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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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Rafael E. Rivadeneira, Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Thermal Image SuperResolution through Deep Convolutional Neural Network. In 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá (pp. 417–426).
Abstract: Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset
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Patricia L. Suarez, D. C., Angel D. Sappa and Henry O. Velesaca. (2022). Transformer based Image Dehazing. In 16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. (pp. 148–154).
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Rafael E. Rivadeneira, A. D. S. and B. X. V. (2022). Multi-Image Super-Resolution for Thermal Images. In 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022), febrero 6-8.
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Jorge L. Charco, A. D. S., Boris X. Vintimilla. (2022). Human Pose Estimation through A Novel Multi-View Scheme. In 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022), febrero 6-8 (pp. 855–862).
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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Angel Morera, Angel Sánchez, Angel D. Sappa, & José F. Vélez. (2019). Robust Detection of Outdoor Urban Advertising Panels in Static Images. In 17th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2019); Ávila, España. Communications in Computer and Information Science (Vol. 1047, pp. 246–256).
Abstract: One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising
panels. For such a purpose, a previous stage is to accurately detect and
locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based
on a deep neural network architecture that minimizes the number of
false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection
over Union (IoU) accuracy metric make this proposal applicable in real
complex urban images.
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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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Luis Chuquimarca, B. V. & S. V. (2023). Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets. In 18th International Conference on Computer Vision Theory and Applications VISAPP 2023.
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Luis Chuquimarca, R. P., Paula Gonzalez, Boris Vintimilla & Sergio Velastin. (2023). Fruit defect detection using CNN models with real and virtual data. In 18th International Conference on Computer Vision Theory and Applications VISAPP 2023.
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Emmanuel Moran, B. V. & M. R. (2023). Towards a Robust Solution for the Supermarket Shelf Audit Problem. In 18th International Conference on Computer Vision Theory and Applications VISAPP 2023.
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