Date of Award
12-2008
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Legacy Department
Electrical Engineering
Committee Chair/Advisor
Birchfield, Stanley T
Committee Member
Walker , Ian D
Committee Member
Hoover , Adam W
Committee Member
Wells , Christina E
Abstract
Linear feature detection in digital images is an important low-level operation in
computer vision that has many applications. In remote sensing tasks, it can be used
to extract roads, railroads, and rivers from satellite or low-resolution aerial images,
which can be used for the capture or update of data for geographic information and
navigation systems. In addition, it is useful in medical imaging for the extraction of
blood vessels from an X-ray angiography or the bones in the skull from a CT or MR
image. It also can be applied in horticulture for underground plant root detection in
minirhizotron images.
In this dissertation, a fast and automatic algorithm for linear feature extraction
from images is presented. Under the assumption that linear feature is a sequence
of contiguous pixels where the image intensity is locally maximal in the direction of
the gradient, linear features are extracted as non-overlapping connected line segments
consisting of these contiguous pixels.
To perform this task, point process is used to model line segments network in
images. Specific properties of line segments in an image are described by an intensity
energy model. Aligned segments are favored while superposition is penalized. These
constraints are enforced by an interaction energy model. Linear features are extracted
from the line segments network by minimizing a modified Candy model energy function
using a greedy algorithm whose parameters are determined in a data-driven
manner. Experimental results from a collection of different types of linear features
(underground plant roots, blood vessels and urban roads) in images demonstrate the
effectiveness of the approach.
Recommended Citation
Zeng, Guang, "Real-Time Automatic Linear Feature Detection in Images" (2008). All Dissertations. 291.
https://open.clemson.edu/all_dissertations/291