Visual inspection : image sampling, algorithms and architectures

The thesis concerns the hexagonal sampling of images, the processing of industrially derived images, and the design of a novel processor element that can be assembled into pipelines to effect fast, economic and reliable processing. A hexagonally sampled two dimensional image can require 13.4% fewer sampling points than a square sampled equivalent. The grid symmetry results in simpler processing operators that compute more efficiently than square grid operators. Computation savings approaching 44% arc demonstrated. New hexagonal operators arc reported including a Gaussian smoothing filter, a binary thinner, and an edge detector with comparable accuracy to that of the Sobel detector. The design of hexagonal arrays of sensors is considered. Operators requiring small local areas of support are shown to be sufficient for processing controlled lighting and industrial images. Case studies show that small features in hexagonally processed images maintain their shape better, and that processes can tolerate a lower signal to noise ratio, than that for equivalent square processed images. The modelling of small defects in surfaces has been studied in depth. The flexible programmable processor element can perform the low level local operators required for industrial image processing on both square and hexagonal grids. The element has been specified and simulated by a high level computer program. A fast communication channel allows for dynamic reprogramming by a control computer, and the video rate element can be assembled into various pipeline architectures, that may eventually be adaptively controlled

Sparse Volumetric Deformation

Volume rendering is becoming increasingly popular as applications require realistic solid shape representations with seamless texture mapping and accurate filtering. However rendering sparse volumetric data is difficult because of the limited memory and processing capabilities of current hardware. To address these limitations, the volumetric information can be stored at progressive resolutions in the hierarchical branches of a tree structure, and sampled according to the region of interest. This means that only a partial region of the full dataset is processed, and therefore massive volumetric scenes can be rendered efficiently. The problem with this approach is that it currently only supports static scenes. This is because it is difficult to accurately deform massive amounts of volume elements and reconstruct the scene hierarchy in real-time. Another problem is that deformation operations distort the shape where more than one volume element tries to occupy the same location, and similarly gaps occur where deformation stretches the elements further than one discrete location. It is also challenging to efficiently support sophisticated deformations at hierarchical resolutions, such as character skinning or physically based animation. These types of deformation are expensive and require a control structure (for example a cage or skeleton) that maps to a set of features to accelerate the deformation process. The problems with this technique are that the varying volume hierarchy reflects different feature sizes, and manipulating the features at the original resolution is too expensive; therefore the control structure must also hierarchically capture features according to the varying volumetric resolution. This thesis investigates the area of deforming and rendering massive amounts of dynamic volumetric content. The proposed approach efficiently deforms hierarchical volume elements without introducing artifacts and supports both ray casting and rasterization renderers. This enables light transport to be modeled both accurately and efficiently with applications in the fields of real-time rendering and computer animation. Sophisticated volumetric deformation, including character animation, is also supported in real-time. This is achieved by automatically generating a control skeleton which is mapped to the varying feature resolution of the volume hierarchy. The output deformations are demonstrated in massive dynamic volumetric scenes

Implementation of a real time Hough transform using FPGA technology

This thesis is concerned with the modelling, design and implementation of efficient architectures for performing the Hough Transform (HT) on mega-pixel resolution real-time images using Field Programmable Gate Array (FPGA) technology. Although the HT has been around for many years and a number of algorithms have been developed it still remains a significant bottleneck in many image processing applications. Even though, the basic idea of the HT is to locate curves in an image that can be parameterized: e.g. straight lines, polynomials or circles, in a suitable parameter space, the research presented in this thesis will focus only on location of straight lines on binary images. The HT algorithm uses an accumulator array (accumulator bins) to detect the existence of a straight line on an image. As the image needs to be binarized, a novel generic synchronization circuit for windowing operations was designed to perform edge detection. An edge detection method of special interest, the canny method, is used and the design and implementation of it in hardware is achieved in this thesis. As each image pixel can be implemented independently, parallel processing can be performed. However, the main disadvantage of the HT is the large storage and computational requirements. This thesis presents new and state-of-the-art hardware implementations for the minimization of the computational cost, using the Hybrid-Logarithmic Number System (Hybrid-LNS) for calculating the HT for fixed bit-width architectures. It is shown that using the Hybrid-LNS the computational cost is minimized, while the precision of the HT algorithm is maintained. Advances in FPGA technology now make it possible to implement functions as the HT in reconfigurable fabrics. Methods for storing large arrays on FPGA’s are presented, where data from a 1024 x 1024 pixel camera at a rate of up to 25 frames per second are processed

Split and Shift Methodology: Overcoming Hardware Limitations on Cellular Processor Arrays for Image Processing

Na era multimedia, o procesado de imaxe converteuse nun elemento de singular importancia nos dispositivos electrónicos. Dende as comunicacións (p.e. telemedicina), a seguranza (p.e. recoñecemento retiniano) ou control de calidade e de procesos industriais (p.e. orientación de brazos articulados, detección de defectos do produto), pasando pola investigación (p.e. seguimento de partículas elementais) e diagnose médica (p.e. detección de células estrañas, identificaciónn de veas retinianas), hai un sinfín de aplicacións onde o tratamento e interpretación automáticas de imaxe e fundamental. O obxectivo último será o deseño de sistemas de visión con capacidade de decisión. As tendencias actuais requiren, ademais, a combinación destas capacidades en dispositivos pequenos e portátiles con resposta en tempo real. Isto propón novos desafíos tanto no deseño hardware como software para o procesado de imaxe, buscando novas estruturas ou arquitecturas coa menor area e consumo de enerxía posibles sen comprometer a funcionalidade e o rendemento

Models of coupled smooth muscleand endothelial cells

Impaired mass transfer characteristics of blood borne vasoactive species such as ATP in regions such as an arterial bifurcation have been hypothesized as a prospective mechanism in the aetiology of atherosclerotic lesions. Arterial endothelial (EC) and smooth muscle cells (SMC) respond differentially to altered local hemodynamics and produce coordinated macro-scale responses via intercellular communication. Using a computationally designed arterial segment comprising large populations of mathematically modelled coupled ECs & SMCs, we investigate their response to spatial gradients of blood borne agonist concentrations and the effect of micro-scale driven perturbation on the macro-scale. Altering homocellular (between same cell type) and heterocellular (between different cell types) intercellular coupling we simulated four cases of normal and pathological arterial segments experiencing an identical gradient in the concentration of the agonist. Results show that the heterocellular calcium (Ca2+) coupling between ECs and SMCs is important in eliciting a rapid response when the vessel segment is stimulated by the agonist gradient. In the absence of heterocellular coupling, homocellular Ca2+ coupling between smooth muscle cells is necessary for propagation of Ca2+ waves from downstream to upstream cells axially. Desynchronized intracellular Ca2+ oscillations in coupled smooth muscle cells are mandatory for this propagation. Upon decoupling the heterocellular membrane potential, the arterial segment looses the inhibitory effect of endothelial cells on the Ca2+ dynamics of underlying smooth muscle cells. The full system comprising hundreds of thousands of coupled nonlinear ordinary differential equations simulated on the massively parallel Blue Gene architecture. The use of massively parallel computational architectures shows the capability of this approach to address macro-scale phenomena driven by elementary micro-scale components of the system