To address the problem that traditional stream ciphers are not sensitive to changes in the plaintext, a novel plaintext-related color image encryption scheme is proposed in this paper, which combines the 6-dimensional cellular neural network (CNN) and Chen’s chaotic system. This encryption scheme belongs to symmetric cryptography. In the proposed scheme, the initial key and switching function generated by the plaintext image are first utilized to control the CNN to complete the scrambling process. Then, Chen’s chaotic system is used to diffuse the scrambled image for realizing higher security. Finally, extensive performance evaluation is undertaken to validate the proposed scheme’s ability to offer the necessary security. Furthermore, the scheme is compared alongside state-of-the-art algorithms to establish its efficiency.
Compared to raster grid, Triangulated Irregular Network (TIN), and point cloud, the benefit of voxel representation lies in that the implicit notion of adjacency and the true 3D representation can be presented simultaneously. A binary voxel-based data (BVD) model is proposed to reconstruct
aerial lidar point cloud and based on the constructed model 3D ground filtering (V3GF) is developed for separating ground points from unground ones. The proposed V3GF algorithm selects the lowest voxels with a value of 1 as ground seeds and then labels them and their 3D connected set as ground
voxels. The ISPRS benchmark dataset are used to compare the performance of V3GF with those of eight other publicized filtering methods. Results indicate that the V3GF improves on Axelsson's performance on five samples in terms of total error. The average Kappa coefficients for sites with relatively
flat urban areas, rough slope and discontinuous surfaces are 92.49 percent, 72.23 percent and 61.27 percent, respectively.
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