No description available.

No description available.

This thesis is concerned with the approximation of Cartesian coordinate data by parametric curves and surfaces, with an emphasis upon a technique known as parametric orthogonal distance regression (parametric ODR). The technique has become increasingly popular in the literature over the past decade and has applications in a wide range of fields, including metrology-the science of measurement, and computer aided design (CAD) modelling. Typically, the data are obtained by recording points measured in the surface of some physical artefact, such as a manufactured part. Parametric ODR involves minimizing the shortest distances from the data to the curve or surface in some norm. Under moderate assumptions, these shortest distances are orthogonal projections from the data onto the approximant, hence the nomenclature ODR. The motivation behind this type of approximation is that, by using a distance-based measure, the resulting best fit curve or surface is independent of the position or orientation of the physical artefact from which the data is obtained. The thesis predominately concerns itself with parametric ODR in a least squares setting, although it is indicated how the techniques described can be extended to other error measures in a fairly straightforward manner. The parametric ODR problem is formulated mathematically, and a detailed survey of the existing algorithms for solving it is given. These algorithms are then used as the basis for developing new techniques, with an emphasis placed upon their efficiency and reliability. The algorithms (old and new) detailed in this thesis are illustrated by problems involving well-known geometric elements such as lines, circles, ellipse and ellipsoids, as well as spline curves and surfaces. Numerical considerations specific to these individual elements, including ones not previously reported in the literature, are addressed. We also consider a sub-problem of parametric ODR known as template matching, which involves mapping in an optimal way a set of data into the same frame of reference as a fixed curve or surface.

Large quantities of bulk grain are moved using graincars in Canada and other parts of the world. Automation has not progressed significantly in the grain industry probably because the market is limited for automated systems. A prototype of a robot (“Grain-o-bot”) using machine vision to automatically open and close graincar hopper gates and detect the contents of the graincar was built and studied. The “Grain-o-bot” was a Cartesian robot equipped with two cameras and an opening tool as the end-effector. One camera acted as the eye to determine the sprocket location, and guided the end-effector to the sprocket opening. For most applications, machine vision solutions based on pattern recognition were developed using images acquired in a laboratory setting. Major constraints with these solutions occurred when implementing them in real world applications. So the first step for this automation was to correctly identify the hopper gate sprocket on the grain car. Algorithms were developed to detect and identify the sprocket under proper lighting conditions with 100% accuracy. The performance of the algorithms was also evaluated for the identification of the sprocket on a grain car exposed to different lighting conditions, which are expected to occur in typical grain unloading facilities. Monochrome images of the sprocket from a model system were acquired using different light. Correlation and pattern recognition techniques using a template image combined with shape detection were used for sprocket identification. The images were pre-processed using image processing techniques, prior to template matching. The template image developed from the light source that was similar to the light source used to acquire ii images was more successful in identifying the sprocket than the template image developed using different light sources. A sample of the graincar content was taken by slightly opening and immediately closing the hopper gates. The sample was identified by taking an image using the second camera and performing feature matching. An accuracy of 99% was achieved in identifying Canada Western Red Spring (CWRS) wheat and 100% for identifying barley and canola.

Large quantities of bulk grain are moved using graincars in Canada and other parts of the world. Automation has not progressed significantly in the grain industry probably because the market is limited for automated systems. A prototype of a robot (“Grain-o-bot”) using machine vision to automatically open and close graincar hopper gates and detect the contents of the graincar was built and studied. The “Grain-o-bot” was a Cartesian robot equipped with two cameras and an opening tool as the end-effector. One camera acted as the eye to determine the sprocket location, and guided the end-effector to the sprocket opening. For most applications, machine vision solutions based on pattern recognition were developed using images acquired in a laboratory setting. Major constraints with these solutions occurred when implementing them in real world applications. So the first step for this automation was to correctly identify the hopper gate sprocket on the grain car. Algorithms were developed to detect and identify the sprocket under proper lighting conditions with 100% accuracy. The performance of the algorithms was also evaluated for the identification of the sprocket on a grain car exposed to different lighting conditions, which are expected to occur in typical grain unloading facilities. Monochrome images of the sprocket from a model system were acquired using different light. Correlation and pattern recognition techniques using a template image combined with shape detection were used for sprocket identification. The images were pre-processed using image processing techniques, prior to template matching. The template image developed from the light source that was similar to the light source used to acquire ii images was more successful in identifying the sprocket than the template image developed using different light sources. A sample of the graincar content was taken by slightly opening and immediately closing the hopper gates. The sample was identified by taking an image using the second camera and performing feature matching. An accuracy of 99% was achieved in identifying Canada Western Red Spring (CWRS) wheat and 100% for identifying barley and canola.

Stuttgart, Univ., Diss., 2003.

Due to the amount of flow simulation and measurement data, automatic detection, classification and visualization of features is necessary for an inspection. Therefore, many automated feature detection methods have been developed in recent years. However, one feature class is visualized afterwards in most cases, and many algorithms have problems in the presence of noise or superposition effects. In contrast, image processing and computer vision have robust methods for feature extraction and computation of derivatives of scalar fields. Furthermore, interpolation and other filter can be analyzed in detail. An application of these methods to vector fields would provide a solid theoretical basis for feature extraction. The authors suggest Clifford algebra as a mathematical framework for this task. Clifford algebra provides a unified notation for scalars and vectors as well as a multiplication of all basis elements. The Clifford product of two vectors provides the complete geometric information of the relative positions of these vectors. Integration of this product results in Clifford correlation and convolution which can be used for template matching on vector fields. Furthermore, for frequency analysis of vector fields and the behavior of vector-valued filters, a Clifford Fourier transform has been derived for 2 and 3 dimensions. Convolution and other theorems have been proved, and fast algorithms for the computation of the Clifford Fourier transform exist. Therefore the computation of Clifford convolution can be accelerated by computing it in Clifford Fourier domain. Clifford convolution and Fourier transform can be used for a thorough analysis and subsequent visualization of vector fields

No description available.

No description available.

A busca por padrões em imagens é um problema clássico em visão computacional e consiste em detectar a presença de uma dada máscara em uma imagem digital. Tal tarefa pode se tornar consideravelmente mais complexa com a invariância aos aspectos da imagem tais como rotação, escala, translação, brilho e contraste (RSTBC - rotation, scale, translation, brightness and contrast). Um algoritmo de busca de máscara foi recentemente proposto. Este algoritmo, chamado de Ciratefi, é invariante aos aspectos RSTBC e mostrou-se bastante robusto. Entretanto, a execução deste algoritmo em um computador convencional requer diversos segundos. Além disso, sua implementação na forma mais geral em hardware é difícil pois há muitos parâmetros ajustáveis. Este trabalho propõe o projeto de um software que gera automaticamente módulos compiláveis em Hardware Description Logic (VHDL) que implementam o filtro circular do algoritmo Ciratefi em dispositivos Field Programmable Gate Array (FPGA). A solução proposta acelera o tempo de processamento de 7s (em um PC de 3GHz) para 1,367ms (em um dispositivo Stratix III da Altera). Esta performance excelente (mais do que o necessário em sistemas em tempo-real) pode levar a sistemas de visão computacional de alta performance e de baixo custo. / Template matching is a classical problem in computer vision. It consists in detecting the presence of a given template in a digital image. This task becomes considerably more complex with the invariance to rotation, scale, translation, brightness and contrast (RSTBC). A novel RSTBC-invariant robust template matching algorithm named Ciratefi was recently proposed. However, its execution in a conventional computer takes several seconds. Moreover, the implementation of its general version in hardware is difficult, because there are many adjustable parameters. This work proposes a software that automatically generates compilable Hardware Description Logic (VHDL) modules that implement the circular filter of the Ciratefi template matching algorithm in Field Programmable Gate Array (FPGA) devices. The proposed solution accelerates the time to process a frame from 7s (in a 3GHz PC) to 1.367ms (in Altera Stratix III device). This excellent performance (more than the required for a real-time system) may lead to cost-effective high-performance coprocessing computer vision systems.