School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
Information and Electronic Technology Institute, Jiamusi University, Jiamusi 154002, China.
Sensors (Basel). 2019 Sep 29;19(19):4244. doi: 10.3390/s19194244.
Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images.
随着越来越多种类的传感器的出现，红外和可见光图像匹配方法变得越来越流行，为视觉导航、精确制导、图像融合和医学图像分析等领域提供了更多的应用。在这些应用中，图像匹配用于定位、融合、图像分析等。本文提出了一种基于明显波长相位一致性（DWPC）和对数 Gabor 滤波器的红外和可见光图像匹配方法。此外，该方法经过修改，可用于具有不同物理波长的非线性图像匹配。相位一致性（PC）理论用于获取具有固有和丰富图像特征的 PC 图像，以便处理包含复杂强度变化或噪声的图像。然后，计算 PC 图像的最大和最小矩，以获得匹配图像中的角点。为了获得描述符，利用对数 Gabor 滤波器并在特定像素的邻域中提取重叠子区域。为了提高算法的准确性，将原始图像和高斯平滑图像的 PC 矩结合起来检测角点。同时，由于两幅匹配图像具有不同的物理波长，因此它们的 PC 波长相同是不合适的。因此，在实验中，改变 PC 的波长以适应不同的物理波长。为了实际应用，提出了 BiDimRegression 方法来计算红外和可见光图像中两个点集之间的相似性。在四个包含 237 对可见和红外图像的数据集上评估了所提出的方法，并将其性能与最先进的方法进行了比较：边缘定向直方图描述符（EHD）、相位一致性边缘定向直方图描述符（PCEHD）和对数 Gabor 直方图描述符（LGHD）算法。实验结果表明，所提出的方法在红外和可见光图像中的准确率比传统方法高 50%。
