1
artículo
Publicado 2020
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Las imágenes de satélite de alta resolución siempre han tenido una gran demanda debido al mayor detalle y precisión que ofrecen, así como al amplio alcance de los campos en los que se podrían aplicar; sin embargo, los satélites en operación que ofrecen imágenes de muy alta resolución (VHR) han experimentado un aumento importante, pero se mantienen en una proporción menor frente a los satélites de menor resolución (HR) existentes. Los modelos recientes de redes neuronales convolucionales (CNN) son muy adecuados para aplicaciones con procesamiento de imágenes, como la mejora de la resolución de imágenes; pero para obtener un resultado aceptable, es importante, no solo definir el tipo de arquitectura de la CNN, sino también el conjunto de imágenes de referencia para entrenar el modelo. Nuestro trabajo propone una alternativa para mejorar la resolución espacial de las imá...
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La detección de rostros es una de las tareas esenciales ampliamente estudiadas en el campo de la Visión por Computador. Varios autores han desarrollado diferentes técnicas para mejorar la detección de rostros en imágenes, pero estas se ven limitadas en su aplicación en videos y más si presentan baja resolución. En este estudio, proponemos un nuevo modelo para la detección de rostros en videos de baja resolución basado en la morfología de la parte superior del cuerpo de las personas y el uso de Deep Learning (CNN). Nuestros resultados muestran un promedio de 39 % de precisión en el conjunto de datos de Caviar y 32 % en el conjunto de datos de UCSP. En comparación con otras técnicas, nuestros resultados son mayores debido a que solo alcanzan el 1% de precisión.
3
artículo
Publicado 2020
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Remote sensing is widely used to monitor earth surfaces with the main objective of extracting information from it. Such is the case of water surface, which is one of the most affected extensions when flood events occur, and its monitoring helps in the analysis of detecting such affected areas, considering that adequately defining water surfaces is one of the biggest problems that Peruvian authorities are concerned with. In this regard, semiautomatic mapping methods improve this monitoring, but this process remains a time-consuming task and into the subjectivity of the experts.In this work, we present a new approach for segmenting water surfaces from satellite images based on the application of convolutional neural networks. First, we explore the application of a U-Net model and then a transfer knowledge-based model. Our results show that both approaches are comparable when trained using ...
4
artículo
Publicado 2020
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Recently, the number of satellite imaging sensors deployed in space has experienced a considerable increase, but most of these sensors provide low spatial resolution images, and only a small proportion contribute with images at higher resolutions. This work proposes an alternative to improve the spatial resolution of Landsat-8 images to the reference of Sentinel-2 images, by applying a Super Resolution (SR) approach based on the use of Generative Adversarial Network (GAN) models for image processing, as an alternative to traditional methods to achieve higher resolution images, hence, remote sensing applications could take advantage of this new information and improve its outcomes. We used two datasets to train and validate our approach, the first composed by images from the DIV2K open access dataset and the second by images from Sentinel-2 satellite. The experimental results are based on...
5
artículo
Publicado 2019
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Earth's behavior comprehension can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by different satellites sensors, the problem can be regarded as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means algorithms, a clustering technique, as distributed solution, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral ima...
6
artículo
Publicado 2023
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PURPOSEMultiple myeloma (MM) is a highly heterogeneous, incurable disease most frequently diagnosed in the elderly. Therefore, data on clinical characteristics and outcomes in the very young population are scarce.PATIENTS AND METHODSWe analyzed clinical characteristics, response to treatment, and survival in 103 patients with newly diagnosed MM age 40 years or younger compared with 256 patients age 41-50 years and 957 patients age 51 years or older.RESULTSThere were no statistical differences in sex, isotype, International Scoring System, renal involvement, hypercalcemia, anemia, dialysis, bony lesions, extramedullary disease, and lactate dehydrogenase (LDH). The most used regimen in young patients was cyclophosphamide, bortezomib, dexamethasone, followed by cyclophosphamide, thalidomide, dexamethasone and bortezomib, thalidomide, dexamethasone. Of the patients age 40 years or younger, o...
