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Author | Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla | ||||
Title | 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 | Jorge Alvarez; Mireya Zapata; Dennys Paillacho | ||||
Title | Mechanical Design of a spatial mechanism for the robot head movements in social robotics for the evaluation of Human-Robot Interaction. | Type | Conference Article | ||
Year | 2019 | Publication | 2nd International Conference on Human Systems Engineering and Design: Future Trends and Applications (IHSED 2019); Munich, Alemania | Abbreviated Journal | |
Volume | 1026 | Issue | Pages | 160-165 | |
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Call Number | gtsi @ user @ | Serial | 104 | ||
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Author | Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. | ||||
Title | Time series in sensor data using state of the art deep learning approaches: A systematic literature review. | Type | Conference Article | ||
Year | 2022 | Publication | VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. | Abbreviated Journal | |
Volume | 252 | Issue | Pages | 503-514 | |
Keywords | time series, deep learning, recurrent networks, sensor data, IoT. | ||||
Abstract | IoT (Internet of Things) and AI (Artificial Intelligence) are becoming support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising results. |
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Call Number | cidis @ cidis @ | Serial | 152 | ||
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Author | Henry Velesaca, Boris Vintimilla, Jorge Vulgarin, Coen Antens & Alberto Rubio Pérez | ||||
Title | Deep Learning-based Multimodal Sensing Framework for AntiSpoofing Systems | Type | Journal 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 | 238 | ||
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Author | Henry Velesaca Lara, Patricia Suarez, Darío Carpio & Angel Sappa | ||||
Title | 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 | Henry Velesaca Lara, Juan Antonio Holgado & José Miguel Gutiérrez | ||||
Title | Optimizing Smart Factory Operations: A Methodological Approach to Industrial System Implementation based on OPC-UA | 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 | 242 | ||
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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 | |
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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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ISSN | 21578672 | ISBN | 978-172817539-3 | Medium | |
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Call Number | cidis @ cidis @ | Serial | 125 | ||
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Author | Henry O. Velesaca, Patricia L. Suárez, Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa. | ||||
Title | Video Analytics in Urban Environments: Challenges and Approaches. | 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 | 101-122 | |
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Call Number | cidis @ cidis @ | Serial | 196 | ||
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Author | Henry O. Velesaca, Patricia L. Suarez, Dario Carpio, and Angel D. Sappa | ||||
Title | Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy | Type | Conference Article | ||
Year | 2021 | Publication | 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science | Abbreviated Journal | |
Volume | 13017 | Issue | Pages | 131-143 | |
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Call Number | cidis @ cidis @ | Serial | 163 | ||
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