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The simulation of aging appearance is essential in the realistic modeling, We propose a method using existing aging image to simulate aging appearance, We establish the classification library, extract the texture feature of different types aging and save them, set the weight of the texture feature and synthesis texture according to the environment. In addition, user can specify the constrained texture mapping position optionally. This method can simulate various rusty phenomenon simultaneously. It can also be combined with physically-based method and ton spreading-based method to implement rendering of realistic 3D model rapidly.

We adopt a fast image texture recognition technology to identify whether an image for texture image, Then we extract the texture feature for image texture, and to extract the color features for Non-texture images, By classifying different types of image retrieval to improve retrieval efficiency. The experimental results show that, this method of the rapid texture recognition technology can greatly improve the accuracy of image retrieval, and it has a great effect in terms of speed.

The program development process based on the OpenGL in order to make the resulting image with a high degree of realism, we often use technology to manipulate the image texture, because Using texture mapping can easy build abundant and vivid scenes in OpenGL programs, Texture mapping through the pixel and geometry object union, to create a geometry object is very complex, real visual effect, also avoided a lot of geometric model established for overhead.

This paper firstly studies the texture features. We construct a gray-difference primitive co-occurrence matrix to extract texture features by combining statistical methods with structural ones. The experiment results show that the features of the gray-difference primitive co-occurrence matrix are more delicate than the traditional gray co-occurrence matrix.

The method of view-dependent smoothly rendering of large-scale vector data based on the vector texture on virtual globe is presented. The vector texture is rasterized from the vector data based on view-dependent quadtree LOD. And the vector texture is projected on the top of the terrain. The smooth transition of multi-level texture is realized by adjusting the transparency of texture dynamically based on view range in two processes to avoid texture “popping”. In “IN” process, the texture’s alpha value increases when the view range goes up while In “OUT” process, the texture’s alpha value decreases. the vector texture buffer updating method is used to accelerate the texture fetching based on the least-recently-used algorithm. In the end, the real-time large-scale vector data rendering is implemented on virtual globe. The result shows that this method can real-time render large-scale vector data smoothly.

Texture Information is widely used as one of the main low-layer features in the content-based image retrieval. In general, when the retrieval is carried out in texture image space, the same description method is adopted to regular and irregular texture images. As a large amount of regular and irregular texture images existed in the image database, it is very difficult to describe every texture with the same description method. In this paper, a retrieval strategy for texture image is proposed. The proposed strategy is divided into steps: First, classify texture images by Wold decomposition into regular and irregular texture images, then describe and retrieve them by regular and irregular texture description separately. Experimental results have showed that proposed strategy can improve classification and retrieval precision.

Content Based Video Retrieval (CBVR) is becoming a hot research topic. We lay a strong emphasis on the research of texture and shape character pick-up algorithm and retrieval technology, explain the meaning of the parameter of Gray-Level Co-occurrence Matrix, and compare the result of three algorithmCanny, Laplacian and Sobel. The article selects Canny and Gray-Level Co-occurrence Matrix in this system. The experiments show that the performance of the retrieval system based on texture and shape is related with each other, especially in searching the image with typical texture and shape character.

A concept of equivalence classes of texture pattern is put forward according to the visual consistency between the rotation texture and the flip texture. An improved texture spectrum descriptor (iTS) is proposed based on the equivalence classes. The iTS depicts the grayscale variation pattern of the pixels in the image neighbour domain and denotes the texture content of an image with a histogram of texture spectrum. Compared with the basic texture spectrum descriptor (TS), local binary pattern (LBP) and Shis local binary pattern (sLBP), iTS has best precision in the image retrieval experiments. The iTS has stronger ability to describle the texture and more adapt to the image rotation transformation.

Texture recognition plays an important role in machine vision and signal processing. Over the last three decades, many texture analysis and recognition methods have been proposed, but most of them are sensitive to geometric distortion. This paper presents a new approach to scaling and rotation invariant texture classification using invariant moments based on radon transform. The radon transform is utilized to project the texture image onto projection space. Then the geometric invariant moments are calculated by the projection data. The support vector machine-based classifier is employed to implement the texture classification. Experimental results show the high classification accuracy of this approach.

Nature image has complex structure and texture. In past years, people copy the pixels, which come from the surrounding neighborhood or selected example, into damaged region along isophate direction. This might cause such result as blur edges, which looks “unreasonable” to human eyes. This paper proposes a novel algorithm which simultaneously inpaints structures and textures of damaged images. This algorithm use TV to decompose the image into two parts. We inpaint the cartoon image part firstly by boundary reconstruction. Then we do texture synthesis to texture image part guided by boundary reconstruction. The method aims at inpainting structure and texture simultaneously and produces good results for texture with complex structure.

The local binary pattern (LBP) operator has been proved to be theoretically simple yet very effective for texture description. However, it lacks the full description of texture in a region and produces a rather long histogram. A novel texture spectrum descriptor was proposed to alleviate these limitations in the paper. It uses the relation of 3 pixels in an 8-neighborhood, the center and the center-symmetric pixels, to define the local texture patterns. The new operator fully uses the texture information contained in the 8-neighbourhood and produces a rather short histogram. On the other hand, the new operator also has the same desirable properties as LBP, such as tolerance to illumination changes and computational simplicity. Experimental results demonstrate that the new descriptor achieves better performance than the conventional LBP with a rather short histogram

The innovation of this paper is that it forwards a new algorithm of target extraction form military infrared images with texture background according to the Mean-shift smooth and segmentation method combined with eight directions difference clustering. According to the texture characteristics of background image, smoothing and clustering are both carried out to extract the characteristics of target. The method is relatively simple making it easy for practical applications. The experimental results show that the algorithm is able to extract the target information form complex military infrared texture background with better self-adapting and robustness.