A Globally Optimal Data-Driven Approach for Image Distortion Estimation

Image alignment in the presence of non-rigid distortions is a challenging task. Typically, this involves estimating the parameters of a dense deformation field that warps a distorted image back to its undistorted template. Generative approaches based on parameter optimization such as Lucas-Kanade can get trapped within local minima. On the other hand, discriminative approaches like Nearest-Neighbor require a large number of training samples that grows exponentially with the desired accuracy. In this work, we develop a novel data-driven iterative algorithm that combines the best of both generative and discriminative approaches. For this, we introduce the notion of a “pull-back” operation that enables us to predict the parameters of the test image using training samples that are not in its neighborhood (not

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Yuandong Tian, Srinivasa Narasimhan
• Keywords: Computer Vision, CVPR 2010, Discriminative Approaches, Parameter, Training Samples