The current resurgence of interest in Neural Networks has opened up several basic issues. In this chapter, we explore the connections between this area and Markov Random Fields. We are speciically concerned with early vision problems which have already beneeted from a parallel and distributed computing perspective. We explore the relationships between the two elds at two diierent levels of a co...

Multi-voxel pattern recognition techniques combined with Hidden Markov models can be used to discover the mental states that people go through in performing a task. The combined method identifies both the mental states and how their durations vary with experimental conditions. We apply this method to a task where participants solve novel mathematical problems. We identify four states in the sol...

Classification, segmentation and discrimination of textures are some of the most fundamental tasks in many image understanding, pattern recognition and machine vision applications. In the past two decades or so, these topics have been actively studied by many researchers and different approaches to the problem have been proposed. Earlier approaches to texture segmentation include co-occurrence ...

Multi-voxel pattern recognition techniques combined with Hidden Markov models can be used to discover the mental states that people go through in performing a task. The combined method identifies both the mental states and how their durations vary with experimental conditions. The paper applies this method to a task where participants solve novel mathematical problems. It identifies four states...

Work on the recognition of Chinese characters is reviewed. All of this work has been reported in just the last nine years, and most of it since 1970. The topics covered are: printed Chinese Character Recognition—Half a dozen groups have tackled this problem, using methods as varied as syntactic description, projection profiles, template matching, Fourier transforms, and hierarchical processing;...

We present a system that uses nearest neighbour classification on the state level of the hidden Markov model. Common speech recognition systems nowadays use Gaussian mixtures with a very high number of densities. We propose to carry this idea to the extreme, such that each observation is a prototype of its own. This approach is well-known and widely used in other areas of pattern recognition an...

In this talk, we will review some recent developments in the area of statistical speech recognition, and which could also be potentially useful to other statistical pattern recognition applications. Among other issues, we will discuss the use of new forms of expert mixtures, for example based on the minimization of the product of error probabilities. This rule, sometimes referred to as “produt-...

Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks. We then consider the major bioinformatics application...

A key challenge for proactive handling of faults is the prediction of system failures. The main principle of the approach presented here is to identify and recognize patterns of errors that lead to failures. I propose the use of hidden Markov models (HMMs) as they have been successfully used in other pattern recognition tasks. The paper further motivates their use, explains how HMMs can be used...

Recognition of occluded objects in synthetic aperture radar (SAR) images is a signi0cant problem for automatic target recognition. Stochastic models provide some attractive features for pattern matching and recognition under partial occlusion and noise. In this paper, we present a hidden Markov modeling based approach for recognizing objects in SAR images. We identify the peculiar characteristi...