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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  |
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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 93 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Deep Learning based Single Image Dehazing | Type | Conference Article | ||
| Year | 2018 | Publication |
14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA | Abbreviated Journal | |
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| Abstract | This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently. A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 83 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Learning to Colorize Infrared Images | Type | Conference Article | ||
| Year | 2017 | Publication |
15th International Conference on Practical Applications of Agents and Multi-Agent Systems | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 58 | ||
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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 |
15th International Conference on Practical Applications of Agents and Multi-Agent Systems | Abbreviated Journal | |
| Volume | 619 | Issue | Pages | 155-163 | |
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| Notes | Approved | no | |||
| 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 |
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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| Notes | Approved | no | |||
| 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 |
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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| Notes | Approved | no | |||
| 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 |
15th LACCEI International Multi-Conference for Engineering, Education, and Technology | Abbreviated Journal | |
| Volume | 2017-July | Issue | Pages | 1-6 | |
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| Notes | Approved | no | |||
| 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 |
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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| Notes | Approved | no | |||
| 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 |
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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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 162 | ||
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| Author | Jacome-Galarza L.-R | ||||
| Title | Crop yield prediction utilizing multimodal deep learning | Type | Conference Article | ||
| Year | 2021 | Publication |
16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 | Abbreviated Journal | |
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| Keywords | Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada. | ||||
| Abstract | La agricultura de precisión es una práctica vital para mejorar la producción de cosechas. El presente trabajo tiene como objetivo desarrollar un modelo multimodal de aprendizaje profundo que es capaz de producir un mapa de salud de cosechas. El modelo recibe como entradas imágenes multiespectrales y datos de sensores de campo (humedad, temperatura, estado del suelo, etc.) y crea un mapa de rendimiento de la cosecha. La utilización de datos multimodales tiene como finalidad extraer patrones ocultos del estado de salud de las cosechas y de esta manera obtener mejores resultados que los obtenidos mediante los índices de vegetación. |
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| Language | Español | Summary Language | Original Title | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 150 | ||
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