Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking level sets by level sets: a method for solving the shape from shading problem
Computer Vision and Image Understanding
International Journal of Computer Vision
Scale-Space Properties of the Multiscale Morphological Dilation-Erosion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computable elastic distances between shapes
SIAM Journal on Applied Mathematics
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Deformable templates for face recognition
Journal of Cognitive Neuroscience
Affine iterative closest point algorithm for point set registration
Pattern Recognition Letters
A Continuum Mechanical Approach to Geodesics in Shape Space
International Journal of Computer Vision
Video-based descriptors for object recognition
Image and Vision Computing
Region matching in the temporal study of mammograms using integral invariant scale-space
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
| Hi-index | 0.00 |
We present a variational approach to the problem of registering planar shapes despite missing parts. Registration is achieved through the evolution of a partial differential equation that simultaneously estimates the shape of the missing region, the underlying 'complete shape' and the collection of group elements (Euclidean, affine) corresponding to the registration. Our technique can be used both for shapes, for instance represented as characteristic functions (binary images) and for grayscale images where it can be interpreted as region 'inpainting.' The novelty of the approach lies on the fact that, rather than estimating the missing region in each image independently, we pose the problem as a joint registration with respect to an underlying 'complete shape' from which the complete version of the original data is obtained via a group action. We simultaneously estimate the complete shape and the group action in an alternating minimization scheme.