Slope units are widely used in the prevention and evaluation of landslide-based geological hazards, whose extraction and division are the primary target and important foundation for the risk assessment of landslide hazards. Considering the parallel boundaries and incorrect segmentation problems of the slope units extracted by traditional Geographic Information System (GIS) method, a slope unit extraction algorithm based on texture watershed was proposed, in which slope units were extracted by segmenting terrain images. Firstly, a Digital Elevation Model (DEM) image was obtained by the pretreatment of terrain data, and DEM texture features were extracted by gray level co-occurrence matrix. Then, the gradient image with gray level fused with texture features was calculated and segmented by marker-based watershed segmentation to accurately obtain mountain boundaries and watershed boundaries. Finally, combined with positive and negative terrains, the mountain objects were segmented by watershed segmentation to extract slope units. The experimental results show that the proposed method is pretty effective in segmentation for DEM images of different landform types and resolutions. Compared with traditional GIS method, horizontal planes and inclined planes can be segmented correctly, and the problem of parallel boundaries caused by filling of depressions can be effectively avoided through the proposed method.

In order to solve the problem of selecting center manually in a clustering algorithm, a Parameter-free Clustering Algorithm based on Laplace centrality and density peaks (ALPC) was proposed. Laplace centrality was used to measure the centrality of objects, and a normal distribution probability statistical method was used to determine clustering centers. The problem that clustering algorithms rely on empirical parameters and manually determine cluster centers was solved by the proposed algorithm. Each object was assigned to the corresponding cluster center according to the distance between the object and the center. The experimental results on synthetic data sets and UCI data sets show that the new algorithm can not only automatically determine cluster centers without any priori parameters, but also get better results with higher accuracy compared with the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm, Clustering by fast search and find of Density Peaks (DPC) algorithm and Laplace centrality Peaks Clustering (LPC) algorithm.

Zodiac is a block cipher algorithm and it supports 3 master key lengths which are called Zodiac-128, Zodiac-192 and Zodiac-256. The security of Zodiac algorithm was evaluated by using zero-correlation linear cryptanalysis. Firstly, 10-round zero-correlation linear approximations of Zodiac algorithm were constructed according to the structural characteristics of the algorithm. Then, the multidimensional zero-correlation linear cryptanalysis on 16-round Zodiac-192 was conducted. The analysis results show that 19-byte keys were restored totally in the process of attack, the data complexity was about 2 ^124.40 known ciphertexts and the computational complexity was 2 ^181.58 encryptions of 16-round. Thus the Zodiac-192 algorithm with the 192-bit key of 16 rounds (full rounds) is not immune to the zero-correlation linear cryptanalysis.

Focusing on object contour extraction technology that had the most significant impact on the process of image analysis, an efficient method was proposed for object contour extraction based on probability distribution. First the foreground object was detected and its hue feature was used to build an object probability model. With this model, the probability distribution image was calculated on the whole image for the object. Finally, the contour was obtained from the probability distribution image directly. After testing the method using 2330 continuous images containing moving object, the results suggest that the method can work efficiently. It takes 20ms to 30ms to process a true color image of 640×480 pixels, and the contours extracted are very clear and accurate.