Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express...

When registering point clouds resolved from an underlying 2-D pixel structure, such as those resulting from structured light and flash LiDAR sensors, or stereo reconstruction, it is expected that some points in one cloud do not have corresponding points in the other cloud, and that these would occur together, such as along an edge of the depth map. In this work, a hidden Markov random field mod...

In this paper range image segmentation is cast in the framework of Bayes inference and Markov random field modeling. To facilitate the inference from distance measurement to labeling set, we introduce the set of surface function parameters as another estimation and construct a novel model accordingly. Subsequent study shows that range image segmentation can be formulated as a combinatorial opti...

Markov random field (MRF) and belief propagation have given birth to stereo vision algorithms with top performance. This article explores their biological plausibility. First, an MRF model guided by physiological and psychophysical facts was designed. Typically an MRF-based stereo vision algorithm employs a likelihood function that reflects the local similarity of two regions and a potential fu...

As the information on the Web grows, the need of recommender systems to ease user navigations becomes evident. There exist many approaches of learning for Web usage based recommender systems. In this study, we apply and compare some of the methods of usage pattern discovery, like simple k-means clustering algorithm, fuzzy relational subtractive clustering algorithm, fuzzy mean field annealing c...

A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is de-ned as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The latter relates to how data is observed and is problem domain dependent. The former depends on how var...

Image segmentation is an essential processing step for many image analysis applications. We propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a Markov random field (MRF) model under a Bayesian framework. We introduce a new pixon scheme that is more suitable for image segmentation than the "fu...

Organism is a multi-level and modularized complex system that is composed of numerous interwoven metabolic and regulatory networks. Functional associations and random evolutionary events in evolution result in elusive molecular, physiological, metabolic, and evolutionary relationships. It is a daunting challenge for biological studies to decipher the complex biological mechanisms and crack the ...

A noninvertible function of a first order Markov process, or of a nearestneighbor Markov random field, is called a hidden Markov model. Hidden Markov models are generally not Markovian. In fact, they may have complex and long range interactions, which is largely the reason for their utility. Applications include signal and image processing, speech recognition, and biological modeling. We show t...

We present a recursive algorithm to compute a collection of normalising constants which can be used in a straightforward manner to sample a realisation from a Markov random field. Further we present important consequences of this result which renders possible tasks such as maximising Markov random fields, computing marginal distributions, exact inference for certain loss functions and computing...