Date of Award

12-2012

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Electrical Engineering

Advisor

Birchfield, Stanley T

Committee Member

Dean , Brian C

Committee Member

Hoover , Adam

Abstract

In this thesis, we present an efficient graph-based image-segmentation algorithm that improves upon the drawbacks of the minimum spanning tree based segmentation algorithm, namely leaks that occur due to the criterion used to merge regions, and the sensitivity of the output to the parameter k. To address these problems, we propose the use of bidirectional Mahalanobis distance, along with a Gaussian model for each region, and an intuitive normalized parameter τ that replaces k and works for all images without having to be changed. Furthermore, we propose an approximation to the algorithm that enables it to run efficiently in O(NlogN) time (N represents the number of pixels in the image), without compromising on the performance. Experiments on a wide variety of images demonstrates the ability of the algorithm to achieve accurate results in an efficient manner.

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