1 research outputs found
Regional Active Contours based on Variational level sets and Machine Learning for Image Segmentation
Image segmentation is the problem of partitioning an image into different
subsets, where each subset may have a different characterization in terms of
color, intensity, texture, and/or other features. Segmentation is a fundamental
component of image processing, and plays a significant role in computer vision,
object recognition, and object tracking. Active Contour Models (ACMs)
constitute a powerful energy-based minimization framework for image
segmentation, which relies on the concept of contour evolution. Starting from
an initial guess, the contour is evolved with the aim of approximating better
and better the actual object boundary. Handling complex images in an efficient,
effective, and robust way is a real challenge, especially in the presence of
intensity inhomogeneity, overlap between the foreground/background intensity
distributions, objects characterized by many different intensities, and/or
additive noise. In this thesis, to deal with these challenges, we propose a
number of image segmentation models relying on variational level set methods
and specific kinds of neural networks, to handle complex images in both
supervised and unsupervised ways. Experimental results demonstrate the high
accuracy of the segmentation results, obtained by the proposed models on
various benchmark synthetic and real images compared with state-of-the-art
active contour models.Comment: IMT PhD thesis, 201