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Author (down) Luis Chuquimarca, Boris Vintimilla & Sergio Velastin
Title Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets. Type Conference Article
Year 2023 Publication 18th International Conference on Computer Vision Theory and Applications VISAPP 2023 Abbreviated Journal
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Call Number cidis @ cidis @ Serial 202
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Author (down) Luis C. Herrera, Leslie del R. Lima, Nayeth I. Solorzano, Jonathan S. Paillacho & Dennys Paillacho.
Title Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. Type Conference Article
Year 2021 Publication The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communication in Computer and Information Science Abbreviated Journal
Volume 1388 Issue Pages 164-178
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Call Number cidis @ cidis @ Serial 191
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Author (down) Low S., Inkawhich N., Nina O., Sappa A. and Blasch E.
Title Multi-modal Aerial View Object Classification 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 417-425
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Abstract This paper details the results and main findings of the

second iteration of the Multi-modal Aerial View Object

Classification (MAVOC) challenge. This year’s MAVOC

challenge is the second iteration. The primary goal of

both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) input

modalities. Teams are encouraged/challenged to develop

multi-modal approaches that incorporate complementary

information from both domains. While the 2021 challenge

showed a proof of concept that both modalities could be

used together, the 2022 challenge focuses on the detailed

multi-modal models. Using the same UNIfied COincident

Optical and Radar for recognitioN (UNICORN) dataset and

competition format that was used in 2021. Specifically, the

challenge focuses on two techniques, (1) SAR classification

and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods

and describing their performance on our blind test set. Notably, all of the top ten teams outperform our baseline. For

SAR classification, the top team showed a 129% improvement over our baseline and an 8% average improvement

from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average

improvement over 2021.
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Call Number cidis @ cidis @ Serial 177
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Author (down) Julien Poujol; Cristhian A. Aguilera; Etienne Danos; Boris X. Vintimilla; Ricardo Toledo; Angel D. Sappa
Title A visible-Thermal Fusion based Monocular Visual Odometry Type Conference Article
Year 2015 Publication Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 Abbreviated Journal
Volume 417 Issue Pages 517-528
Keywords Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion
Abstract The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
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Call Number cidis @ cidis @ Serial 44
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Author (down) Juca Aulestia M., Labanda Jaramillo M., Guaman Quinche J., Coronel Romero E., Chamba Eras L., & Roberto Jacome Galarza
Title Open innovation at university: a systematic literature review Type Journal Article
Year 2020 Publication Advances in Intelligent Systems and Computing Abbreviated Journal
Volume 1159 AISC, 2020 Issue Pages 3-14
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Call Number cidis @ cidis @ Serial 189
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Author (down) Juan C. Basurto, Patricia Chávez and Hernán Córdova
Title A Proximity-Aware Transparent Handoff Mobility Scheme for VoIP Communication over Infrastructure Mesh Networks Type Conference Article
Year 2011 Publication International Congress of Electronic, Electrical and Systems Engineering-INTERCON 2011 Abbreviated Journal
Volume Issue Pages
Keywords Wireless Mesh Networks; Quality of Service; Mobility Management; Voice over IP.
Abstract Mobility Management plays a key role in Voice-over- IP (VoIP) communications over Wireless Mesh Networks (WMN) as clients should maintain adequate levels of Quality of Service (QoS) as they move across the network. This paper presents PATH, a Proximity-Aware Transparent Handoff mobility scheme for real time voice communications over wireless mesh networks. Our study focuses on Medium Access Control (MAC) layer procedures and relies on gratuitous ARP unicasting in order to provide fast-handoffs. An experimental evaluation has been conducted and its results are shown in this paper.
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Call Number cidis @ cidis @ Serial 20
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Author (down) 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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Call Number gtsi @ user @ Serial 63
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Author (down) Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa
Title Fine-tuning based deep covolutional networks for lepidopterous genus recognition Type Conference Article
Year 2016 Publication XXI IberoAmerican Congress on Pattern Recognition Abbreviated Journal
Volume Issue Pages 1-9
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Abstract This paper describes an image classi cation approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio
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Call Number cidis @ cidis @ Serial 53
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Author (down) José Reyes; Axel Godoy; Miguel Realpe.
Title Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones. Type Conference Article
Year 2019 Publication International Multi-Conference of Engineering, Education and Technology (LACCEI 2019); Montego Bay, Jamaica Abbreviated Journal
Volume 2019-July Issue Pages
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Abstract Los drones o aeronaves no tripuladas son muy útiles para la adquisición de imágenes, de forma mucho más simple que los satélites o aviones. Sin embargo, las imágenes adquiridas por drones deben ser combinadas de alguna forma para convertirse en información de valor sobre un terreno o cultivo. Existen diferentes programas que reciben imágenes y las combinan en una sola imagen, cada uno con diferentes características (rendimiento, precisión, resultados, precio, etc.). En este estudio se revisaron diferentes programas de código abierto para fusión de imágenes, con el ?n de establecer cuál de ellos es más útil, especí?camente para ser utilizado por pequeños y medianos agricultores en Ecuador. Los resultados pueden ser de interés para diseñadores de software, ya que al utilizar código abierto, es posible modi?car e integrar los programas en un ?ujo de trabajo más simpli?cado. Además, que permite disminuir costos debido a que no requiere de pagos de licencias para su uso, lo cual puede repercutir en un mayor acceso a la tecnología para los pequeños y medianos agricultores. Como parte de los resultados de este estudio se ha creado un repositorio de acceso público con algoritmos de pre-procesamiento necesarios para manipular las imágenes adquiridas por una cámara multiespectral y para luego obtener un mapa completo en formatos RGB, CIR y NDVI.
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Call Number gtsi @ user @ Serial 102
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Author (down) Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa
Title Deep learning based camera pose estimation in multi-view environment. Type Conference Article
Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal
Volume Issue Pages 224-228
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Abstract This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.
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Call Number gtsi @ user @ Serial 93
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