greenhouse gases, carbon footprint, object detection, object tracking, website framework, off-the-shelf video analytics. | ||||
Abstract | This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach. |
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Language | English | Summary Language | Original Title | ||
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ISSN | 21578672 | ISBN | 978-172817539-3 | Medium | |
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Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 125 | ||
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