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Author 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 (up) 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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Author Cristhian A. Aguilera; Xaver Soria; Angel D. Sappa; Ricardo Toledo
Title RGBN Multispectral Images: a Novel Color Restoration Approach Type Conference Article
Year 2017 Publication (up) 15th International Conference on Practical Applications of Agents and Multi-Agent Systems Abbreviated Journal
Volume 619 Issue Pages 155-163
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Call Number cidis @ cidis @ Serial 59
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Image patch similarity through a meta-learning metric based approach Type Conference Article
Year 2019 Publication (up) 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia Abbreviated Journal
Volume Issue Pages 511-517
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Abstract Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results

have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results.
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Call Number gtsi @ user @ Serial 115
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
Title Vegetation Index Estimation from Monospectral Images Type Conference Article
Year 2018 Publication (up) 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science Abbreviated Journal
Volume 10882 Issue Pages 353-362
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Abstract This paper proposes a novel approach to estimate Normalized

Difference Vegetation Index (NDVI) from just the red channel of

a RGB image. The NDVI index is defined as the ratio of the difference

of the red and infrared radiances over their sum. In other words, information

from the red channel of a RGB image and the corresponding

infrared spectral band are required for its computation. In the current

work the NDVI index is estimated just from the red channel by training a

Conditional Generative Adversarial Network (CGAN). The architecture

proposed for the generative network consists of a single level structure,

which combines at the final layer results from convolutional operations

together with the given red channel with Gaussian noise to enhance

details, resulting in a sharp NDVI image. Then, the discriminative model

estimates the probability that the NDVI generated index came from the

training dataset, rather than the index automatically generated. Experimental

results with a large set of real images are provided showing that

a Conditional GAN single level model represents an acceptable approach

to estimate NDVI index.
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Call Number gtsi @ user @ Serial 82
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Author Dennis G. Romero; Roberto Yoncon; Angel Guale; Bonny Bayot; Fanny Panchana
Title Evaluación de técnicas de clasificación orientadas a la identificación automática de órganos del camarón a partir de imágenes histológicas Type Conference Article
Year 2017 Publication (up) 15th LACCEI International Multi-Conference for Engineering, Education, and Technology Abbreviated Journal
Volume 2017-July Issue Pages 1-6
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Call Number cidis @ cidis @ Serial 61
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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 (up) 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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Author Patricia L. Suárez, Dario Carpio, and Angel Sappa
Title Non-Homogeneous Haze Removal through a Multiple Attention Module Architecture. Type Conference Article
Year 2021 Publication (up) 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science Abbreviated Journal
Volume 13018 Issue Pages 178-190
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Call Number cidis @ cidis @ Serial 162
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Author Angel D. Sappa, Patricia L. Suárez, Henry O. Velesaca, Darío Carpio
Title Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. Type Conference Article
Year 2022 Publication (up) 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2022), julio 20-22 Abbreviated Journal
Volume Issue Pages 85-92
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Call Number cidis @ cidis @ Serial 182
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Author Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla.
Title Thermal Image SuperResolution through Deep Convolutional Neural Network. Type Conference Article
Year 2019 Publication (up) 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá Abbreviated Journal
Volume Issue Pages 417-426
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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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Call Number gtsi @ user @ Serial 103
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Author Patricia L. Suarez, Dario Carpio, Angel D. Sappa and Henry O. Velesaca
Title Transformer based Image Dehazing. Type Conference Article
Year 2022 Publication (up) 16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. Abbreviated Journal
Volume Issue Pages 148-154
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Call Number cidis @ cidis @ Serial 195
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