This thesis provides a comparative study of machine vision (MV) classification techniques for the detection of missing clips on an automotive part known as a cross car beam. This is a difficult application for an automated MV system because the inspection is conducted in an open manufacturing environment with variable lighting conditions. A laboratory test cell was first used to investigate the effect of lighting. QVision, a software program originally developed at Queen’s University, was used to perform a representative inspection task. Solutions with different light sources and camera settings were investigated in order to determine the best possible set up to acquire an image of the part. Feature selection was applied to improve the results of this classification. The MV system was then installed on an industrial assembly line. QVision was modified to detect the presence or absence of four clips and communicate this information to the computer controlling the manufacturing cell. Features were extracted from the image and then a neuro fuzzy (ANFIS) system was trained to perform the inspection. A performance goal of 0% False Positives and less than 2% False Negatives was achieved with the feature based ANFIS classifier. In addition, the problem of a rusty clip was examined and a radial hole algorithm was used to improve performance in this case. In this case, the system required hours to train. Five new classifiers were then compared to the original feature based ANFIS classifier: 1) feature based with a Neural Network, 2) feature based with principle component analysis (PCA) applied and ANFIS, 3) feature based with PCA applied and a Neural Network, 4) Eigenimage based with ANFIS and 5) Eigenimage based with a Neural Network. The effect of adding a Hough rectangle feature and a principle component colour feature was also studied. It was found that the Neural Network classifier performed better than the ANFIS classifier. When PCA was applied the results improved still further. Overall, feature based classifiers had better performance than Eigenimage based classifiers. Finally, it should be noted that these six classifiers required only minutes to train. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2009-08-07 17:03:10.422

Like many other multimedia applications, image compression involves a significant amount of data processing for coding images. Sophisticated general-purpose processors with parallel architectures and advanced cache systems can be dedicated to enhancing performance for serial multimedia applications through parallelization. This thesis describes parallelization of the JasPer reference software for the JPEG-2000 image compression standard and presents results from simulation, and from hardware execution on a multicore processor where speedups of more than 2 are obtained with 4 processors. Results from execution and cache behavior analysis are presented to establish the expected speedup and to further characterize JasPer execution. The JasPer encoding process has been analyzed on a single processor for both simulated and hardware execution in order to obtain more insights into application behavior. On recent hardware platforms, the significant contributors to the total execution time have been identified through profiling. The granularity of parallelism for parallelizable loops have been analyzed for execution on real hardware. Cache behavior and memory access pattern have been studied closely for the simulated execution. To facilitate parallelization, selected parallelizable loops have been transformed in order to assist the partitioning of loop iterations for parallel execution and to increase workload granularity and reduce synchronization overhead. These modifications include loop index and body transformation, and loop fusion. A memory access pattern tracking feature has also been introduced for serial and parallel execution of a program in simulation. This feature tracks the number of memory accesses in a particular data region during a particular interval of time in order to gain additional insights into execution behavior. The multithreaded execution of the parallelized JasPer encoder presents a relatively balanced workload which indicates a reasonable efficiency for parallel execution. The generated images have been compared against their original images by using analytical tools to ensure the image quality and to verify correctness. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2011-10-29 00:10:43.437

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