Texture Classification with Neural Networks
Fecha de publicación
Springer Science + Business Media
Distributed Computing and Artificial Intelligence. 10th International Conference. Advances in Intelligent Systems and Computing. Volumen 217, pp. 325-332.
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.
978-3-319-00550-8(Print) / 978-3-319-00551-5(Online)
2194-5357(Print) / 2194-5365(Online)
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