Medical Image Segmentation using Skeletally Coupled Deformable
Segmentation uses a deformable model that allows for region competition
(for details see skeletally coupled deformable
models). The main application domain is segmenting
carpal bones from CT images.
Finds an optimal correspondence (alignment) between two curves. An alignment
curve is introduced to ensure symmetric treatment of both curves. Dynamic
programming is used to find the optimal correspondence. The application
include handwritten character recognition, prototype formation, shape morphing,
and object recognition. Details.
Curve matching framework is used to compute an average of a set of curves.
This average can serve as the mean of a collection of anatomical structures
and can be used for distinguishing diseased from normal states. Details
Shock graph matching for recognizing shapes
The similarity of shapes is defined in terms of the minimum extent of deformation
necessary to morph one shape to another. The space of shapes and deformation
paths are discretized. An edit-distance algorithm which finds the optimal
deformation path in polynomial time. The approach is robust to a variety
of visual transformations including boundary perturbations, articulation
and deformation of parts, viewpoint variation, segmentation errors and
partial occlusion. 100% recognition rate is achieved on two distinct databases
of 99 and 216 shapes each. Details
Indexing into large shape database
We have created a database consisting of 1032 shapes, developed a coarse-scale
version of shock graph matching and an exemplar-based indexing scheme,
to make shock-based indexing into large shape databases practical. Details
Symmetry-based segmentation and recognition.
Shape-based Object Recognition.
Laboratory for Engineering Man/Machine
Box D, Division of Engineering
Providence, RI 02912