In order to solve tracking failure problem caused by target occlusion, appearance variation and long time tracking in practical monitoring, an object tracking algorithm based on RANdom SAmpling Consensus (RANSAC) estimation was proposed. Firstly, the local invariant feature set in the searching area was extracted. Then the object features were separated from the feature set by using the transfer property of feature matching and non-parametric learning algorithm. At last, the RANSAC estimation of object features was used to track the object location. The algorithm was tested on video data sets with different scenarios and analyzed by using three analysis indicators including accuracy, recall and comprehensive evaluation (F1-Measure). The experimental results show that the proposed method improves target tracking accuracy and overcomes track-drift caused by long time tracking.

Concerning the problems of low efficiency and less processing points of the traditional algorithm for point cloud simplification, according to the slicing principle in the rapid prototyping with feature-preserving and low computational complexity, a parallel slicing algorithm was designed and implemented for more than ten millions point cloud of Light Detection And Ranging (LiDAR) data. The point cloud model was layed with the slicing principle and every layer was sorted according to the angle. Incorporating the parallel computation framework of Compute Unified Device Architecture (CUDA) proposed by NVIDA and taking the highly parallel performance advantages of the programmable Graphics Processing Unit (GPU), and parallel execution of the single slice point cloud simplification with the multi-thread of GPU was done, which improved the algorithm efficiency. Finally, a comparing experiment was done with three groups of point cloud data in different order of magnitudes. The experimental results show that the efficiency of the proposed algorithm has 1-2 order of magnitude higher than that of traditional algorithm under the condition of keeping the model characteristics and not changing the compression ratio.

Concerning the influence of changing light, shape, appearance, as well as occlusion on target tracking, a target tracking algorithm based on Speeded Up Robust Feature (SURF) and Multi-Instance Learning (MIL) was proposed. Firstly, the SURF features of the target and its surrounding image were extracted. Secondly, SURF descriptor was introduced to the MIL as the examples in positive and negative bags. Thirdly, all the extracted SURF features were clustered, and a visual vocabulary was established. Fourthly, a "word document" matrix was establish by calculating the importance of the visual words in bag, and the latent semantic features of the bag was got by Latent Semantic Analysis (LSA). Finally, Support Vector Machine (SVM) was trained with the latent semantic features of the bag, so that MIL problem could be handled in accordance with the supervised learning problem. The experimental results show that the robustness and efficiency of the proposed algorithm under the variation of scale, gesture and appearance, as well as short-term partial occlusion.

In order to solve the problem of information loss caused by local motion blur in daily captured images or videos, a local motion detection algorithm based on region energy estimation was proposed. Firstly, the Harris feature points of the image were calculated, and alternative areas were screened out according to the distribution of feature points of each area. Secondly, according to the characteristic of smooth gradient distribution in monochromatic areas, the gradient distribution of the alternative areas was calculated and the average amplitude threshold was used to filter out most of areas which can be easily misjudged. At last, the blur direction of the alternative areas was estimated according to the energy degeneration feature of motion blur images, and the energy of the blur direction and its perpendicular direction were calculated, thus the monochrome region and defocus blur areas were further removed according to the energy ratio in both above directions. Experimental results on image data sets show that the proposed method can detect the motion blur areas from images with monochromatic areas and defocus blur areas, and effectively improve the robustness and adaptability of local motion blur detection.

Spherical Voronoi diagram generating algorithm based on distance computation and comparison of Quaternary Triangular Mesh (QTM) has a higher precision relative to dilation algorithm. However, massive distance computation and comparison lead to low efficiency. To improve efficiency, Graphic Processing Unit (GPU) parallel computation was used to implement the algorithm. Then, the algorithm was optimized with respect to the access to GPU shared memory, constant memory and register. At last, an experimental system was developed by using C++ and Compute Unified Device Architecture (CUDA) to compare the efficiency before and after the optimization. The experimental results show that efficiency can be improved to a great extent by using different GPU memories reasonably. In addition, a higher speed-up ratio can be acquired when the data scale is larger.

Signcryption as a cryptographic primitive is a splendid combination of signature with authentication and encryption with confidentiality simultaneously. Online/offline signcryption, with the online/offline technique, provides higher efficiency for the system. However, most of the present signcryption schemes are implemented in the identity-based setting in which there exists key escrow problem. Based on the certificateless cryptography system's advantages with revocation of certificate management and without key escrow problem, a secure online/offline certificateless signcryption scheme was proposed. The proposed scheme satisfied the requirement that there is no need to determine the recipient's information in the offline stage. Moreover, its security was proved in the Random Oracle Model (ROM).

A method of object detection with few samples based on two-stage voting was proposed to detect objects using template matching method while there are only a few samples. Firstly, the voting space was constructed off-line by using probability model through several samples. Then, a method of two-stage voting was used to detect objects in testing images. In the first stage, the components of object from testing image were detected, and the positions of components in query image were saved. In the second stage, the similarity of the object was computed integrally based on the components. According to the theory analysis and experimental results, the proposed method obtains lower computation complexity and higher precisions than previous works.

The existing algorithms for trajectory prediction have very low prediction accuracy when there are a limited number of available trajectories. To address this problem, the Modified Bayesian Inference (MBI) approach was proposed, which constructed the Markov model to quantify the correlation between adjacent locations. MBI decomposed historical trajectories into sub-trajectories to get more precise Markov model and the probability formula of Bayesian inference was obtained. The experimental results based on real datasets show that MBI approach is two to three times faster than the existing algorithm, and it has higher prediction accuracy and stability. MBI makes full use of the available trajectories and improves the efficiency and accuracy for the prediction of trajectory.

Bayesian network is one of the most important theoretical models for the representation and reasoning of uncertainty. At present, its structure learning has become a focus of study. In this paper, a Bayesian network structure learning algorithm was developed, which was based on topological order and quantum genetic algorithm. With the richness of the quantum information and the parallelism of quantum computation, this paper designed generator strategy of topological order based on a quantum chromosome to improve not only the efficiency of search, but also the quality of Bayesian network structure. And then by using self-adaptive quantum mutation strategy with upper-lower limit, the diversity of the population was increased, so that the search performance of the new algorithm was improved. Compared to some existing algorithms, the experimental results show that the new algorithm not only searches higher quality Bayesian structure, but also has a quicker convergence rate.

Through the analysis of Partial Differential Equation (PDE), the image denoising algorithms based on Laplacian operator and image inpainting were designed for the processing of the polluted image by noise: Rudin-Osher-Fatemi (ROF) harmonical Laplacian algorithm and ROF harmonical inpainting algorithm, which were simply called RHL and RHI respectively. By analyzing the local features of the image, the ability of the ROF model in protecting image edges and the harmonical model in overcoming the "ladder effect", and the advantages of the Laplacian operator in enhancing edges, the first image denoising algorithm, RHL was designed. Meanwhile, the second algorithm RHI was designed by syncretizing the image inpainting model. The experimental results show that the two designed algorithms, RHL and RHI, have better performance visually and quantitatively than other algorithms, which combine the advantages of the ROF model and harmonical model in image denoising effectively. Compared with other PDE based algorithms, the two designed algorithms can remove noise, protect smooth region and edge information much better.

Concerning the inefficient time performance and lower clustering accuracy revealed by the traditional density-based algorithms of DBSCAN and DBRS, this paper proposed an improved density-based clustering algorithm called DBLRS, which is combined with limited regional sampling technique. The algorithm used the parameter Eps to search for the neighborhood and expanded points of a core point without increasing time and space complexity, and implemented data sampling in a limited area (Eps,2Eps). The experimental results confirm that DBLRS can reduce the probability of large clusters' splitting and improve the algorithmic efficiency and clustering accuracy by selecting representative points to expand a cluster.

This paper proposed an Improved Quantum Genetic Algorithm (IQGA) for the problem of slow convergence in test data generation. There are two main improvements. First, every bit of every individual was reversed to conduct the evolution; second, the binary individuals were mutated after measurement, instead of the traditional exchange of the probability amplitude of quantum bits. IQGA was applied into test data generation. The experiments on three basic programs prove that IQGA is better than QGA in terms of coverage rate and the number of iterations. IQGA can not only ensure the right direction of the evolution of populations, but also avoid premature phenomenon, and it can get the solution at a faster convergence speed.

When building up the virtual battlefield system, to realize the three-dimensional (3D) detection range of radar in complex natural environment and complex environment of electronic interference, an improved support jamming model was proposed, according to the fundamental principle of Advanced Propagation Model (APM) and taking full consideration of the influence of electronic interference. This model mixed APM and electronic interference model together, and paid special attention to the refractive influence. Besides, this model could depict the double influence from complex natural and electronic interfering environments. Furthermore, a modified Marching Cube (MC) model, the triangles gained by MC being replaced by surface points and the interpolated points by middle points, was used to accelerate the process of visualization. According to the procedure of data gaining, data processing and data rendering, the 3D detection range of radar on specific electronic jamming environment was rendered via Visualization Toolkit (VTK).