Parallel Hough Transform for image processing on a Mesh of Trees architecture

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.Includes bibliographical references.The Hough Transform and its generalization have been found very useful in computer vision and are frequently used in detecting the shape of object boundaries in image pattern analysis. It uses a constraint equation relating points in feature space to possible parameter values of the searched for shape. For each feature point, invariably edge points, votes are accumulated for all parameter combinations which satisfy the constraint. A large vote indicates strong evidence for the presence of the shape with the corresponding parameters. This transform is computationally intensive. The aim of the thesis is to design a group of parallel algorithms implemented on Mesh of Trees to calculate the Hough Transform. Our algorithm would have an optimal time complexity based on Mesh of Trees parallel computer and also a low memory usage. The developed algorithms were simulated on a cube parallel computer. The results were analyzed and compared to those of other parallel computer models