A Stochastic Modeling Approach to Region-and Edge-Based Image Segmentation

The purpose of image segmentation is to isolate objects in a scene from the background. This is a very important step in any computer vision system since various tasks, such as shape analysis and object recognition, require accurate image segmentation. Image segmentation can also produce tremendous data reduction. Edge-based and region-based segmentation have been examined and two new algorithms based on recent results in random field theory have been developed. The edge-based segmentation algorithm uses the pixel gray level intensity information to allocate object boundaries in two stages: edge enhancement, followed by edge linking. Edge enhancement is accomplished by maximum energy filters used in one-dimensional bandlimited signal analysis. The issue of optimum filter spatial support is analyzed for ideal edge models. Edge linking is performed by quantitative sequential search using the Stack algorithm. Two probabilistic search metrics are introduced and their optimality is proven and demonstrated on test as well as real scenes. Compared to other methods, this algorithm is shown to produce more accurate allocation of object boundaries. Region-based segmentation was modeled as a MAP estimation problem in which the actual (unknown) objects were estimated from the observed (known) image by a recursive classification algorithms. The observed image was modeled by an Autoregressive (AR) model whose parameters were estimated locally, and a Gibbs-Markov random field (GMRF) model was used to model the unknown scene. A computational study was conducted on images having various types of texture images. The issues of parameter estimation, neighborhood selection, and model orders were examined. It is concluded that the MAP approach for region segmentation generally works well on images having a large content of microtextures which can be properly modeled by both AR and GMRF models. On these texture images, second order AR and GMRF models were shown to be adequate

Workshop on Squeezed States and Uncertainty Relations

The proceedings from the workshop are presented, and the focus was on the application of squeezed states. There are many who say that the potential for industrial applications is enormous, as the history of the conventional laser suggests. All those who worked so hard to produce squeezed states of light are continuing their efforts to construct more efficient squeezed-state lasers. Quite naturally, they are looking for new experiments using these lasers. The physical basis of squeezed states is the uncertainty relation in Fock space, which is also the basis for the creation and annihilation of particles in quantum field theory. Indeed, squeezed states provide a unique opportunity for field theoreticians to develop a measurement theory for quantum field theory

Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

Boundaries and Topological Algorithms

This thesis develops a model for the topological structure of situations. In this model, the topological structure of space is altered by the presence or absence of boundaries, such as those at the edges of objects. This allows the intuitive meaning of topological concepts such as region connectivity, function continuity, and preservation of topological structure to be modeled using the standard mathematical definitions. The thesis shows that these concepts are important in a wide range of artificial intelligence problems, including low-level vision, high-level vision, natural language semantics, and high-level reasoning

Automatic Multilevel Feature Abstraction in Adaptable Machine Vision Systems

Vision is a complex task which can be accomplished with apparent ease by biological systems, but for which the design of artificial systems is difficult. Although machine vision systems can be successfully designed for a specific task, under certain conditions, they are likely to fail if circumstances change. This was the motivation for the research into ways in which systems can be self-designing and adaptable to new visual tasks. The research was conducted in three vital areas of concern for machine vision systems. The first area is finding a suitable architecture for forming an appropriate representation for the current task. The research investigated the application of Hypernetworks theory to building a multilevel, generally-applicable representation, through repeated application of a fundamental 'self-similarity' principle, that parts of objects assembled under a particular relation at one level, form whole objects at the next. Results show that this is potentially a powerful approach for autonomously generating an adaptable system-architecture suitable for multiple visual tasks. The second area is the autonomous extraction of suitable low-level features, which the research investigated through random generation of minimally-constrained pixel-configurations and algorithmic generation of homogeneous and heterogeneous polygons. The results suggest that, despite the simplicity of the features making them vulnerable to image transformations, these are promising approaches worth developing further. The third area is automatic feature selection. The research explored management of 'dimensionality' and of 'combinatorial explosion', as well as how to locate relevant features at multiple representation levels, in the context of 'emergence' of structure. Results indicate that this approach can find useful 'intermediate-level' constructs through analysis of the connectivity of the simplices representing objects at higher levels. The research concludes that the proposed novel approaches to tackling the above issues, in particular the application of hypernetworks to the formation of multilevel representations and the resulting emergence of higher-level structure, is fruitful

Heavy Flavour Physics Theory and Experimental Results in Heavy Quark Physics

This book provides a thorough introduction to the phenomenology of heavy flavour physics, those working on the B-factories, LHCb, BTeV, HERA and the Tevatron. It explains how heavy quark theory could be implemented on the lattice, and discusses the status of CP-violation in the neutral kaon system

Microgravity Science and Applications: Program Tasks and Bibliography for Fiscal Year 1996

NASA's Microgravity Science and Applications Division (MSAD) sponsors a program that expands the use of space as a laboratory for the study of important physical, chemical, and biochemical processes. The primary objective of the program is to broaden the value and capabilities of human presence in space by exploiting the unique characteristics of the space environment for research. However, since flight opportunities are rare and flight research development is expensive, a vigorous ground-based research program, from which only the best experiments evolve, is critical to the continuing strength of the program. The microgravity environment affords unique characteristics that allow the investigation of phenomena and processes that are difficult or impossible to study an Earth. The ability to control gravitational effects such as buoyancy driven convection, sedimentation, and hydrostatic pressures make it possible to isolate phenomena and make measurements that have significantly greater accuracy than can be achieved in normal gravity. Space flight gives scientists the opportunity to study the fundamental states of physical matter-solids, liquids and gasses-and the forces that affect those states. Because the orbital environment allows the treatment of gravity as a variable, research in microgravity leads to a greater fundamental understanding of the influence of gravity on the world around us. With appropriate emphasis, the results of space experiments lead to both knowledge and technological advances that have direct applications on Earth. Microgravity research also provides the practical knowledge essential to the development of future space systems. The Office of Life and Microgravity Sciences and Applications (OLMSA) is responsible for planning and executing research stimulated by the Agency's broad scientific goals. OLMSA's Microgravity Science and Applications Division (MSAD) is responsible for guiding and focusing a comprehensive program, and currently manages its research and development tasks through five major scientific areas: biotechnology, combustion science, fluid physics, fundamental physics, and materials science. FY 1996 was an important year for MSAD. NASA continued to build a solid research community for the coming space station era. During FY 1996, the NASA Microgravity Research Program continued investigations selected from the 1994 combustion science, fluid physics, and materials science NRAS. MSAD also released a NASA Research Announcement in microgravity biotechnology, with more than 130 proposals received in response. Selection of research for funding is expected in early 1997. The principal investigators chosen from these NRAs will form the core of the MSAD research program at the beginning of the space station era. The third United States Microgravity Payload (USMP-3) and the Life and Microgravity Spacelab (LMS) missions yielded a wealth of microgravity data in FY 1996. The USMP-3 mission included a fluids facility and three solidification furnaces, each designed to examine a different type of crystal growth

BUILDING DSS USING KNOWLEDGE DISCOVERY IN DATABASE APPLIED TO ADMISSION & REGISTRATION FUNCTIONS

This research investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The research describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs' analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The research investigates the organizational requirements that are required to underpin these functions in Egyptian Universities. These requirements have been identified following an in-depth survey of the recruitment process in the Egyptian Universities. This survey employed a multi-part admission and registration DSS questionnaire (ARDSSQ) to identify the required data sources together with the likely users and their information needs. The questionnaire was sent to senior managers within the Egyptian Universities (both private and government) with responsibility for student recruitment, in particular admission and registration. Further, access to a large database has allowed the evaluation of the practical suitability of using a data warehouse structure and knowledge management tools within the decision making framework. 1600 students' records have been analyzed to explore the KDD process, and another 2000 records have been used to build and test the data mining techniques within the KDD process. Moreover, the research has analyzed the key characteristics of data warehouses and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a data warehouse for the Egyptian Universities that handle their admission and registration related archival data. The decision makers' potential benefits of the data warehouse within the student recruitment process will be explored. The design of the proposed admission and registration DSS (ARDSS) will be developed and tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will also be used as a clustering package. Finally, the contribution of this research is found in the following areas: A new DSS development methodology; The development and validation of a new research questionnaire (i.e. ARDSSQ); The development of the admission and registration data warehouse; The evaluation and use of cluster analysis proximities and techniques in the KDD process to find knowledge in the students' records; And the development of the ARDSS software that encompasses the advantages of the KDD and DW and submitting these advantages to the senior admission and registration managers in the Egyptian Universities. The ARDSS software could be adjusted for usage in different countries for the same purpose, it is also scalable to handle new decision situations and can be integrated with other systems

Expression and structural studies of multidomain proteins and complexes

It is generally accepted that there is a level of organization in proteins that overlaps the classical definitions of tertiary and quaternary structure, i.e. sequentially consecutive residues in polypeptide chains fold into distinct compact regions called domains. Many multidomain proteins are flexible and are not amenable to X-ray crystallography or are too big for multi dimensional nuclear magnetic resonance techniques, while other proteins form oligomeric structures from subunits. It is possible using small-angle X-ray and neutron scattering, coupled with molecular modelling techniques, to locate the relative positions of these domains or subunits relative to each other within the full protein structure. This PhD thesis has looked at a variety of native and recombinant oligomeric proteins and domains and attempts have been made to produce low resolution structures of their oligomerisation or their multidomain structures. Expression systems used include a Pseudomonas aeruginosa over-expression system and the baculovirus expression system. One multidomain protein was studied, namely factor I of the complement system. Two forms of factor I were studied, a native form purified from human plasma, and a recombinant form produced in insect cells. Scattering modelling was used to elucidate a bilobal domain arrangement in factor I, in which the different types of carbohydrate present on the two different forms could be modelled. The quaternary structures of two complexes were determined, namely the homo- oligomeric complexes of the Ps. aeruginosa amidase regulatory protein, AmiC, and the Mycobacterium leprae Holliday junction protein, RuvA. It was determined that in solution AmiC exists as a monomer-trimer equilibrium, and that RuvA adopts an octameric structure, both when lice and when complexed with DNA, within which the Holliday junction is buried in the RuvA-DNA complex