摘要
针对图像旋转和尺度变换所引起的图像重采样和重量化的问题,提出了Zernike矩的改进方法.该方法先对图像中的目标区域进行形状归一化,再对Zernike矩进行归一化.实验数据表明,改进后的Zernike矩不仅具有旋转不变性,而且还具有改进前不具备的比例不变性.基于最小距离分类器对待识别目标进行分类的结果表明,改进的Zernike矩具有较高的识别率,其不足是不适用目标图像背景复杂的情况.
Aiming at the resampling and requantization error caused by image rotation and scaling transformation,the improved Zernike moment method was proposed,that is,first the target area in the image was normalized shapely,and then the Zernike moments were normalized. The experimental data showed that the improved Zernike moments not only had rotation invariance,but also had the scale invariance which didn＇t have before improved. The classification results of the target to be identified based on the minimum distance classifier showed that the improved Zernike moments had a higher recognition rate. Its shortcomings are not applicable to the complex situations of target image background.
出处
《郑州轻工业学院学报（自然科学版）》
CAS
2013年第5期66-69,共4页
Journal of Zhengzhou University of Light Industry:Natural Science
关键词
ZERNIKE矩
图像识别
旋转不变性
尺度变换
Zernike moment
image recognition
rotation invariance
Zernike polynomial
scaling invariance
