Research And Application Of Image Edge Feature Extraction Based On Multiple Information Fusion And Image Registration Technique

Multi-source information fusion is the technique that utilizes different information from the same object synthetically in order to obtain the more objective and entitative information for this object. Multi-source information fusion technique had its beginnings the early 1970's firstly. Ever since its emergence, this technique has made a strong impact on entire information science, such as image fusion. At the same time, studies show that vision information achieves 75% in all the information received by human. Images contain rich information, and how to obtain this information and utilize it synthetically by effective method is important subject in the pattern recognition field.This dissertation applies the idea of multi-source information fusion. The whole dissertation is closely linked to edge detection based on fusing multiple information, secondary edge feature extraction and register multiple images based on edge feature. The edge detection and image registration are the two main topics in this dissertation. Image edge is the most important information in image, and edge detection is the basis for the follow-up work generally. And image registration belongs to image pattern recognition research field. And the image registration technique is used mainly for multiple images fusion and has practical significance in the field of military and remote sensing.The work in the dissertation is focus on the studying of edge detection, secondary edge feature extraction and image registration. The main goal is to improve the stability and real-time performance. And this study is applied to AGVS, SAR image and numeral character recognition, etc. The major contributions in the dissertation are as follows:(1) To overcome the contradiction between multi-dimensional vector and small training set, element correlation in vectors, computational efficiency when applying Bayesian statistical inference theory, an improved dimensional reduction method is proposed for multi-dimensional data space. The reduction direction computation is robust to outliers by introducing weight function and adjustment factor. Absolute gradient and relative gradient at several scales are constructed to be the multi-dimensional image information. Firstly, the multi-dimensional image information is reduced. Then Bayesian statistical inference theory is employed to fuse multiple features to accomplish edge detection. This method is applied to edge detection in complex scene and guided line detection in AGVS.(2) With the research on DS evidence theory, an improved relevant evidence fusion algorithm is proposed. This method overcomes overestimation with evidence fusion, improve the reasonableness of fusion result and suitable for multiple relevant evidence fusion. Correlation degree is computed by the contribution of evidence reliability. Then a novel edge detection model based on evidence theory is also constructed. This edge detection method is used to fuse ROA operator responses and gradient operator responses at two scales in order to realize SAR image edge detection. The major advantage of this method is fusing information directly to realize edge detection without learning process.(3) A novel image registration method based on edge fitting is proposed. This method is based on edge feature and fit these edges to straight line feature which can be expressed with mathematical method easily through an alterable precision fitting algorithm. Based the relationship of edge and image registration, straight line is screened and set a reliability value. And a weighted voting algorithm is employed to perform registration process to ensure the registration performance.(4) According to the character of image registration, traditional Hasudorff distance is transformed to a normalized similarity value.. The traditional edge curvature computation is adjusted in order to extract the edge corners more stable. "Local jet", edge geometry and distance sequence restriction are used to improve the computation efficiency. Improved Hausdorff distance and morphological method are employed to perform registration process. This registration method is applied to route mark recognition in AGVS, SAR and aerial images registration. Experiments show that this registration method is effective and feasible.(5) Image template matching is the spectral form of image registration. For the two application background of characters on steel billets and working station mark in AGVS, two novel numeral character recognition methods are proposed. For characters on steel billets, classical template matching technique was improved by membership assignment conception in order to improve the recognition stability. For working station mark in AGVS, first skew rectify and second own rectify are proposed to make distorted character closed to natural character. Then the two stage template matching method is employed to recognize the numeral guide character fast and stably.