Visual Pattern Recognition by Moment Invariants不变矩阵视觉模式识别英文文献翻译.doc

Visual Pattern Recognition by Moment Invariants
MING-KUEI HU, SENIOR MEMBER, IRE
Summary
In this paper a theory of two-dimensional moment invariants for planar geometric figures is presented. A fundamental theorem is established to relate such moment invariants to the well known algebraic invariants. Complete systems of moment invariants under translation, similitude and orthogonal transformations are derived. Some moment invariants under general two-dimensional linear transformations are also included.
Both theoretical formulation and practical models of visual pattern recognition based upon these moment invariants are discussed. A Simple simulation program together with its performance are also presented. It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be plished. It is also indicated that generalization is possible to include invariance with parallel projection.
I. INTRODUCTION
Recognition of visual patterns and characters independent of position, size, and orientation in the visual field has been a goal of much recent research. To achieve maximum utility and flexibility, the methods used should be insensitive to variations in shape and should provide for improved performance with repeated trials. The method presented in this paper meets all these conditions to some degree.
Of the many ingenious and interesting methods so far devised, only two main categories will be mentioned here: 1) The property-list approach, and 2) The statistical approach, including both the decision theory and approaches [1]. The property-list method works very well when the list is designed for a particular set of patterns. In theory, it is truly position, size, and orientation independent, and may also allow for other variations. Its severe limitation is that it es quite useless, if a different set of patterns is presented to it. There is no known method which can generate automatically a new pro