Texture is an important spatial feature which plays a vital role in content based image retrieval. The enormous growth of the internet and the wide use of digital data have increased the need for both efficient image database creation and retrieval procedure. This paper describes a new approach for texture classification by combining statistical texture features of Local Binary Pattern and Text...

People like magic. It is fun to imagine a cloud is shaped like a Mickey mouse, or the pattern of leaves and flowers are associated with human face. Sometimes in fiction movie or cartoon production, we like to see similar visual effects. In this paper, we define the problem as following: Given two images, one for pattern and one for shape. We output another image, which draws the shape provided ...

High-capacity image watermarking scheme aims at maximize bit rate of hiding information, neither eliciting perceptible image distortion nor facilitating special watermark attack. Texture, in preattentive vision, delivers itself by concise high-order statistics, and holds high capacity for watermark. However, traditional distortion constraint, e.g. just-noticeable-distortion (JND), cannot evalua...

We present a new method for texture image classification using Basic Gray Level Aura Matrices (BGLAMs). Given an unseen texture image, our approach classifies it into one of the pre-learned classes, each of which is characterized using BGLAMs. There are two stages in our algorithm: a learning stage and a classification stage. In the first stage, models of texture classes are learned from the BG...

We propose a novel sparsity-based method for cartoon and texture decomposition based on Convolutional Sparse Coding (CSC). Our method first learns a set of generic filters that can sparsely represent cartoon and texture type images. Then using these learned filters, we propose a sparsity-based optimization framework to decompose a given image into cartoon and texture components. By working dire...

The statistical approaches such as texture spectrum and local binary pattern methods have been discussed in this paper. The features are extracted by the computation of LBP and Texture Spectrum histogram. A combined approach of LBP with texture spectrum is also proposed further. Experiments of texture feature extraction, classification of textures and similarity-based image-to-image matching ar...

The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, i...

This thesis is concerned with example based image processing. Example based image processing is a general term for any class of image processing operation where the manipulation and analysis of the image in question is guided by some set of example images. This thesis focuses on two applications, texture synthesis and image segmentation, in which example based image processing is proposed. Give...

The algorithm first proposed in [3] stems from a theory proposed by Yves Meyer in [1]. The cartoon+texture algorithm decomposes any image f into the sum of a cartoon part, u , where only the image contrasted shapes appear, and a textural v part with the oscillating patterns. Such a decomposition f=u+v is analogous to the classical signal processing low pass-high pass filter decomposition. Howev...

Features such as edges and corners play an important role in visual data processing. The proposed research employs features to facilitate image segmentation, texture synthesis and visual data approximation. Edges capture primary color/intensity changes in an image, which can be utilized to accelerate image segmentation. In example-based texture synthesis, edges and ridges/valleys can also help ...