- Texture boundaries or edges are useful information for segmenting heterogeneous textures into several classes. Texture edge detection is different from the conventional edge detection that is based on the pixel-wise changes of gray level intensities, because textures are formed by patterned placement of texture elements over some regions. We propose a prediction-based texture edge detection method that includes encoding and prediction modules as its major components. The encoding module projects n-dimensional texture features onto a 1-dimensional feature map through SOFM algorithm to obtain scalar features, and the prediction module computes the predictive relationship of the scalar features with respect to their neighbors sampled from 8 directions. The variance of prediction errors is used as the measure for detection of edges. In the experiments with the micro-textures, our method has shown its effectiveness in detecting the texture edges.