# Cross-Modal Image Registration via Rasterized Parameter Prediction for Object Tracking
## 2 Related Work
### Feature Extraction-Based Image Registration  
Conventional image registration methods extract sparse features from a raw image, then implement image registration at the feature layer. The coordinate mapping parameters between image pairs are obtained by solving the relationship between feature pairs, such as the Scale-Invariant Feature Transform (SIFT) feature point detection algorithm ([PERSON] et al., 2016; [PERSON] et al., 2017) and Random Sample Consensus (RANSAC) outlier removal algorithm ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017). The SIFT algorithm ([PERSON] et al., 2016) extracts feature points by constructing an image pyramid, computing the dominant orientation, and computing descriptor vectors. The RANSAC algorithm ([PERSON] et al., 2016) uses hypothesis testing to obtain the minimal subset without outliers by resampling, minimizing the proportion of incorrectly matched samples in feature pairs and improving the estimation accuracy of the pixel coordinate mapping relationship.  
Several researches ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017) have proposed combining the SIFT class feature extraction algorithm ([PERSON] et al., 2016) and RANSAC abnormal value deletion algorithm ([PERSON] et al., 2016) for image registration. The SIFT algorithm constructs image pyramids to compute the main direction and description vectors for feature points extraction, which is scale- and rotation-invariant. The RANSAC algorithm uses the hypothesis testing method to resample a potential smallest subset without abnormal values for reduction of incorrectly matching samples and improved pixel coordinate mapping estimating accuracy. As the transformation between multimodal images is nonlinear and grayscale, researchers have proposed enhancing the feature operators description capability ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017). For example, [PERSON] et al. (2017) employed the Speeded-Up Robust Features (SURF) algorithm for local feature extraction, then used the shape represented by boundaries as global features to describe the appearance of differences. [PERSON] et al. (2017) used an edge detection operator to extract the contour maps of infrared-visible image pairs as matching features, which were then used to process the spatial gradient differences between multimodal image pixels.  
In addition, other researchers have proposed enhancing the processing of feature matching and image transformation for infrared-visible light image registration ([PERSON] et al., 2017; [PERSON] et al., 2017). For example, [PERSON] et al. (2017) constructed an enhanced model by combining affine and polynomial transformation. Their approach uses the edge point features of an infrared-visible light image pair for transformation parameter estimation, leading to the description of non-rigid and global deformation modes. [PERSON] et al. (2017) extracted similar structural features through multi-directional phase coherence and saliency ranking. Then, the feature points were tracked using the kernel correlation filtering method and matched by incorporating the RANSAC algorithm.