# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 1 Introduction
Feature description methods for infrared and visible image match can be divided into two categories: gradient-based methods and edge-based methods [20]. After feature points are detected, feature descriptions need to be established for matching. To handle the difference of infrared and visible images, gradient and edge information are used to build feature descriptions. Gradient-based methods establish feature descriptions by dealing with the gradient orientation reversal and the gradient magnitude discrepancy caused by nonlinear intensity variations of infrared and visible images [20]. To cope with the gradient orientation reversal, the symmetric-SIFT [21] limits the gradient orientation in the interval [0,\(\pi\)) during the construction of descriptions. MM-SURF (Multimodal Speeded Up Robust Features) [22] accumulates Haar responses according to their signs. To cope with the gradient magnitude discrepancy, PIIFD (Partial Intensity Invariant Feature Descriptor) [23] and NG-SIFT (Normalized Gradient SIFT) [24] normalizes gradients to the interval [0,1]. PIIFD establishes symmetric feature descriptions further to overcome the gradient orientation reversal. MN-SIFT (Modified Normalized Gradient SIFT) [25] binarizes gradients to {0,1} to tackle the edge strength variation caused by the gradient magnitude discrepancy of multi-modality images. MSCB (Morphological SIFT and Centric Brief) [26] utilizes the morphological gradient method to obtain consistent gradient images of infrared and visible images and establishes descriptions by brief methods [14]. However, gradient-based methods cannot handle feature description variations caused by the discrepancies of image local details because image gradients are essentially dependent on pixel intensities. Edge-based methods establish feature descriptions by the image edge orientation according to the consistency of edge structures of infrared and visible images [20]. To compute the edge orientation,multi-oriented Sobel spatial filters are introduced in EOH (Edge Oriented Histogram) [27], PCEHD (Phase Congruency Edge Histogram Descriptor) [28], HoDMs (Histogram of Directional Maps) [29], and HOSM (Histogram of Oriented Structure Maps) [30]. EOH calculates edge orientation responses by multi-oriented Sobel spatial filters and use the orientation corresponding to the maximum response as the edge orientation. The edge orientation histogram is established as feature descriptions. PCEHD extracts edges and feature points of infrared and visible images by using the phase consistency method and calculates the edge orientation histogram for feature points. Based on EOH, HoDMs uses edge strength responses to describe image textures and HOSM enhances image edges by the guided filter. Multi-scale and multi-oriented Log-Gabor filters are introduced in LGHD (Log-Gabor Histogram Descriptor) [31], MFD (Multispectral Feature Descriptor) [32], and RIDLG (Rotation Invariant Feature Descriptor based on Log-Gabor Filters) [33] to compute the edge orientation. These methods establish the edge orientation histogram by the orientation of the maximum Log-Gabor response. On this basis, RIFT (Radiation Invariant Feature Transform) [34] and MSPC (Maximally Stable Phase Congruency) [35] extract image features by phase congruency. To increase the number of feature points in multi-modal images, RIFT adds edge points as feature points further. Log-Gabor filters can obtain richer feature descriptions than Sobel filters. However, Log-Gabor filter suffers from a high computation burden. Because edge-based methods leverage image structures rather than pixel information, these methods are more suitable for infrared and visible image match than gradient-based methods [31]. However, infrared and visible image matching methods still face the following problems. Gradient-based methods and edge-based methods need to extract common feature points to avoid non-common feature points participating in image matching.