Markov random field

About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.

MAP-MRF approach for binarization of degraded document image

Abstract: We propose an algorithm for the binarization of document images degraded by uneven light distribution, based on the Markov Random Field modeling with Maximum A Posteriori probability (MAP-MRF) estimation. While the conventional algorithms use the decision based on the thresholding, the proposed algorithm makes a soft decision based on the probabilistic model. To work with the MAP-MRF framework we formulate an energy function by a likelihood model and a generalized Potts prior model. Then we construct a graph for the energy, and obtain the optimized result by using the well-known graph cut algorithm. Experimental results show that our approach is more robust to various types of images than the previous hard decision approaches.

3-D Reconstruction from Sparse Views using Monocular Vision

Abstract: We consider the task of creating a 3-d model of a large novel environment, given only a small number of images of the scene. This is a difficult problem, because if the images are taken from very different viewpoints or if they contain similar-looking structures, then most geometric reconstruction methods will have great difficulty finding good correspondences. Further, the reconstructions given by most algorithms include only points in 3-d that were observed in two or more images; a point observed only in a single image would not be reconstructed. In this paper, we show how monocular image cues can be combined with triangulation cues to build a photo-realistic model of a scene given only a few images-even ones taken from very different viewpoints or with little overlap. Our approach begins by over-segmenting each image into small patches (superpixels). It then simultaneously tries to infer the 3-d position and orientation of every superpixel in every image. This is done using a Markov random field (MRF) which simultaneously reasons about monocular cues and about the relations between multiple image patches, both within the same image and across different images (triangulation cues). MAP inference in our model is efficiently approximated using a series of linear programs, and our algorithm scales well to a large number of images.

An efficient two-pass MAP-MRF algorithm for motion estimation based on mean field theory

Abstract: This paper presents a two-pass algorithm for estimating motion vectors from image sequences. In the proposed algorithm, the motion estimation is formulated as a problem of obtaining the maximum a posteriori in the Markov random field (MAP-MRF). An optimization method based on the mean field theory (MFT) is opted to conduct the MAP search. The estimation of motion vectors is modeled by only two MRFs, namely, the motion vector field and unpredictable field. Instead of utilizing the line field, a truncation function is introduced to handle the discontinuity between the motion vectors on neighboring sites. In this algorithm, a "double threshold" preprocessing pass is first employed to partition the sites into three regions, whereby the ensuing MPT-based pass for each MRF is conducted on one or two of the three regions. With this algorithm, no significant difference exists between the block-based and pixel-based MAP searches any more. Consequently, a good compromise between precision and efficiency can be struck with ease. To render our algorithm more resilient against noise, the mean absolute difference instead of mean square error is selected as the measure of difference, which is more reliable according to the knowledge of robust statistics. This is supported by our experimental results from both synthetic and real-world image sequences. The proposed two-pass algorithm is much faster than any other MAP-MRF motion estimation method reported in the literature so far.

Fusion of image classifications using Bayesian techniques with Markov random fields

Abstract: This study investigates whether combining several different image classifications together with an a priori image model of the expected spatial distribution of the classes can produce a better classification. A maximum likelihood classifier and the cascade-correlation neural network architecture are used to generate various classification maps for satellite image data by varying the input features and network parameter settings. A likelihood for each pixel's class label is derived from the source classifications and combined with a Markov random field spatial image model to produce the final image classification. The method is applied to a ground cover type study based on Landsat Thematic Mapper (TM) imagery. It was found that a carefully selected combination could significantly improve individual classification results.