Aiming at the unreasonable problem of current football tactical analysis, this paper studies the application method of the multiple regression method in football tactical analysis. Build a football tactical rehearsal model, optimize player information and tactical analysis management functions, and analyze football player status. Using the multiple regression method to choose the appropriate position of the players, make the football attack and defense plan. At the same time, the application of the multiple regression method to the analysis of football tactics can better select the optimal tactical plan, to ensure the rationality of football tactical selection.

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In holographic display, the reconstructed image suffers from speckle noise severely. In this paper, we propose a method to suppress speckle noise using time multiplexing in phase‐only holographic display. Adjacent pixels of the recorded object are separated into object point groups firstly. Particularly, the pixel interval of each object point group is larger compared with the conventional pixel separation method. And then, sub‐computer–generated holograms (sub‐CGHs) are calculated by the modified Gerchberg–Saxton (GS) algorithm with different initial random phases. Finally, the final integrated image is reconstructed with low speckle noise using time multiplexing technique. Both numerical and optical experimental results are presented to demonstrate the effectiveness and feasibility with our proposed method.

For the problem that the different parameters of infrared imaging equipment and the environment around the target cause the poor robustness of threshold value automatic acquisition method in infrared human target segmentation algorithm, starting from the principle of infrared imagery and connecting with the characteristics of the histogram and K-means clustering algorithm, we propose an infrared image segmentation algorithm using histogram-based self-adaptive K-means clustering. We use histogram peaks to determine the K' value of K-means clustering and select the grey values corresponding to this K peaks as the K initial cluster center values of clustering algorithm. After clustering, we select appropriate trough as a segmentation point through the cluster center's moving direction. This algorithm does not require to balance the image beforehand and to suppose background distribution. The experimental results show that the algorithm is simple and flexible, easy to implement, and has good robustness.