Browsing by Subject "Image processing"

Now showing 1 - 3 of 3
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    Open Access
    Classification of cured tobacco leaves by colour and plant position by means of computer processing of digital images
    (1999) Tattersfield, George Metcalf; De Jager, Gerhard
    This dissertation investigates the machine vision grading of flue-cured Virginia tobacco by means of digital processing of tobacco leaf images. With reference to international grading standards and to modem image processing techniques, two classifiers are designed. The colour classifier uses seven features extracted from each leaf image to grade the leaf into one of five official colour classes. It does this with an expected correct classification rate of 93.5%. The plant position classifier identifies the position on the stalk from which a leaf was reaped, using ten size and shape features to classify the leaf into one of six plant position categories. It has a correct classification rate of 70%. Average colours for each colour class and archetypal shapes for each plant position category are derived from the digital leaf data. These should be of value to tobacco graders as objective representations of typical leaves within each class.
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    Open Access
    Design of a low-cost optical motion capture system using a multi-camera configuration and an asynchronous extended kalman filter
    (2025) Meyer, Zakariya; Pretorius, Arnold; Hepworth, James
    Motion capture technology, originating from the entertainment industry, has expanded its applications to various fields including robotics, medical and healthcare, the automotive industry, and virtual and augmented reality. Despite its versatility, the high cost of off-the-shelf commercial motion capture systems makes this technology inaccessible for many smaller institutions and businesses. This dissertation presents the design and development of a low-cost optical motion capture system using a multi-camera setup and a novel algo-rithm that embeds the camera model within an extended Kalman filter (EKF) for precise tracking of a robot's pose. The goal of this dissertation is to reduce the cost of an optical motion capture system by a factor of 7, targeting a total system cost of approximately $900. In comparison, off-the-shelf commercial optical motion capture systems currently cost over $6,600. The methodology includes initial simulation of the system in MATLAB, which is enhanced by real-world experimentation using affordable cameras programmed to track predefined features on a rigid-body robot. These cameras use image processing techniques to transmit pixel coordinate locations to a local base station, where the EKF algorithm processes the data to estimate the robot's pose. Experimental testing results demonstrates the system's ability to achieve a position and orientation accuracy of less than 1 cm and 2◦, respectively, within a 2 × 2 × 2 m capture space, at a cost of $883,34, which is significantly lower when compared to off-the-shelf commercial systems. The development revealed significant challenges in balancing cost and performance, pri-marily due to the limitations of low-cost cameras. The accuracy of motion capture is heavily dependent on camera specifications such as resolution and refresh rate. As cam-era performance improves, costs rise dramatically. The implications of this research are broad, offering a foundation for future explorations into cost-effective motion capture so-lutions. The current work is completely opensource and offered as an invitation to share and collaborate with other institutes of interest.
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    Morphological characterisation of yeast colony growth on solid media using image processing
    (Springer, 1998) Dickason, G; Robinson, A; Sadr-kazemi, N; Harrison, S T L
    To rapidly determine the effect of environmental factors on yeast growth, a cell counting and colony sizing image analysis method was developed to characterise colony growth on solid media. A digitised microscopic image of the yeast was analysed using the Watershed algorithm for cell number determination and a morphological edge detection for colony size determination. The influence of temperature and physiological stress on yeast growth was then investigated over 12.5 h and data extracted by the image analysis method.