Research On Image Preprocessing And Targets Recognition Of Imaging Guidance System

As an autonomous intelligent accurate guidance, infrared imaging guidance is playing an increasingly important role in air war. In this system, targets recognition is key to guidance processing. But there are many problems need to be solved in targets recognition and these problems lie in different parts, such as preprocessing, feature extraction and classification. Under this background, this paper is organized on infrared image analyzing and targets recognition. To solve these problems, the image filtering, target segment, feature extraction of 2D and 3D targets recognition are investigated systemically in this dissertation. The important contributions and creative achievements are summarized as follows:1. As the existent filters haven't good effect in denoising and preserving edge, an anisotropic piecewise gaussian filter has been put forward. In this method, the parameters of filter are estimated to make it smooth only in effective neighbor. But the computation amount of this anisotropic piecewise gaussian filter is large, so a piecewise moment filter is developed in this dissertation. Based on fast convolution and stair edge model, the moment filter can make a fast smoothing in effective neighbor. The experiment result shows that this new method has better capability than other common methods in suppressing noise and preserving edge.2. Image segment is the base of feature extraction, but the standard segmentation methods are ineffective for extracting target in nonuniform background. To solve the problem, a surface fitting segmentation method is proposed. Firstly, a statistical model of noise has been used to estimate the variance of noise. Then the fairing constrained surface fitting is made for the image background. The experimental result shows that the new method has better capability than other common methods in removing background and extracting target.3. To increase the speed of computation and simplify the system, 2D targets recognition system is often used. In this dissertation, a polar-projection moment is proposed to solve the problems that the invariant features aren't stable and the computational amount of trace transform is large. In the polar-projection moment, dimension of these features can be adapted by the number of invariant functions. The simulation shows that these features have mighty stability when the target have scale, rotational and noise changes and classification experiment demonstrated that these features have good effect in classification.4. Furthermore, affine invadant features also have the problems that invariant features aren't stable and the matching process of normalized contour is complex. To solve these problems, a affine-projection moment is proposed. These features can realize invariance by normalization and scale average. After that, dimension of features also can be adjusted by the number of invariant functions. Simulation explains that these features are stable and the classification experiment displays that the data can be classified well by this affine projection moment.5. To get the least information redundancy of orthogonal moment, a K-L fourier moment is put forward by involving K-Ltransform to get the best orthogonal moment in recognition. But the K-L orthogonal bases are not fixed orthogonal bases and it is not good at recognizing the multi-classification data, so a cosine fourier moment is proposed. Because the cosine transform is the best approximation to K-L transform in one order markov process, the cosine fourier moment is the suboptimal orthogonal moment. As the cosine fourier moment can be divided into independent cosine transform and fourier transform, the speed of computation can be increased. The analysis in theory and experiments show that this feature can obtain stable invariance and classification ability.6. To reduce the excessive sub-classifier and complex structure of SVM multi-classification, a minimal random risk method is proposed by analyzing the risk of structure. When the same SVM classifiers are used in classification, the random structural risk of system is minimized. The experiment presents that it can get better result than DAG method and it's speed is high. Finally in the experiment, the recognition system composed of cosine fourier moment and SVM gets higher classification accuracy in processing 3D target.