Under the most general conditions of an anisotropic Markov random Ž eld, we model the twodimensional spatial distribution of microchips on a silicon wafer. The proposed model improves on its predecessors as it stipulates the spatial correlation of different strengths in all eight directions. Its canonical parameters represent the intensity of failures, main effects, and interactions of neighbor...

Event data are one of the most widely used indicators in quantitative international relations research. To date, most of the models using event data have constructed numerical indicators based on the characteristics of the events measured in isolation and then aggregated. An alternative approach is to use quantitative pattern recognition techniques to compare an existing sequence of behaviors t...

Parallel pattern recognition requires great computational resources. It is desirable from an engineering point of view to achieve good performance with limited resources. For this purpose, we develop a serial model for visual pattern recognition based on the primate selective attention mechanism. The idea in selective attention is that not all parts of an image give us information. If we can at...

Motion estimation in image sequences is undoubtedly one of the most studied research fields, given that motion estimation is a basic tool for disparate applications, ranging from video coding to pattern recognition. In this paper a new methodology which, by minimizing a specific potential function, directly determines for each image pixel the motion parameters of the object the pixel belongs to...

The embedded hidden Markov model (HMM) is a statistical model that can be used in many pattern recognition and computer vision applications. This model inherits the partial size invariance of the standard HMM, and, due to its pseudo two-dimensional structure, is able to model twodimensional data such as images, better than the standard HMM. In this paper we describe the maximum likelihood train...

A new deformed trademark retrieval method based on two-dimensional pseudo-hidden Markov model (2D PHMM) is proposed in this paper. Most trademark retrieval systems focus on color features, shape silhouettes, or the combination of color and shape. However, these approaches adopted individual silhouettes as shape features, leading to the following two crucial problems. First, most trademarks have...

The huge popularity of Hidden Markov models in pattern recognition is due to the ability to ”learn” model parameters from an observation sequence through Baum-Welch and other re-estimation procedures. In the case of HMM parameter estimation from an ensemble of observation sequences, rather than a single sequence, we require techniques for finding the parameters which maximize the likelihood of ...

In this paper we present a stochastic framework for the recognition of binary random patterns which advantageously combine hmms and Markov random elds (mrfs). The hmm component of the model analyzes the image along one direction, in a speci c state observation probability given by the product of causal mrf-like pixel conditional probabilities. Aspects concerning de nition, training and recognit...

EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently developed to address a related problem of recogniser robustness to uncontrollable signal variation which also occurs in automatic speech recognition ...

Face recognition has become an important topic within the field of pattern recognition and computer vision. In this field a number of different approaches to feature extraction, modeling and classification techniques have been tested. However, many questions concerning the optimal modeling techniques for high performance face recognition are still open. The face recognition system developed by ...