|
Luis Chuquimarca, B. V. & S. V. (2024). A Review of External Quality Inspection for Fruit Grading using CNN Models (Vol. Vol. 14).
|
|
Luis C. Herrera, L. del R. L., Nayeth I. Solorzano, Jonathan S. Paillacho & Dennys Paillacho. (2021). Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. In The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communication in Computer and Information Science (Vol. 1388, pp. 164–178).
|
Low S., I. N., Nina O., Sappa A. and Blasch E. (2022). Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. In Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 417–425).
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.
|
|
Leo Thomas Ramos & Angel D. Sappa. (2025). Enhanced Aerial Scene Classification Through ConvNeXt Architectures and Channel Attention. In 10th International Congress on Information and Communication Technology.
|
|
Leo Thomas Ramos & Angel D. Sappa. (2025). Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery. Scientific Reports, Vol. 15.
|
|
Leo Thomas Ramos & Angel D. Sappa. (2025). Dual-branch ConvNeXt-based Network with Attentional Fusion Decoding for Land Cover Classification Using Multispectral Imagery. In IEEE SoutheastCon 2025.
|
|
Leo Ramos & Angel D. Sappa. (2024). Multispectral Semantic Segmentation for Land Cover Classification: An Overview (Vol. Vol. 17).
|
|
Kevin E. Munoz, L. N. S., Steven S. Araujo, and Boris X. Vintimilla. (2025). Stereo Vision Techniques: A Comparative Study of Traditional and Machine Learning-Based Approaches. In 5th International Conference on Computer Vision and Robotics CVR 2025.
|
|
Kevin E. Muñoz Loberlly N. Salazar Steven S. Araujo Boris X. Vintimilla. (2025). Detecting and Characterizing Human Interactions to EnhanceHuman-Robot Engagement. In 3rd International Conference on Robotics, Control and Vision Engineering RCVE 2025.
|
Julien Poujol, Cristhian A. Aguilera, Etienne Danos, Boris X. Vintimilla, Ricardo Toledo, & Angel D. Sappa. (2015). A visible-Thermal Fusion based Monocular Visual Odometry. In Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 (Vol. 417, pp. 517–528).
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.
|
