Aiming at the problems of information distortion， poor objectivity of attribute weights， and high time complexity in hesitant fuzzy clustering analysis， an Agglomerative Hierarchical Clustering algorithm based on Hesitant Fuzzy set （AHCHF） was proposed. Firstly， the average value of hesitancy fuzzy elements was used to expand the data object with small hesitation. Secondly， the weights of data object before and after expansion were calculated by using the original information entropy and internal maximum difference， and the comprehensive attribute weight was determined according to the minimum discrimination information between the two weight vectors. Finally， with the goal of making the sum of weighted distances smaller， a center point construction method with constant hesitation was given. Experimental results on specific examples and synthetic datasets show that compared with the classic Hesitant Fuzzy Hierarchical Clustering algorithm （HFHC） and the recent Fuzzy Hierarchical Clustering Algorithm （FHCA）， the proposed AHCHF increases the mean Silhouette Coefficient （SC） by 23.99% and 9.28% respectively， and shortens the running time by 27.18% and 6.40% averagely and respectively， proving that the proposed algorithm can effectively solve the problems of information distortion and poor objectivity of attribute weights， and improve the clustering effect and performance well.

Since the contradiction between edge-preserving in homogeneous regions and structure-preserving in the boundary region of an image, a new algorithm combined with cross-based framework and guided filter was proposed. The main idea of the algorithm was adding an adjust offset in guided filter to ensure remaining the edge structure. Usually the fixed size window was used as neighborhood filtering, while the new algorithm employed cross-based framework, which chose a threshold in grayscale similarity. Taking the advantage of stereo matching, the adaptive filtering blocks whose sizes and shapes can adjust automatically were generalized. The adjust offset was proportional to the threshold, which was more robust than a hard threshold. In the simulation experiments of processing international standard sequence, the blocks were generalized by cross-based framework efficiently and effectively, homogeneous regions were smoothed well. The added offset outperforms many other algorithms in terms of sharpness enhancement. Compared to the guided filter, the value of Peak Signal-to-Noise Ratio (PSNR) of the proposed method has been improved by about 2 dB. The test results of real natural picture show that the proposed algorithm has a good future in practical application.

To improve the speed of image reconstruction based on fan-beam Filtered Back Projection (FBP), a new optimized fast reconstruction method was proposed for polar back-projection algorithm. According to the symmetry feature of trigonometric function, the preprocessing projection datum were back-projected on the polar coordinates at the same time. During the back-projection data coordinate transformation, the computation of bilinear interpolation could be reduced by using the symmetry of the pixel position parameters. The experimental result shows that, compared with the traditional convolution back-projection algorithm, the speed of reconstruction can be improved more than eight times by the proposed method without sacrificing image quality. The new method is also applicable to 3D cone-beam reconstruction, and can be extended to multilayer spiral three-dimensional reconstruction.

To solve the low efficiency and precision of the traditional methods, a near-duplicate image detection algorithm based on Bag-of-words and Hash coding was proposed in this paper. Firstly, a 500-dimensional feature vector was used to represent an image by Bag-of-words; secondly, feature dimension was reduced by Principal Component Analysis (PCA) and Scale-Invariant Feature Transform (SIFT) and features were encoded by Hash coding; finally, dynamic distance metric was used to detect near-duplicate images. The experimental results show that the algorithm based on Bag-of-words and Hash coding is feasible in detecting near-duplicate images. This algorithm can achieve a good balance between precision and recall rate: the precision rate can reach 90%-95%, and entire recall rate can reach 70%-80%.

A median priori image reconstruction algorithm based on mixed model was put forward to solve the problems of over-smoothness and stepladder edge of reconstructed image by Maximum A Posterior (MAP). First,in the median priori distribution of MAP reconstruction method,the combination of wavelet shrinkage and forward-and-backward anisotropic diffusion filter was introduced before each of median filtering. In addition, if the background area still kept a small amount of noise, the fine filter with a nonlinear diffusion that smoothed the smaller image gradient threshold region could be chosen to join in the last of iteration,so as to optimize the image.The simulation results show that the algorithm has good performance in both lowering noise effect and preserving edges. Compared with other classical algorithms,the Signal-to-Noise Ratio (SNR) can be improved by 0.9dB to 3.8dB.

The effect of existing methods for image edge detection is not ideal,and the detected image edge may be fuzzy. Therefore, combining fractional-order differentiation theory with the existing Laplacian operator method, a new image edge detection model based on fractional-order differentiation was proposed. Compared with the existing integer-order differentiation edge detection methods, the experimental results show that the model, not only can detect the image edge well, but also have a certain effect on noise. It can detect more texture detail information especially for texture rich images. It is an effective method for edge detection.

Presently, applying the nature language to explain Telecom Tariff Discount Policies (TTDP) by telecom operators not only makes different people understand differently, but also makes computers not be used to implement automatic conflict detection of reduplicated TTDP. Therefore, aiming at the phenomena of overloading and inefficient Business Sporting Systems (BSS) due to increasing reduplicated TTDP, the algorithm of conflict detection for reduplicated TTDP was proposed and automatic conflict detection was implemented with combination of expert platform. Each discount policy was expressed into the conditional part and conclusive part with traditional knowledge expression method and applying idea of the representative algorithms of conflict detections such as Rete and improved Rete algorithms. The practical results show the efficiency and rationality of the knowledge expression and the conflict detection algorithm for TTDP, better effects with scientific and rational set up of TTDP, decrease in customer complaints, compression of the number of TTDP, improved system efficiency and increased economic and social benefits.

A new watermarking scheme for the copyright protection of 3D mesh models was proposed. In this scheme, the geometric signal processing theory was adopted to transform 3D mesh signals into planar regularly sampling signals, and then Singular Value Decomposition (SVD) technique was employed to embed watermark. Experimental results show that the proposed algorithm has better imperceptibility and stronger robustness.