Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.

Hybrid numerical methods for exponential models of growth

A series of novel numerical methods for the exponential models of growth are proposed. Based on these methods, hybrid predictor-corrector methods are constructed. The hybrid numerical methods can increase the accuracy and the computing speed obviously, as well as enlarge the stability domain greatly. (c) 2005 Published by Elsevier Inc.

Qualitative Circuit Models in Failure Analysis Reasoning

Lee M.H., Qualitative Circuit Models in Failure Analysis Reasoning, AI Journal. vol 111, pp239-276.1999.

Methods to accelerate the learning of bayesian network structures

R. Daly and Q. Shen. Methods to accelerate the learning of bayesian network structures. Proceedings of the Proceedings of the 2007 UK Workshop on Computational Intelligence.

Causality enabled compositional modelling of Bayesian networks

J. Keppens and Q. Shen. Causality enabled compositional modelling of Bayesian networks. Proceedings of the 18th International Workshop on Qualitative Reasoning, pages 33-40, 2004.

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.

Self perceived health status of schizophrenic patients in Spain: an analysis of geog raphical differences using bayesian approach

Objectives. This paper explores the use of regression models for estimating health status of schizophrenic patients, from a Bayesian perspective. Our aims are: 1- To obtain a set of values of health states of the EQ-5D based on self-assessed health from a sample of schizophrenic patients. 2- To analyse the differences in the health status and in patients’ perceptions of their health status between four mental-health districts in Spain. Methods. We develop two linear models with dummy variables. The first model seeks to obtain an index of the health status of the patients using a VAS as a dependent variable and the different dimensions of EQ-5D as regressors. The second model allows to analyse the differences between the self-assessed health status in the different geographic areas and also the differences between the patients’ self-assessed health states, irrespective of their actual health state, in the different geographic areas. The analysis is done using Bayesian approach with Gibbs sampling (computer program WinBUGS 1.4). Data concerning self-assessed EQ-5D with VAS from four geographic areas of schizophrenic patients were obtained for the purposes of this analysis. Results. We obtained the health status index for this sample and analysed the differences for this index between the four geographic areas. Our study reveals variables that explain the differences in patients’ health status and differences in their health states assessment. We consider four possible scenarios.

Dry mixing of spice powders - investigation of effect of powder properties on mixture quality of binary powder mixtures

Dry mixing of binary food powders was conducted in a 2L lab-scale paddle mixer. Different types of food powders such as paprika, oregano, black pepper, onion powder and salt were used for the studies. A novel method based on a digital colour imaging system (DCI) was developed to measure the mixture quality (MQ) of binary food powder mixtures. The salt conductivity method was also used as an alternative method to measure the MQ. In the first part of the study the DCI method was developed and it showed potential for assessing MQ of binary powder mixes provided there was huge colour difference between the powders. In the second and third part of the study the effect of composition, water content, particle size and bulk density on MQ was studied. Flowability of powders at various moisture contents was also investigated. The mixing behaviour was assessed using coefficient of variation. Results showed that water content and composition influence the mixing behavior of powders. Good mixing was observed up to size ratios of 4.45 and at higher ratios MQ disimproved. The bulk density had a larger influence on the MQ. In the final study the MQ evaluation of binary and ternary powder mixtures was compared by using two methods – salt conductivity method and DCI method. Two binary food and two quaternary food powder mixtures with different coloured ingredients were studied. Overall results showed that DCI method has a potential for use by industries and it can analyse powder mixtures with components that have differences in colour and that are not segregating in nature.

Bayesian Analysis of Latent Threshold Dynamic Models

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online. © 2013 Copyright Taylor and Francis Group, LLC.

Bayesian Model Averaging in the M-Open Framework

This chapter presents a model averaging approach in the M-open setting using sample re-use methods to approximate the predictive distribution of future observations. It first reviews the standard M-closed Bayesian Model Averaging approach and decision-theoretic methods for producing inferences and decisions. It then reviews model selection from the M-complete and M-open perspectives, before formulating a Bayesian solution to model averaging in the M-open perspective. It constructs optimal weights for MOMA:M-open Model Averaging using a decision-theoretic framework, where models are treated as part of the ‘action space’ rather than unknown states of nature. Using ‘incompatible’ retrospective and prospective models for data from a case-control study, the chapter demonstrates that MOMA gives better predictive accuracy than the proxy models. It concludes with open questions and future directions.

SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication

We propose a novel unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation is to represent the pattern of links between records as a {\em bipartite} graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible new representation of the linkage structure naturally allows us to estimate the attributes of the unique observable people in the population, calculate $k$-way posterior probabilities of matches across records, and propagate the uncertainty of record linkage into later analyses. Our linkage structure lends itself to an efficient, linear-time, hybrid Markov chain Monte Carlo algorithm, which overcomes many obstacles encountered by previously proposed methods of record linkage, despite the high dimensional parameter space. We assess our results on real and simulated data.

Enhancing the usability of numerical models with case-based reasoning

Numerical models are important tools used in engineering fields to predict the behaviour and the impact of physical elements. There may be advantages to be gained by combining Case-Based Reasoning (CBR) techniques with numerical models. This paper considers how CBR can be used as a flexible query engine to improve the usability of numerical models. Particularly they can help to solve inverse and mixed problems, and to solve constraint problems. We discuss this idea with reference to the illustrative example of a pneumatic conveyor problem. The paper describes example problems faced by design engineers in this context and the issues that need to be considered in this approach. Solution of these problems require methods to handle constraints in both the retrieval phase and the adaptation phase of a typical CBR cycle. We show approaches to the solution of these problesm via a CBR tool.