الفهرس 
ChallengesOnly 14 pages are availabe for public view
 |  | from | 183 |  |  |
| | | from | 183 | | |
Abstract
In computer vision, the term shape refers to a variety objects such as
images, videos and three-dimensional models that are collected from laser
scans. Shape deformations refer to all kind of methods that aim to alter
the object’s shape or form based on specific applications. The contentaware shape deformation is the process that depends on the local
content/information of shapes in order to apply the required change.
Segmentation is one of the most common problems that is considered an
important pre-process step for a lot of computer vision applications. It
aims at partitioning the object into multiple meaningful regions/segments
in order to separately deal with each region or cluster. Concerning the
image segmentation process, it depends mainly on the local content of
image features such as color, boundary, texture, edges or any
combination of attributes to locate objects in the image. In other words,
each of the pixels in an image region is assigned the same label and is
similar with respect to some characteristic or computed property.
Graph-based segmentation methods are very promising and more
appropriate to find exact solutions to solve the image segmentation
problem via optimization methods. One of the most powerful graph based
image segmentation methods is the GrabCut technique that depends on a
probabilistic model in order to segment color images iteratively. One of
the main contributions of this thesis is to apply modifications and
extensions to the current semi-automatic binary-label GrabCut technique
in order to solve existing problems and improve segmentation accuracy of
natural images. All the proposed image segmentation techniques are
evaluated for relevant accuracy criteria, and comparative studies are
constructed with relevant techniques.
II
The semi-automatic GrabCut capabilities are extended to segmentation of
human faces from images of full humans. The main contribution is the
introduction of a new prior face location model to the GrabCut energy
minimization function in addition to the existing color model. The
location model considers the distance distribution of the pixels from the
silhouette boundary of a fitted head, of a 3D morphable model, to the
image. The proposed technique succeeds in eliminating the camouflage
problem associated with the original GrabCut technique. In addition, it
improves the segmentation accuracy with an error rate of 0.19% in
comparison to the rate of 0.29% of the original GrabCut.
Extension to the semi-automatic GrabCut aims at replacing the manual
initial user intervention with the segmentation process with a fully
automatic scheme that can segment images directly without any user
guidance. Unsupervised image clustering techniques are considered an
ideal solution for the automation process. The Orchard and Bouman
(O&B), Self-Organizing Feature Map (SOFM) and Fuzzy C-Means
(FCM) clustering techniques are selected for the development of the
proposed clustering-based automatic GrabCut. A hybrid technique that
combines advantages of using O&B-based and SOFM-based succeeds in
producing the best segmentation accuracy with an error rate of 3.91% and
overlap score rate of 90.16% in comparison to 5.49% and 87.71% rates
achieved by original GrabCut. The RGB color space produced the best
segmentation results with error rates of 4.25% and 5.4% using the O&Bbased and SOFM-based techniques respectively. The YUV color space
produced the best results using the FCM-based technique with an error
rate of 6.2%.
Another contribution is the extension of the automatic binary-label
GrabCut into a multi-label segmentation technique that can segment a
III
given image to its natural objects. The novel contribution provides the
optimal solution via multiple piecewise iterative behavior instead of
solving the NP-hard multi-labeling problem. On a dataset of 203 images,
the O&B-based multi-label GrabCut achieves a ratio of more than 90% of
the images with acceptable accuracy. Meanwhile, the SOFM-based multilabel GrabCut achieves a ratio of more than 94% of the images with
acceptable consistency with human segmentations.
Concerning segmentation of 3D meshes, it consists of subdividing a
polygonal surface into patches of uniform properties either from a
geometrical point of view or from a perceptual / semantic point of view.
The other contribution of this thesis is to extend the use of the
unsupervised clustering techniques to the problem of 3D mesh
segmentation. The K-means and the FCM clustering techniques are
selected for the development of the proposed clustering-based 3D mesh
segmentation techniques. Based on empirical results on a dataset of 3D
mesh models, the FCM-based mesh segmentation technique outperforms
the K-means-based one in terms of accuracy and consistency with human
segmentations.