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| Author | Henry O. Velesaca & Angel D. Sappa | ||||
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Seeing the Unseen: AI-Powered Camouflaged Pest Detection | Type | Journal Article | ||
| Year | 2025 | Publication | 9th International Conference on Machine Vision and Information Technology (CMVIT 2025) | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 268 | ||
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| Author | Rangnekar,Aneesha; Mulhollan,Zachary; Vodacek,Anthony; Hoffman,Matthew; Sappa,Angel D.; Yu,Jun et al. | ||||
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Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. | Type | Conference Article | ||
| Year | 2022 | Publication | Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. | Abbreviated Journal | CONFERENCE |
| Volume | 2022-June | Issue | Pages | 389-397 | |
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| Call Number | cidis @ cidis @ | Serial | 176 | ||
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| Author | Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic | ||||
| Title |
Sensor Fault Detection and Diagnosis for autonomous vehicles | Type | Conference Article | ||
| Year | 2015 | Publication | 2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 | Abbreviated Journal | |
| Volume | 30 | Issue | MATEC Web of Conferences | Pages | 1-6 |
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| Abstract | In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor. | ||||
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| Publisher | EDP Sciences | Place of Publication | Editor | ||
| Language | English | Summary Language | English | Original Title | |
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| Call Number | cidis @ cidis @ | Serial | 42 | ||
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| Author | Viñán-Ludeña, M.S., Roberto Jacome Galarza, Montoya, L.R., Leon, A.V., & Ramírez, C.C. | ||||
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Smart university: an architecture proposal for information management using open data for research projects. | Type | Journal Article | ||
| Year | 2020 | Publication | Advances in Intelligent Systems and Computing | Abbreviated Journal | |
| Volume | 1137 AISC, 2020 | Issue | Pages | 172-178 | |
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| Call Number | cidis @ cidis @ | Serial | 188 | ||
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| Author | Viñán-Ludeña M.S., De Campos L.M., Roberto Jacome Galarza, & Sinche Freire, J. | ||||
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Social media influence: a comprehensive review in general and in tourism domain | Type | Journal Article | ||
| Year | 2020 | Publication | Smart Innovation, Systems and Technologies. | Abbreviated Journal | |
| Volume | 171, 2020 | Issue | Pages | 25-35 | |
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| Call Number | cidis @ cidis @ | Serial | 190 | ||
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| Author | Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado | ||||
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Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. | Type | Conference Article | ||
| Year | 2021 | Publication | 19th LACCEI International Multi-Conference for Engineering, Education, and Technology | Abbreviated Journal | |
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| Keywords | processing, software engineering, deep learning, intelligent vision systems, cloud computing. | ||||
| Abstract | Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems. Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 154 | ||
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| Author | Victor Santos; Angel D. Sappa; Miguel Oliveira | ||||
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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 | Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A. | ||||
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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 | Armin Mehri, Parichehr Behjati, Dario Carpio, and Angel D. Sappa | ||||
| Title |
SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE access | Abbreviated Journal | |
| Volume | Vol. 11 | Issue | Pages | 121457 - 121469 | |
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| ISSN | 21693536 | ISBN | Medium | ||
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| Call Number | cidis @ cidis @ | Serial | 227 | ||
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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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| ISSN | ISBN | 14248220 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 133 | ||
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