# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 2 Proposed Methods
### Consistent Edge Extraction  
Consistent edges are usually the long edges of global structures, which are similar in infrared and visible images [36]. Due to different imaging mechanisms, the consistent edges in infrared and visible images are mainly the long edges that are formed by global structures of images, while the inconsistent edges are mainly the short edges that are formed by local details like textures [12]. Figure 1 illustrates a pair of visible and infrared images and their corresponding edge images. As shown in Figure 1, the long edges of global structures like the building, the crown, and the tent maintain consistent in infrared and visible images. Since each side of these edges has different reflection and radiation characteristics, these edges can usually be captured in infrared and visible images. The short edges of local details like road textures and leaves are different, as shown in Figure 1. These short edges are a mutual loss or have different position responses in infrared and visible images.  
Consistent edges of infrared and visible images are extracted by selecting the long edges of images. First, due to the intense difference between infrared and visible images, a white balance method is utilized to handle infrared and visible images so that they all occupy the maximal possible range [0,255][37]. Second, histogram equalization is used to enhance the edges of infrared and visible images [38]. Third, the Canny method [39] is used to extract image edges. The key idea of Canny is to use two different thresholds in order to determine which point should belong to an edge: a low threshold T1 and a high threshold T2. Points with a gradient greater than T2 are edge points. Points with a gradient greater than T1 and less than T2 are edge points if there is a continuous path linking those points to the points with a gradient greater than T2. In this paper, the modified Canny method [40] is adopted. The high and low thresholds of the Canny method are set automatically: The detection procedure of the modified Canny method incorporates the same steps as the original Canny method except that T2 and T1 are set locally. Thresholds (T2, T1) are determined within a moving window centered on the current pixel. Following the setups used in Reference [40], the window size is chosen to be \(20\times 20\). T2 is set to the gradient magnitude that is ranked as the top 30% in the window and T1 is set to 40% of T2 [40]. The set of the extracted edge is denoted as \(E=\{e_{1}\),\(e_{2}\),... \(e_{\text{M}}\}\) where M is the number of edges. Lastly, the edge length is calculated by the contour search algorithm [38]. As a result, an edge is denoted as \(e_{i}=\{p_{1}\),\(p_{2}\),... \(p_{\text{{i}}\text{{i}}}\}\) where \(p\) is the edge point and \(\text{{i}}\text{{i}}\) is the edge length. Consistent edges are generated by selecting edges that are longer than \(\alpha\). The set of consistent edges is denoted as \(l\)\(=\{e_{i}\}\)\(|e_{i}|\)\(>\)\(\alpha\), \(e_{i}\)\(\in\)E\(\}\).  
Figure 1: Edge images extracted from infrared and visible images. (**a**) Infrared image and the corresponding edge image. (**b**) Visible Image and the corresponding edge image.