Model-based object recognition from a complex binary imagery using genetic algorithm

This paper describes a technique for model-based object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line segments. The problem is then formulated as that of finding the best describing match between a hypothesized object and the image. A special form of template matching is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with standard techniques indicate the scope for further research in this direction

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and for high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but with performance analysis and optimization impeded by speed of computation. We have developed new feature extraction techniques on notional use of physical paradigms, with parametrization aimed to be more familiar to a scientifically trained user, aiming to make best use of computational resource. This paper is the first unified description of these new approaches, outlining the basis and results that can be achieved. We describe how gravitational force can be used for low-level analysis, while analogies of water flow and heat can be deployed to achieve high-level smooth shape detection, by determining features and shapes in a selection of images, comparing results with those by stock approaches from the literature. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics

The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes. In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery. In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimer’s disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs. A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners