Heikki Kälviäinen presents Segmentation of partially overlapping convex objects

This presentation considers overlapping objects in an image, usually at
microscopic level and for industrial and medical purposes. The methods for the
segmentation of partially overlapping convex shape objects in silhouette images
are proposed.

The methods involve two main steps: contour evidence extraction
and contour estimation. Contour evidence extraction starts by recovering
contour segments from a binarized image using concave contour point detection.
The contour segments which belong to the same objects are grouped by utilizing
a
criterion defining the convexity, symmetry, and ellipticity of the resulting
object. The grouping is formulated as a combinatorial optimization problem and
solved using the well-known branch and bound algorithm. Finally, the contour
estimation is implemented through a non-linear ellipse fittting problem in
which partially observed objects are modeled in the form of ellipse-shape
objects.

The experiments on a dataset of consisting of nanoparticles demonstrate that
the
proposed method outperforms four current state-of-art approaches in overlapping
convex objects segmentation. The method relies only on edge information
and can be applied to any segmentation problems where the objects are partially
overlapping and contain an approximately convex shape.