Yin Qingbo, Shen Liran, Kim Jong-Nam, Jeong Yong-Jae
College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, PR China.
Opt Express. 2009 Sep 14;17(19):16581-9. doi: 10.1364/OE.17.016581.
A novel scale and shift invariant pattern recognition method is proposed to improve the discrimination capability and noise robustness by combining the bidimensional empirical mode decomposition with the Mellin radial harmonic decomposition. The flatness of its peak intensity response versus scale change is improved. This property is important, since we can detect a large range of scaled patterns (from 0.2 to 1) using a global threshold. Within this range, the correlation peak intensity is relatively uniform with a variance below 20%. This proposed filter has been tested experimentally to confirm the result from numerical simulation for cases both with and without input white noise.
提出了一种新颖的尺度和移位不变模式识别方法，通过将二维经验模式分解与梅林径向谐波分解相结合来提高识别能力和抗噪声鲁棒性。其峰值强度响应相对于尺度变化的平坦度得到了改善。这一特性很重要，因为我们可以使用全局阈值检测大范围的尺度模式（从0.2到1）。在此范围内，相关峰值强度相对均匀，方差低于20%。已对该提出的滤波器进行了实验测试，以证实有无输入白噪声情况下数值模拟的结果。
