# A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging
## 1 Introduction  
Integration of infrared messages and polarization messages will significantly increase the amount of information in the image and enhance the ability of target detection and recognition [1; 2; 3; 4; 5; 6]. However, the coupled infrared camera and polarized camera imaging in different perspectives, fields of view, and sizes [7] introduce misalignment in the resulting images, which is a common problem in multi-sensor detection methods. Before further analyses, the images need to be aligned properly, which is difficult to implement in hardware. Therefore, it is certainly more practical and economical to seek an available registration algorithm in software [8]. The image registration algorithm aligns two or more images of the same scene with matching contents. The existing registration methods are commonly classified into intensity-based and feature-based methods. Intensity-based methods include metrics based such as phase correlation [9], Mutual Information [10; 11], and optimization based methods such as BSO (brain storm optimization) [12], GA (genetic algorithms) [13], PSO (particle swarm optimization) [14], Powell [15], etc. Intensity-based image registration methods require consistent gray-level information of images, therefore working well in monomodal images taken by single-modal cameras. By contrast, intensity-based methods have a limited performance in registering multi-modal images from heterogenous imaging systems, and usually suffer from image distortions, illumination changes and heavy computational complexities. It can be inferred that such algorithms are difficult to be applied in infrared and visibly polarized images with differences in grayscale distribution and content structure. Feature-based methods are the most commonly used methods in practical applications [16], their representative algorithms are SURF (speeded-up robust feature) [17], Harris [18], FAST [19], BRISK (binary robust invariant scalable keypoints) [20], MSER (maximally stable extremal regions) [21] and the newly proposed MIND [22] (modality independent neighborhood descriptor), etc. This kind of algorithm is conducted based on descriptors related to structures such as points, corners and edges. As for heterogeneous image registration, the differences in sensitive information between heterogeneous sensors makes it extremely difficult to accurately catch the common features in both images. In the polarization and infrared image registration, infrared images are characterized by smooth intensity regions with different gray levels, while polarization parameter images have less gray level information, mainly contour and texture information. It is difficult to register the above two images with only one feature-based algorithm or intensity-based algorithm. To overcome the lack of common features, we addressed the topological structure in the heterogeneous images, and implemented image segmentation and morphology processes in this work. Our Segment-Skeletonize-Registration (SSR) segments the input images into regions with distinct geometric features, skeletonizes the regions into skeleton branches, and finally, takes the endpoints of the skeleton as registration control points. In addition, the polarization parameter images were segmented into regions according to the texture information, and the gray level was assigned according to different regions. Thereafter, the polarization parameter image features can be transformed into features common with the infrared image. Instead of registering the images by gray level regions directly, the SSR algorithm takes advantage of the topological characteristics of the target to process the segmented region morphologically, and to obtain more robust feature points.