  Image segmentation is an essential component in many image processing and
computer vision tasks. The primary goal of image segmentation is to simplify an
image for easier analysis, and there are two broad approaches for achieving
this: edge based methods, which extract the boundaries of specific known
objects, and region based methods, which partition the image into regions that
are statistically homogeneous. One of the more prominent edge finding methods,
known as the level set method, evolves a zero-level contour in the image plane
with gradient descent until the contour has converged to the object boundaries.
While the classical level set method and its variants have proved successful in
segmenting real images, they are susceptible to becoming stuck in noisy regions
of the image plane without a priori knowledge of the image and they are unable
to provide details beyond object outer boundary locations. We propose a
modification to the variational level set image segmentation method that can
quickly detect object boundaries by making use of random point initialization.
We demonstrate the efficacy of our approach by comparing the performance of our
method on real images to that of the prominent Canny Method.
