An online multiple target tracking method for the aerial infrared targets was proposed based on the hierarchical data association to solve the tracking difficulty caused by the high similarity, large number and large false detections of the targets in star background. Firstly, according to the characteristics of the infrared scene, the location features, gray features and scale features of the targets were extracted. Secondly, the above three features were combined to calculate the preliminary relationship between the targets and the trajectories in order to obtain the real targets. Thirdly, the obtained real targets were classified according to their scales. The large-scale target data association was calculated by adding three features of appearance, motion and scale. The small-scale target data association was calculated by multiplying the two features of appearance and motion. Finally, the target assignment and trajectory updating were performed to the two types of targets respectively according to the Hungarian algorithm. Experimental results in a variety of complex conditions show that:compared with the online tracking method only using motion features, the proposed method has the tracking accuracy improved by 12.6%; compared with the method using multi-feature fusion, the hierarchical data correlation of the proposed method not only improves the tracking speed, but also increases the tracking accuracy by 19.6%. In summary, this method not only has high tracking accuracy, but also has good real-time performance and anti-interference ability.

When noise and intensity distortion exist in brain images, the method based on structural information cannot accurately extract image intensity information, edge and texture features at the same time. In addition, the computational complexity of continuous optimization is relatively high. To solve these problems, according to the structural information of the image, a non-rigid multi-modal brain image registration method based on Improved Zernike Moment based Local Descriptor (IZMLD) and Graph Cuts (GC) discrete optimization was proposed. Firstly, the image registration problem was regarded as the discrete label problem of Markov Random Field (MRF), and the energy function was constructed. The two energy terms were composed of the pixel similarity and smoothness of the displacement vector field. Secondly, a smoothness constraint based on the first derivative of the deformation vector field was used to penalize displacement labels with sharp changes between adjacent pixels. The similarity metric based on IZMLD was used as a data item to represent pixel similarity. Thirdly, the Zernike moments of the image patches were used to calculate the self-similarity of the reference image and the floating image in the local neighborhood and construct an effective local descriptor. The Sum of Absolute Difference (SAD) between the descriptors was taken as the similarity metric. Finally, the whole energy function was discretized and its minimum value was obtained by using an extended optimization algorithm of GC. The experimental results show that compared with the registration method based on the Sum of Squared Differences on Entropy images (ESSD), the Modality Independent Neighborhood Descriptor (MIND) and the Stochastic Second-Order Entropy Image (SSOEI), the mean of the target registration error of the proposed method was decreased by 18.78%, 10.26% and 8.89% respectively; and the registration time of the proposed method was shortened by about 20 s compared to the continuous optimization algorithm. The proposed method achieves efficient and accurate registration for images with noise and intensity distortion.