Polar-Radius-Invariant-Moment for Pattern Recognition
Cao Mao · Chinese Journal of Computers · 2004
A novel moment, called polar-radius-invariant-moment, is proposed for the object recognition and classification. Following the process of segmentation, we get binaried image. The centroid denoted by (x c ,y c ) of the object is calculated firstly. Then the polar radius r, that is, the distance from an arbitrary point of the object to the centroid,is computed.We define the p - order polar radius moment m p and central moment m c p as m p = ∫∫ Dr p ds, m c p = ∫∫ D(r- r- ) p ds, respectively. Normalized moment and normalized central moment are defined as m np =1A ∫∫ Dr r- p ds, m ncp = 1A ∫∫ Dr- r- r- p ds, respectively, where A is the area of the object and r- =1A ∫∫ Drds is the mean of r. The shifting, rotation and scaling invariance of the normalized moment m np and normalized central moment m ncp are proved theoretically. After that five derived invariant moments are employed as features in the recognition of objects. Examples are presented to illustrate the performance of these moments. In our experiments, we make use of the random movement of the pixels of the object to simulate the noise disturbance, and utilize the minimum distance rule to classify the shapes. In the comparing experiment of recognition of plane models, polar-radius-invariant-moments give a recognition rate of 100%, while that of contour sequence moments is 86% (p=0.3). When classifying a group of symmetric shapes, our approach arrives a rate of 91.4% whereas Hu’s moments reach a rate of 81.6%. These moments can be used for both the boundary shape recognition and the interior region shape recognition.