Texture Analysis

CVGResearch Home 3D Reconstruction Texture Analysis Shape Analysis Segmentation Recognition Fractal Graphics Members				Texture Analysis The unevenness patterns of the pixel attributes in a region give rise to the visual perception of image texture. Texture reflects the variations of the optical properties of object surfaces. Different materials usually produce distinctive surface texture. This makes image texture an important source for discriminant information. Texture plays an important role in various recognition problems such as product surface inspection, recognition of objects in images, medical screening and diagnosis.
				Classifying image texture with statistical landscape features. Cun Lu Xu, Yan Qiu Chen. Pattern Analysis and Applications, Volume 8, Number 4, Pages 321-331, 2006
				Abstract. This paper proposes to use three-dimensional information derived from the graph of an image function for texture description. The graph of an image function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous images, the entire Brodatz texture set, as well as the VisTex texture collection.

				Artificial Life: A New Approach to Texture Classification. Duo Zhang, Yan Qiu Chen. International Journal of Pattern Recognition and Artificial Intelligence, Vol. 19, Issue 3, Pages 355-374, 2005
				Abstract. This paper presents a novel approach to image texture classification, which involves a model of artificial organisms i.e. Artificial Crawlers (ACrawlers) and a series of evolution curves representing the features of the texture. The distributed ACrawlers locally interact with their living environment, i.e. textured regions, and each ACrawler acts according to a set of homogenous rules for isotropic motion, energy absorption and colony formation, etc. The ACrawlers evolve through natural selection, which produces the specific curves of agent evolution, habitant settlement and colony formation as well as the scale distribution of all colonies. The feasibility and effectiveness of the proposed method have been demonstrated by experiments.

				Statistical geometrical features for texture classification. Yan Qiu Chen, Mark S. Nixon, David W. Thomas. Pattern Recognition, Volume 28, Issue 4, Pages 537-552, 1995
				Abstract. This paper proposes a novel set of 16 features based on the statistics of geometrical attributes of connected regions in a sequence of binary images obtained from a texture image. Systematic comparison using all the Brodatz textures shows that the new set achieves a higher correct classification rate than the well-known Statistical Gray Level Dependence Matrix method, the recently proposed Statistical Feature Matrix, and Liu's features. The deterioration in performance with the increase in the number of textures in the set is less with the new SGF features than with the other methods, indicating that SGF is capable of handling a larger texture population. The new method's performance under additive noise is also shown to be the best of the four.