3 research outputs found
A Proximal Bregman Projection Approach to Continuous Max-Flow Problems Using Entropic Distances
One issue limiting the adaption of large-scale multi-region segmentation is
the sometimes prohibitive memory requirements. This is especially troubling
considering advances in massively parallel computing and commercial graphics
processing units because of their already limited memory compared to the
current random access memory used in more traditional computation. To address
this issue in the field of continuous max-flow segmentation, we have developed
a \textit{pseudo-flow} framework using the theory of Bregman proximal
projections and entropic distances which implicitly represents flow variables
between labels and designated source and sink nodes. This reduces the memory
requirements for max-flow segmentation by approximately 20\% for Potts models
and approximately 30\% for hierarchical max-flow (HMF) and directed acyclic
graph max-flow (DAGMF) models. This represents a great improvement in the
state-of-the-art in max-flow segmentation, allowing for much larger problems to
be addressed and accelerated using commercially available graphics processing
hardware.Comment: 10 page
A Continuous Max-Flow Approach to Cyclic Field Reconstruction
Reconstruction of an image from noisy data using Markov Random Field theory
has been explored by both the graph-cuts and continuous max-flow community in
the form of the Potts and Ishikawa models. However, neither model takes into
account the particular cyclic topology of specific intensity types such as the
hue in natural colour images, or the phase in complex valued MRI. This paper
presents \textit{cyclic continuous max-flow} image reconstruction which models
the intensity being reconstructed as having a fundamentally cyclic topology.
This model complements the Ishikawa model in that it is designed with image
reconstruction in mind, having the topology of the intensity space inherent in
the model while being readily extendable to an arbitrary intensity resolution.Comment: 8 pages, 1 figur
Shape Complexes in Continuous Max-Flow Hierarchical Multi-Labeling Problems
Although topological considerations amongst multiple labels have been
previously investigated in the context of continuous max-flow image
segmentation, similar investigations have yet to be made about shape
considerations in a general and extendable manner. This paper presents shape
complexes for segmentation, which capture more complex shapes by combining
multiple labels and super-labels constrained by geodesic star convexity. Shape
complexes combine geodesic star convexity constraints with hierarchical label
organization, which together allow for more complex shapes to be represented.
This framework avoids the use of co-ordinate system warping techniques to
convert shape constraints into topological constraints, which may be ambiguous
or ill-defined for certain segmentation problems.Comment: 9 pages, 1 figur