A texture extraction technique using 2D-DFT and Hamming distance

Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. This paper presents a novel technique for texture extraction and classification. The proposed feature extraction technique uses 2D-DFT transformation. A combination of this technique and a Hamming Distance based neural network for classification of extracted features is investigated. The experimental results on a benchmark database and detailed analysis are presented.