Image Registration Based on a Minimized Cost Function and SURF Algorithm

Computer vision and image recognition became one of the interesting research areas. Image registration has been widely used in fields such as computer vision, MRI images, and face recognition. Image registration is a process of aligning multiple images of the same scene which are taken from a different angle or at a different time to the same coordinate system. Image registration transforms the target image to the source image based on the affine transformation such as translation, scaling, reflection, rotation, shearing etc. It is a challenging task to find enough matching points between the source and the target images. In the proposed method, we used Speeded-Up Robust Features (SURF) and Random sample consensus (RANSAC) to find the best matching points between the pair images in addition to the minimized cost function which enhances the image registration with a few matching points. We took in our concentration some of the affine transformation which is translation, rotation, and scaling. We achieved a higher accuracy in the image registration with few matching points as low as two matching points. Experimental results show the efficiency and effectiveness of the proposed method.