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Image Segmentation |
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An Extended Chain Graph
and its Applications in Computer Vision Both undirected graphical models and
directed acyclic graphical models have some limitation on their modeling
power. While Chain Graph models are powerful enough, existing Chain Graph
models used in real applications have simplified structures, therefore not
fully leveraging the power of Chain Graph. This fact is due to several reasons
such as the difficulty of parameter learning
for a complex Chain Graph with very general topology and the lack of efficient
probabilistic inference method for such Chain Graph. To this end we
developed an extended Chain Graph model and presented a suite of
methods |
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A Bayesian Network Model
for Automatic and Interactive Image Segmentation We proposed a hierarchical Bayesian Network
(BN) model for both automatic and interactive image segmentation. Our BN segmentation model captures the
statistical relationships among multiple image entities and encodes several constraints
for effective image segmentation. In addition, we developed a mutual information based actively interactive segmentation
approach that were more effective and efficient than the passively
interactive segmentation. More information is
available here. |
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Level Set-based Image
Segmentation with Global Shape Prior For image segmentation of objects
with certain types of shapes, the global shape prior information can be
leveraged to improve the effectiveness of image segmentation. We propose a
global shape based on the contour difference and integrate it into a
level-set based image segmentation framework. We have applied this
segmentation approach |
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Video Segmentation using a
Spatio-temporal Conditional Random Field For segmentation in video sequences,
the spatial and temporal information among the neighborhood of a pixel should Medical Image Segmentation
In this research, we
develop a model-based automated approach to extracting tubular objects and
curvilinear structures
from noisy volume images. Details
about this project may be found here
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