Image Segmentation | |||||||

* Active Contours without Edges for Vector-Valued Images * Active Contours without Edges | |||||||

## Image Segmentation using level sets and the piecewise constant Mumford-Shah Model
We propose a multiphase level set algorithm for solving the minimal partition
Problem for image segmentation. Our starting point is the piecewise constant
Mumford-Shah model for segmentation. The proposed method can also be viewed as
as an extension and generalization of as active contour model without edges
based on a 2-phase segmentation (Chan and Vese, 1999). Our Multiphase level
set formulation is new and of interest on its own: by construction, it
automatically avoids the problems of vacuum and overlap; it needs only
log n level set functions for n phases; it can represent boundaries with
complex topologies, including triple junctions.(from CAM 00-14 abstract,
April 2000).
Report by Chan and Vese .
## Active Contours without Edges for Vector-Valued Images
We propose an active contour algorithm for object detection in vector
valued images (such as RGB or Multi-spectral). The model is as extension
of the scalar Chan-Vese(1999) algorithm to the vector-valued case. The model
minimizes a Mumford-Shah functional over the length of the contour,
plus the sum of the fitting error over the each component of the vector-valued
image. Like the C-V model, our vector-valued model can detect both edges with
or without gradient. We show examples where our model detects vector-valued
objects, which are undetectable in any scalar representation. For instance,
objects with different missing parts in different channels are completely
detected (such as occlusion). Also, in color images, objects which are
invisible in each channel or in intensity, can be detected by our algorithm.
Finally, the model is robust with respect to noise, requiring no a priori
denoising step.(from CAM 99-35 abstract, Oct 1999).
Report by Chan, Sandberg and Vese . ## Active Contours without Edges We propose a new model for active contours to detect objects in a given image,
based on techniques of curve evolution, Mumford-Shah functional for segmentation
and level sets. Our model can detect objects whose boundaries are not
necessarily defined by gradient. We minimize an energy which can be seen as
a particular case of so-called minimal partition problem. In the level set
formulation, the problem becomes a "mean-curvature flow"-like evolving the
active contour, which will stop on the desired boundary. However,
the stopping term does not depend on the gradient of the images, as in the
classical active contour models, but is instead related to
a particular segmentation of the image. (from CAM 98-53 abstract, Dec. 98).
Report by Chan and Vese .
Reports on Segmentations
Reports on Computational Techniques Reports on Level Set Methods People |