An Improved SOM Based Surface Texture Synthesis
نویسندگان
چکیده
This paper proposed a new method for surface texture synthesis using improved selforganizing maps as the synthesis logic. The method will first loop map the sample image to the target surface until the surface is full filled, then construct a improved selforganizing maps model, in which use sample image as the input level and the target three-dimensional surface as out put level, to adjust the pixel position in target surface, and the mapping area is controlled in the neighborhood of local extremum by reducing search area of variables. The new algorithm not only speed up the synthesis progress, and also enhances the image quality of target surface texture, and compare with the previous algorithm, it is not necessary for the user’s intervention. Compared with previous results for the proof of our concepts, we have successfully implemented the experimental results and the proposed algorithm.
منابع مشابه
Unsupervised self-organizing texture descriptor
We propose a local texture descriptor based on a pyramidal composition of Self Organizing Map (SOM). As with the SOM model, our visual descriptor presents two operational steps: a first unsupervised learning phase and a second mapping phase involving a dimensionality reduction of the input data. During the first step a large number of image patches, including different classes of textures, are ...
متن کاملReal-time surface inspection by texture
In this paper a real-time surface inspection method based on texture features is introduced. The proposed approach is based on the Local Binary Pattern (LBP) texture operator and the Self-Organizing Map (SOM). A very fast software implementation of the LBP operator is presented. The SOM is used as a powerful classifier and visualizer. The efficiency of the method is empirically evaluated in two...
متن کاملLandforms identification using neural network-self organizing map and SRTM data
During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...
متن کاملTexture Classification by Combining Local Binary Pattern Features and a Self-Organizing Map
This paper deals with the combined use of Local Binary Pattern (LBP) features and a Self-Organizing Map (SOM) in texture classification. With this approach, the unsupervised learning and visualization capabilities of a SOM are utilized with highly efficient histogram-based texture features. In addition to the Euclidean distance normally used with a SOM, an information theoretic log-likelihood (...
متن کامل