Browsing by Subject "PROCESAMIENTO DE IMAGEN"

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    PublicationOpen Access
    Descripción de un sistema para la medición de las presiones plantares por medio del procesamiento de imágenes. Fase I
    (2014-05-08) Autor Díaz-León, C. A. (Christian Andrés); Torres-Velásquez, A. (Andrés); Ramirez-Giraldo, J. I. (Jose Ignacio); Garcia-Muriel, L. F. (Luisa Fernanda); Álvarez-Agudelo, N. (Natalia)
    The measurement of the plantar pressure is basic to know the force distribution in the sole. These values are important to diagnosis and treat different pathologies as diabetic ulcerate in the sole, bone, and muscular deformations in the lower limb, etc. This paper describes the development of a pedobarograph in order to analyze the sole. The development can be classified in three stages, building of the Podoscopio, selection of the transducer material and development of the software in order to processes the picture captured by the camcorder. The device PodoMED makes a dynamic and static analysis of the sole, using pseudocolor images for walking analysis and podological indexes as valgo index, Clarke angle, Hernandez Corvo index, etc. for static analysis. In the development of this project we carried out experimental test to minimize the noise present in the image. It was achieved by using a physical filter and image processing filter. In a future phase, it plans the system calibration and validation, which expects to offer more reliability itself.
  • Thumbnail Image
    PublicationOpen Access
    Descripción del método log-vision de procesamiento de imágenes en color en aplicaciones a imágenes oftálmicas
    (2014-04-20) Valencia-Díaz, E. (Edison)
    This paper describes an algorithm to sharpen a colour digital image based on S-CIE LAB extension. S-CIE LAB involves a series of smoothing spatial filters in the opponent color space to approximate the contrast sensitivity functions of the human vision system. The filters are linear combinations of Gaussian masks. The algorithm combines these spatial filters with the Laplacian operator in each opponent channel to obtain the sharpened image. The resulting image is then subtracted from the original image in each opponent channel and back transformed to the device independent representation space (XYZ) to obtain the final sharpened image. This article describes two examples that use the sharpening algorithm with medical images of the ophthalmic area.