Scene segmentation of natural images using texture features and back-propagation

Knowledge of three-dimensional world is essential for many guidance and navigation applications. A sequence of images from an electro-optical sensor can be processed using optical flow algorithms to provide a sparse set of ranges as a function of azimuth and elevation. A natural way to enhance the range map is by interpolation. However, this should be undertaken with care since interpolation assumes continuity of range. The range is continuous in certain parts of the image and can jump at object boundaries. In such situations, the ability to detect homogeneous object regions by scene segmentation can be enhanced by interpolation. This paper describes an image segmentation method based on scalar texture features derived from the spatial gray-level dependence matrix. The scalar features are used with a neural net to automate the segmentation procedure. Back-propagation is used to train a feed forward neural network. It is shown that the use of multiple scalar features as input to the neural network result in a superior segmentation when compared with a single scalar feature