Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Sensors (Basel). 2023 Apr 9;23(8):3842. doi: 10.3390/s23083842.
In this paper, we propose a novel method for 2D pattern recognition by extracting features with the log-polar transform, the dual-tree complex wavelet transform (DTCWT), and the 2D fast Fourier transform (FFT2). Our new method is invariant to translation, rotation, and scaling of the input 2D pattern images in a multiresolution way, which is very important for invariant pattern recognition. We know that very low-resolution sub-bands lose important features in the pattern images, and very high-resolution sub-bands contain significant amounts of noise. Therefore, intermediate-resolution sub-bands are good for invariant pattern recognition. Experiments on one printed Chinese character dataset and one 2D aircraft dataset show that our new method is better than two existing methods for a combination of rotation angles, scaling factors, and different noise levels in the input pattern images in most testing cases.
在本文中，我们提出了一种新的二维模式识别方法，通过使用对数极坐标变换、双树复小波变换 (DTCWT) 和二维快速傅里叶变换 (FFT2) 提取特征。我们的新方法在多分辨率下对输入的二维模式图像的平移、旋转和缩放具有不变性，这对于不变模式识别非常重要。我们知道，非常低分辨率的子带会丢失模式图像中的重要特征，而非常高分辨率的子带包含大量噪声。因此，中间分辨率的子带更适合不变模式识别。在一个印刷汉字数据集和一个二维飞机数据集上的实验表明，在大多数测试情况下，对于输入模式图像的旋转角度、缩放因子和不同噪声水平的组合，我们的新方法优于两种现有方法。
