The Application Of Invariants Theory In Pattern Recognition

In object recognition system the objects always make movements relative to the camera, which represent as translation, scaling, rotation and skew of the objects. In this article Invariant Theory is used in the image recognition to resolve the image distortions problems. This paper studied the invariable character of the moment techniques and used the character to resolve the image distortions. The work of this paper mainly includes two parts.The first part looked back the moment techniques and some characters of it. Then a new method for image normalization is provided, which can correctly normalize the images distorted by translation, scaling, rotation, and skew transformations, while extant normalization methods can normalize three distortions at most. This new method first process the input image using compact algorithm, then rotate the compact image according to image eclipse tilt angle.In the second part a new set of rotation invariant features for image recognition is introduced. The features are the magnitudes of a set of orthogonal complex moments of the image called Zernike moments. Taking advantage of the orthogonal property, a systematic feature selection method for choosing an appropriate number of the Zernike features is developed. It is based on computing a measure of the information content differences of features of different classes. The performance of the method is experimentally tested on a 26-class data set involving differently oriented binary images. At last the system is added to a watching system. The recognition result proved that the method introduced in this part is more effective for the image recognition.