A Content Based Image Retrieval System is a computer system for browsing, searching and retrieving images from a large database of digital images .Most common methods of image retrieval utilize some method of adding meta data such as captioning, keywords or description to the images so that retrieval can be performed over the annotation words. Content Based Image Retrieval (CBIR) deals with ret...

This paper develops a constitutive description and a computational treatment of anisotropic friction within the context of the finite element method. The theoretical approach makes use of structural tensors that characterize texture, are convected with the material surfaces, and represent the symmetries that reflect changes in relative orientation of the surfaces. In the discrete setting, the t...

This paper presents a new efficient and fast approach to texture characterization for high-speed web inspection applications (mainly focused on textured material grading and fault detection). The texture description proposed makes use of semicover concept over binary planes derived from grey images. A measure of the local semicover tendency based on joint occurrences of elementary semicover pat...

The one-dimensional (1-D) Boolean model is used to calculate features for the description of binary textures. Each two-dimensional (2-D) texture is converted into several 1-D strings by scanning it according to raster vertical, horizontal or Hilbert sequences. Several different probability distributions for the segment lengths created this way are used to model their distribution. Therefore, ea...

A dynamic texture (DT) refers to a sequence of images that exhibit spatial and temporal regularities. The modeling of DTs plays an important role in many video-related vision tasks, where the main difficulty lies in fact how to simultaneously depict the spatial and temporal aspects of DTs. While unlike the modeling of DTs, tremendous achievements have been recently reported on static texture mo...

A system is proposed which analyzes various textures statistically or structurally, depending on the nature of the textures. The system first describes a texture globally by both gray level first order statistics and edge statistics to roughly classify it. Next, the system extracts homogeneous gray level regions to try to describe the texture structurally. However, if too many regions are extra...

Texture description contributes as one of the most important low-level features in content-based image retrieval. In MPEG-7, homogeneous texture descriptor (HTD), texture browsing descriptor (TBD), and edge histogram descriptor (EHD) have been proposed as texture descriptors. However, no comprehensive evaluation and comparison of these three descriptors have been made. In this paper, we propose...

Although most of the theoretical and implementation aspects of wavelet based algorithms in texture characterization are well studied and understood, many issues related to the choice of filter bank in texture processing remain unresolved. The impact of the wavelet basis has been mentioned in a few papers, whereas other more detailed investigations have considered only the choice of the wavelet ...

BACKGROUND AND PURPOSE Texture analysis is an image processing technique that can be used to extract parameters able to describe meaningful features of an image or ROI. Texture analysis based on the gray level co-occurrence matrix gives a second-order statistical description of the image or ROI. In this work, the co-occurrence matrix texture approach was used to extract information from brain M...