Almost all of existing works on structure-preserving texture smoothing utilize the statistical features of pixels within local rectangular patches to distinguish structures from textures.However, the patch sizes of the rectangular regions are single-scale, which may lead to texture over-smoothed or non-smoothed for images with sharp structures or structures at different scales. Thus, an adaptive scale bilateral texture filtering method was proposed. Firstly, the patch size of rectangular region for each pixel was chosen adaptively from some given candidate sizes based on statistical analysis of local patches, where larger patch sizes were chosen for the homogeneous texture regions and smaller ones for regions near the structure edges. Secondly, guided image were computed via the adaptive patch sizes. Finally, the guided bilateral filtering was operated on the original image. The experimental results demonstrate that the proposed method can better preserve image structures and smooth textures.

The commonly used Least Square (LS) ellipse fitting algorithm based on minimum algebraic distance is simple and easy to implement, but it has no choice to the sample points, which leads to the fitting results are easily inaccurate due to the error points. According to this case, an improved ellipse fitting algorithm based on Letts criterion was proposed to overcome the shortage of LS algorithm. Firstly, the ellipse was fitted from the fitting curve by using the LS ellipse fitting algorithm based on minimum algebraic distance. Then, the algebraic distance of ellipse fitted by LS algorithm from the point distance on the fitting curve was set as the fitting point set. After the point set was verified to be normal distribution, the points which were greater than|3 σ|were determined to be outliers and eliminated by using Letts criterion. Then the steps above were repeated until all points were within the scope of  [-3σ ,3σ ]. Finally, the best fitting ellipse was obtained. The simulation experiment results show that the fitting error of the improved algorithm based on Letts criterion is within 1.0%, and its fitting accuracy is improved by at least 2 percentage points compared with the LS algorithm under the same condition. The simulation result and the practical application in roundness measurement of cigarette verify the effectiveness of the improved algorithm.

To eliminate the influence of visual difference on color image filtering, a method to compute the visual sensitivity value of three primary colors was given. The filter algorithm detected pixels by rough set first, then used visual sensitivity to correct the test results, finally filtered the noise pixels by the improved Directional Distance Filter (DDF). The experimental results show that the new filter, compared with several other representative vector filters, provides better performance on color keeping, edge detail preservation, and noise filtering rate. In addition, the normalized mean square error of our method is the smallest and the Peak Signal-to-Noise Ratio (PSNR) is the largest under different noise density.

Regarding the existing normative data quality issues of the national land use/cover at scale of 1 to 250000 as the research object, the open source Geographical Information System (GIS) software was used to build a normative quality evaluation system that had characteristics of simple operation, objective evaluation and comprehensive inspection. This quality inspection system is a new rules-based quality evaluation system. According to different data quality issues, it flexibly designed quality inspection rules, and effectively integrated into the system. The experimental results show that the system can flexibly and efficiently check out the land use/cover normative data quality issues, and give an objective evaluation.

Fast and accurate identification of the true type of an arbitrary file is very important in information security. Concerning the problems of current contentbased file type identification algorithms, variablelength gram was introduced for describing statistic characteristics of files binary content, and a new evaluation function combining gram divergence, stability and conditional width was adopted for feature selection for structured file types. This algorithm does not rely on the structure and key words of any specific file types, which allows the approach to be applied more widely. The experimental results show that the proposed approach improves the precision and recall of file type identification.