2026-09-17T14:56:45Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1351792025-05-30T09:17:59Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134486

Raveane, William González Arrieta, María Angélica 2013-05 Texture classification poses a well known difficulty within computer vision systems. This paper reviews a method for image segmentation based on the classification of textures using artificial neural networks. The supervised machine learning system developed here is able to recognize and distinguish among multiple feature regions within one or more photographs, where areas of interest are characterized by the various patterns of color and shape they exhibit. The use of an enhancement filter to reduce sensitivity to illumination and orientation changes in images is explored, as well as various post-processing techniques to improve the classification results based on context grouping. Various applications of the system are examined, including the geographical segmentation of satellite images and a brief overview of the model’s performance when employed on a real time video stream. application/pdf http://dx.doi.org/10.1007/978-3-319-00551-5_40 http://hdl.handle.net/10366/135179 en Springer Science + Business Media Texture Classification with Neural Networks info:eu-repo/semantics/conferenceObject TEXT Gredos. Repositorio Documental de la Universidad de Salamanca Hispana