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Angel D. Sappa, P. L. S., Henry O. Velesaca, Darío Carpio. (2022). Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. In 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2022), julio 20-22 (pp. 85–92).
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Sianna Puente, Cindy Madrid, Miguel Realpe, & Boris X. Vintimilla. (2017). An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System. In 2017 International Conference on Deep Learning Technologies (ICDLT 2017) (Vol. Part F128535, pp. 60–65).
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Alex Ferrin, Julio Larrea, Miguel Realpe, & Daniel Ochoa. (2018). Detection of utility poles from noisy Point Cloud Data in Urban environments. In Artificial Intelligence and Cloud Computing Conference (AICCC 2018) (pp. 53–57).
Abstract: In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners.
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Patricia Suarez, H. V., Dario Carpio, Angel Sappa, Patricia Urdiales, Francisca Burgos. (2022). Deep Learning based Shrimp Classification. In 17th International Symposium on Visual Computing, San Diego, USA, Octubre 3-5. Lecture Notes in Computer Science (LNCS) (Vol. 13598 LNCS, pp. 36–45).
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Lukas Danev, Marten Hamann, Nicolas Fricke, Tobias Hollarek, & Dennys Paillacho. (2017). Development of animated facial expression to express emotions in a robot: RobotIcon. In IEEE Ecuador Technical Chapter Meeting (ETCM) (Vol. 2017-January, pp. 1–6).
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Xavier Soria, Angel D. Sappa, & Arash Akbarinia. (2017). Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities. In The 7th International Conference on Image Processing Theory, Tools and Application (pp. 1–6).
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Milton Mendieta, F. Panchana, B. Andrade, B. Bayot, C. Vaca, Boris X. Vintimilla, et al. (2018). Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. In IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador (pp. 1–6).
Abstract: The identification of shrimp organs in biology using
histological images is a complex task. Shrimp histological images
poses a big challenge due to their texture and similarity among
classes. Image classification by using feature engineering and
convolutional neural networks (CNN) are suitable methods to
assist biologists when performing organ detection. This work
evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof-
Words (PBOW) models for image classification leveraging big
data techniques; and transfer learning for the same classification
task by using a pre-trained CNN. A comparative analysis
of these two different techniques is performed, highlighting
the characteristics of both approaches on the shrimp organs
identification problem.
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Miguel Realpe, Boris X. Vintimilla, & Ljubo Vlacic. (2015). Sensor Fault Detection and Diagnosis for autonomous vehicles. In 2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 (Vol. 30, pp. 1–6). EDP Sciences.
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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Miguel Realpe, Boris X. Vintimilla, & Ljubo Vlacic. (2016). A Fault Tolerant Perception system for autonomous vehicles. In 35th Chinese Control Conference (CCC2016), International Conference on, Chengdu (pp. 1–6).
Abstract: Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset.
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Juan A. Carvajal, Dennis G. Romero, & Angel D. Sappa. (2016). Fine-tuning based deep covolutional networks for lepidopterous genus recognition. In XXI IberoAmerican Congress on Pattern Recognition (pp. 1–9).
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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