The current skeletonisation algorithms, based on thinning, extract the morphological features of an object in an image but the skeletonized objects are coarsely presented. This paper proposes an algorithm which goes beyond that approach by changing the coarse line segments into perfect "straight" line segments, obtaining points, angles, line segment size and proportions. Our technique is applied in the postprocessing phase of the skeleton, which improves it no matter which skeletonisation technique is used, as long as the structure is made with one-pixel width continuous line segments. This proposal is a first step towards human activity recognition through the analysis of human poses represented by their skeletons.
Cardiovascular diseases are the leading cause of death worldwide. Therefore, getting help in time makes the difference between life and death. In many cases, help is not obtained in time when a person is alone and suffers a heart attack. This is mainly due to the fact that pain prevents him/her from asking for help. This article presents a novel proposal to identify people with an apparent heart attack in colour images by detecting characteristic postures of heart attack. The method of identifying infarcts makes use of convolutional neural networks. These have been trained with a specially prepared set of images that contain people simulating a heart attack. The promising results in the classification of infarcts show 91.75% accuracy and 92.85% sensitivity.
En la actualidad se ha creado una dinámica en la cual las personas exigen una mayor calidad de imagen en diferentes medios (Juegos, películas, animaciones). Una mayor definición por lo general requiere el procesamiento de imágenes de mayor tamaño, esto trae consigo la necesidad de aumentar la capacidad de cálculo. El presente artículo expone un caso de estudio en el cual se muestra la implementación de una plataforma de bajo costo, sobre la nube de Amazon, para el procesamiento (renderizado) de imágenes y animaciones de forma paralela.
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