A novel algorithm is proposed to learn pattern similarities for texture image retrieval. Similar patterns in di erent texture classes are grouped into a cluster in the feature space. Each cluster is isolated from others by an enclosed boundary, which is represented by several support vectors and their weights obtained from a statistical learning algorithm called support vector machine (SVM). Th...

Herein, we propose a novel multi-layer Markov random field (MRF) image segmentation model which aims at combining color and texture features: Each feature is associated to a so called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation ...

Image segmentation plays an important role in human vision, computer vision, and pattern recognition fields. Segmentation based on texture can improve the accuracy of interpretation. Satellite images are used in order to detect the distribution of classes such as soil, vegetation, built-up areas, roads, rivers, lakes etc. A problem that arises when segmenting an image is that the number of feat...

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 met...

In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initia...

This paper proposes a method to control texture resolution for rendering large-scale 3D urban maps. Since on the 3D maps texture data generally tend to be far larger than geometry information such as vertices and triangles, it is more effective to reduce the texture by exploiting the LOD (Level of Detail) in order to decrease whole data size. For this purpose, we propose a new method to control...

Texture-based computerized analysis of high-resolution computed tomography images from patients with interstitial lung diseases is introduced to assist radiologists in image interpretation. The cornerstone of our approach is to learn lung texture signatures using a linear combination of N-th order Riesz templates at multiple scales. The weights of the linear combination are derived from one-ver...

Texture can be considered as a repeating pattern of local variation of pixel intensities. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. One of the difficulties in texture classification was the lack of tools that characterize textures. Classification of textures has received attention during last few decades. As DCT works on gray leve...

Texture classification plays an important role in computer vision and image processing applications, which has received considerable attention over the last several decades. In this paper, diffusion approach for texture analysis based on anisotropic diffusion and local directional binary patterns (LDBP) is presented. The proposed approach uses partial differential equation (PDE) as the pre proc...