Maximum a posteriori estimation

About: Maximum a posteriori estimation is a research topic. Over the lifetime, 7486 publications have been published within this topic receiving 222291 citations. The topic is also known as: Maximum a posteriori, MAP & maximum a posteriori probability.

Avoiding the "streetlight effect": tracking by exploring likelihood modes

Abstract: Classic methods for Bayesian inference effectively constrain search to lie within regions of significant probability of the temporal prior. This is efficient with an accurate dynamics model, but otherwise is prone to ignore significant peaks in the true posterior. A more accurate posterior estimate can be obtained by explicitly finding modes of the likelihood function and combining them with a weak temporal prior. In our approach, modes are found using efficient example-based matching followed by local refinement to find peaks and estimate peak bandwidth. By reweighting these peaks according to the temporal prior we obtain an estimate of the full posterior model. We show comparative results on real and synthetic images in a high degree of freedom articulated tracking task.

A global perspective on MAP inference for low-level vision

Abstract: In recent years the Markov Random Field (MRF) has become the de facto probabilistic model for low-level vision applications However, in a maximum a posteriori (MAP) framework, MRFs inherently encourage delta function marginal statistics By contrast, many low-level vision problems have heavy tailed marginal statistics, making the MRF model unsuitable In this paper we introduce a more general Marginal Probability Field (MPF), of which the MRF is a special, linear case, and show that convex energy MPFs can be used to encourage arbitrary marginal statistics We introduce a flexible, extensible framework for effectively optimizing the resulting NP-hard MAP problem, based around dual-decomposition and a modified min-cost flow algorithm, and which achieves global optimality in some instances We use a range of applications, including image denoising and texture synthesis, to demonstrate the benefits of this class of MPF over MRFs

A Fast MAP Algorithm for High-Resolution Image Reconstruction with Multisensors

Abstract: In many applications, it is required to reconstruct a high-resolution image from multiple, undersampled and shifted noisy images. Using the regularization techniques such as the classical Tikhonov regularization and maximum a posteriori (MAP) procedure, a high-resolution image reconstruction algorithm is developed. Because of the blurring process, the boundary values of the low-resolution image are not completely determined by the original image inside the scene. This paper addresses how to use (i) the Neumann boundary condition on the image, i.e., we assume that the scene immediately outside is a reflection of the original scene at the boundary, and (ii) the preconditioned conjugate gradient method with cosine transform preconditioners to solve linear systems arising from the high-resolution image reconstruction with multisensors. The usefulness of the algorithm is demonstrated through simulated examples.

Adaptive Segmentation of Textured Images by Using the Coupled Markov Random Field Model

Abstract: Although simple and efficient, traditional feature-based texture segmentation methods usually suffer from the intrinsical less inaccuracy, which is mainly caused by the oversimplified assumption that each textured subimage used to estimate a feature is homogeneous. To solve this problem, an adaptive segmentation algorithm based on the coupled Markov random field (CMRF) model is proposed in this paper. The CMRF model has two mutually dependent components: one models the observed image to estimate features, and the other models the labeling to achieve segmentation. When calculating the feature of each pixel, the homogeneity of the subimage is ensured by using only the pixels currently labeled as the same pattern. With the acquired features, the labeling is obtained through solving a maximum a posteriori problem. In our adaptive approach, the feature set and the labeling are mutually dependent on each other, and therefore are alternately optimized by using a simulated annealing scheme. With the gradual improvement of features' accuracy, the labeling is able to locate the exact boundary of each texture pattern adaptively. The proposed algorithm is compared with a simple MRF model based method in segmentation of Brodatz texture mosaics and real scene images. The satisfying experimental results demonstrate that the proposed approach can differentiate textured images more accurately