Texture Classification with Neural Networks
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.
KeywordsNeural Network Neural Network Model Color Channel Aerial Photography Neural Network System
Unable to display preview. Download preview PDF.
- 2.Encog Online Documentation: Resilient Propagation. Heaton Research, http://www.heatonresearch.com/wiki/
- 3.Getreuer, P.: Chan-Vese Segmentation. Image Processing On Line (2012)Google Scholar
- 5.Natarajan, K., Subramanian, V.: Texture Classification, Using Neural Networks to Differentiate a Leopard from its Background. Science Applications for NeuroDimension, http://www.nd.com/apps/science.html
- 8.Yu, G., Slotine, J.J.: Fast Wavelet-Based Visual Classification. In: Proc. IEEE International Conference on Pattern Recognition, Tampa (2008)Google Scholar