The segmentation of objects has been an area
of interest in numerous fields.
The use of texture has been explored to
improve convergence
in the presence
of cluttered backgrounds
or objects with distinct textures,
where intensity variations are insufficient.
Additionally, saliency and feature maps have been
applied for contour initialization.
However,
taking advantage of texture to improve initialization and convergence
has not been extensively explored.
To address this,
we propose a hybrid structural and texture distinctiveness vector field convolution (STVFC) approach,
where both the structural
characteristics and
the concept of texture distinctiveness are incorporated into a multi-functional vector field convolution
(VFC) model. In this novel approach, texture distinctiveness is used to enable automatic initialization
and is incorporated with intensity variation to improve and accelerate convergence towards the object
boundary. Experiments using three datasets, containing natural images and Brodatz textures, demonstrated
that STVFC achieved better or comparable segmentation accuracy.