While tracking dim and small moving targets in the electro-optical (EO) tracking system, the numerous false alarms resulted from the low signal-to-noise ratio would seriously debase the performance of target recognition and tracking. The probabilistic data association filter in conjunction with a maximum likelihood approach (PDAF-ML) has been applied effectively to low observable or dim target motion analysis. Whereas, the PDAF-ML supposes that the amplitude of target is not correlative among different sampling instants, and that the greater the amplitude value is, the greater the probability of being the target of interest would be. In the EO imaging tracking system, the amplitude information and the motion of target are consistent and highly correlative in a short period. To resolve the problem that the PDAF-ML is inconsistent with the EO imaging tracking system, the two features, namely, the amplitude information and the motion as well as their consistency, are modeled as Markov stationary random signals and are fused by means of PDAF. Experiments are carried out, and the results show that, with the proposed approach, the uncertainty of trajectory association would be largely decreased, and the performance of target recognition and tracking could be significantly improved.
Many approaches to compute the wavefront of interferometer have been devised, for example least squares method, Gram-Schmidt method, covariance matrix method and SVD method, but one of the most interesting is based on the Zernike Polynomials. Zernike polynomials are ideal for fitting the measured data points in a wavefront to a two-dimensional polynomial, due to their orthogonal properties. The key problem of wavefront fitting is how to express exactly the whole wavefront. In established algorithms, the fixed mode number of Zernike polynomials is used, for example most analyzing software using 36 Zernike polynomials (i.e., Metropro of Zygo). When analyzing high spatial frequency aberrations, the analyzed result is not accurate. We develop a method of wavefront fitting with regression analysis. Regression analysis is the most widely used technique in statistics, and it is a statistical technique for investigating and modeling the relationship between variables. With stepwise regression we obtain the optimum combination of mode, and the wavefront can be exactly expressed.
Segmentation and restoration of highly noisy images is a very challenging problem. There are a number of methods reported in the literature, but more effort still need to be put on this problem.
In this paper we describe the development and implementation of a new effective approach to segmentation and restoration of imagery with pervasive, large amplitude noise. The new approach is based on the recently developed stabilized inverse diffusion equations (SIDE) and mathematical morphology. First, we find an optimized SIDE force function. Secondly, we segment the image to several regions accurately using the SIDE method. Finally a grayscale mathematical morphological filter combined with SIDE is assigned to the initial image data in each region to suppress the noise and to restore the total image. A test study based on available database is presented, and the results so far indicate that this approach to highly noisy imagery segmentation and restoration is highly effective.
Dim point targets detection in highly cluttered backgrounds is a challenging problem. In this paper we describe the development and the implementation of a new effective methodology to detect moving point targets in infrared or visual images. The new approach is based on mathematical morphology and motion analysis. First, the image is filtered by means of gray-scale morphology in order to reject background objects. Then, with the residual image a motion analysis based on trajectory conjunction is carried out to extract the potential point like moving targets. In the motion analysis stage we define a function to describe the characteristics of moving targets, and by this means a strategy is constructed to judge whether or not a given area contains the potential moving target. Through motion analysis, the non-target points are kicked out while the true target points are detected out after merely several frames of image. This approach doesn’t depend on threshold techniques and requires no assumptions about the behavior of the target motion. The only limitation is that the target’s speed doesn’t exceed several pixels per frame. A test study based on available database is presented and the results so far indicate that this approach to detect moving point target is highly effective.
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