Image Registration Algorithm Based On The Feature Points And Application

Image Registration is a fundamental task in image Processing used to (mainly in geometrical) match two or more images obtained at different time, from different sensors or from different viewpoints. Image Registration is the foundation of many image processing and applications such as image fusion, image stitching, change detection. And the effect of registration works on the following processing steps directly. In this paper, two image registration methods are presented: the registration based on the Shape Context Descriptor (SCD) and the registration based on the Gray-value Differential Invariants (GDI).A novel shape context descriptor is presented. For each feature, the shape context descriptor divides 360°central angle (the centre is the feature point) equally into N parts, then according the angle values between the feature point and the edge points, get a 1×N vector. This new technique doesn't depend on global information. It is robust to noise, scale and so on. To overcome the drawback that the registration algorithm is only suitable to small rotation angle, the orientation difference between two images is calculated using a local gradient orientation histogram.Another registration algorithms based on the Gray-value Differential Invariants is introduced. The differential invariants are robust to noise, translation and rotation. Firstly the differential invariants are used to form local invariant feature, then the matching degree is gained via Euclidean distance. The experiment results show this method is robust to noise and registration error.Finally, we apply the two proposed algorithms in image stitching and image fusion. In image stitching, the results from experiments show the validity, good stability, and high precision of the two algorithms. In image fusion, transparency fusion technique is applied in the fusion of the visible and infrared light images.