Improved inverse compositional algorithm and operators comparison
基于局部指向性的反向合成图像对齐算法

Traditional inverse compositional image alignment algorithm (ICIA) compares intensity values between a template image and an input image. However, it is easily affected by the variations of the lighting conditions, which leads to poor convergence or even divergence. Local orientation of an image tends to be less sensitive to lighting conditions than intensity values. Therefore an inverse compositional gradient (ICG) algorithm was proposed based on the local orientation. Experimental results demonstrate that this algorithm could overcome the influence of the lighting variations efficiently. In addition, the evaluation of the local orientation and image gradients is essentially equivalent. Hence several gradient operators were used to compute the local orientation, and the influences of the operators on the ICG algorithms under different lighting conditions were analyzed and compared.