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According to the structural characteristics of thin-walled parts, a model slicing method is proposed, and its mathematical process is established. The three-dimensional transient temperature field in the process of synchronous powder feeding laser cladding is studied and verified by numerical simulation method, and the thin-walled parts formed by later experimental processing are processed by the results of numerical simulation. Using the simulation results of temperature field as the basis for optimizing the processing parameters, the forming path of thin-walled parts is programmed and optimized, and the experimental verification shows the reliability of this method.

Aiming at the fusion of visible and Passive Millimeter Wave (PMMW) images, a novel algorithm based on clustering and NSCT (Nonsubsampled Contourlet Transform) is proposed. It takes advantages of the particular ability of PMMW image in presenting metal target and uses the clustering algorithm for PMMW image to extract the potential target regions. In the process of fusion, NSCT is applied to both input images, and then the decomposition coefficients on different scale are combined using different rules. At last, the fusion image is obtained by taking the inverse NSCT of the fusion coefficients. Some methodologies are used to evaluate the fusion results. Experiments demonstrate the superiority of the proposed algorithm for metal target detection compared to wavelet transform and Laplace transform.

The fast compressive tracking (FCT) algorithm is a simple and efficient algorithm, which is proposed in recent years. But, it is difficult to deal with the factors such as occlusion, appearance changes, pose variation, etc in processing. The reasons are that, Firstly, even if the naive Bayes classifier is fast in training, it is not robust concerning the noise. Secondly, the parameters are required to vary with the unique environment for accurate tracking. In this paper, we propose an improved fast compressive tracking algorithm based on online random forest (FCT-ORF) for robust visual tracking. Firstly, we combine ideas with the adaptive compressive sensing theory regarding the weighted random projection to exploit both local and discriminative information of the object. The second reason is the online random forest classifier for online tracking which is demonstrated with more robust to the noise adaptively and high computational efficiency. The experimental results show that the algorithm we have proposed has a better performance in the field of occlusion, appearance changes, and pose variation than the fast compressive tracking algorithm’s contribution.

A novel double-threshold Otsu algorithm based on prior area is proposed to improve the detection accuracy of small target in this paper. The proposed algorithm makes good use of the area information of each region in images to improve the accuracy of target detection, and can handle scenes containing small target which has low computational complexity, and achieves robust detection for concealed target in Passive Millimeter Wave (PMMW) images. Experiments are conducted on PMMW images collected by PMMW imaging system. The experimental results show that the proposed algorithm can effectively detect the concealed targets, and has better performance in target detection than other traditional threshold segmentation algorithm based on Otsu.