In this report we introduce a novel approach for determining correspondence in a sequence of images. We formulate a probabilistic framework that relates a feature's appearance and its position under relaxed statistical assumptions. We employ a Monte-Carlo approximation for the joint probability density of the feature position and its appearance that uses a flexible noise and motion model to generate random samples. The joint probability density is modeled by a Gaussian Mixture. The feature's position given its appearance is then determined by maximizing its posterior. We evaluate our method using real and synthetic sequences and compare its performance with leading or popular algorithms from the literature. The noise robustness of our algorithm is superior under a wide variety of conditions. The method can be applied in the context of optical flow, tracking and any application that needs feature point matching.