Infrared and Radar data fusion algorithms have drawn a great deal of attention due to its implementation of complementary information, improvement of target tracking and enhancement of system viability. However, in the step of estimating the target state by multi-sensor, different sampling rates between two sensors make it difficult for data fusion. In order to solve this problem and make full use of the advantages of the data obtained by multi-sensor, an effective state estimation algorithm by combining the theory of multi-scale and converted measurement Kalman filter (CMKF) algorithm is presented in this paper. By establishing the multi-scale model, target state is estimated at the finest scale with the Interacting Multiple Model (IMM) algorithm at first. Then, at the coarse scale, appropriate observational information is selected in accordance with specific conditions. Angle information estimated by infrared sensor and the distance information obtained by radar sensor are fused to locate the target when two sensors have the same sampling time instant, otherwise, the target is located only by using the angle and distance information acquired by radar sensor. In addition, CMKF algorithm is used to estimate the target state and obtain the optimal fusion estimation. The simulation results under the environment of MATLAB show that the proposed algorithm effectively improves the precision and the instability of infrared/radar detection system.
In order to improve the precision of visible and infrared (VIS/IR) image registration, an image registration method based on visual salient (VS) features is presented. First, a VS feature detector based on the modified visual attention model is presented to extract VS points. Because the iterative, within-feature competition method used in visual attention models is time consuming, an alternative fast visual salient (FVS) feature detector is proposed to make VS features more efficient. Then, a descriptor-rearranging (DR) strategy is adopted to describe feature points. This strategy combines information of both IR image and its negative image to overcome the contrast reverse problem between VIS and IR images, making it easier to find the corresponding points on VIS/IR images. Experiments show that both VS and FVS detectors have higher repeatability scores than scale invariant feature transform in the cases of blurring, brightness change, JPEG compression, noise, and viewpoint, except big scale change. The combination of VS detector and DR registration strategy can achieve precise image registration, but it is time-consuming. The combination of FVS detector and DR registration strategy can also reach a good registration of VIS/IR images but in a shorter time.
As the electronic image stabilization (EIS) algorithm based on SIFT feature matching has the problem of complex computation and time consuming, a modified EIS algorithm based on PCA-SIFT feature matching and self-adaptive high-pass filtering is proposed in this paper. Firstly, feature points are extracted by using PCA-SIFT algorithm in reference frame and current frame. And the corresponding points are matched between these two images. Then the Random Sample Consensus (RANSAC) algorithm is used to eliminate the error matching pairs to reduce the influence of local motion in the scene and improve the estimation accuracy of global motion parameters. Finally, the random dithering parameters obtained by self-adaptive high-pass filtering are used to compensate the current frames. And the size of filter is adjusted automatically according to dithering frequency to prevent the overstabilization or understabilization. Experimental results show that the algorithm proposed in this paper can effectively remove vectors caused by random dithering and obtain a stable video.
Inspired by the process of manual registration, a method based on visual attention is proposed in this paper for multi-sensor image registration. In the first stage, the corner points are selected from both of the multi-sensor images using multi-scale Harris detector and then the outlines are extracted by Gabor filter. In the second stage, the selected points are described based on the contour images to find the matching pairs. Finally, the parameters of the affine transformation model between the images are obtained according to the matching pairs. Pairs of visible and infrared images are used to evaluate the performance of the proposed algorithm and SIFT algorithm. Experimental results show that the proposed method can achieve good performance for registering visible and infrared images.