Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Tracking Objects Using Density Matching and Shape Priors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Statistical Shape Analysis: Clustering, Learning, and Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Affine-Invariant Geometric Shape Priors for Region-Based Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
B-spline active contour with handling of topology changes for fast video segmentation
EURASIP Journal on Applied Signal Processing
Affine-Invariant multi-reference shape priors for active contours
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
IEEE Transactions on Image Processing
Stochastic differential equations and geometric flows
IEEE Transactions on Image Processing
Constraining active contour evolution via Lie Groups of transformation
IEEE Transactions on Image Processing
Introducing shape priors in object-based tomographic reconstruction
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multiphase active contour segmentation constrained by evolving medial axes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
Smooth contour coding with minimal description length active grid segmentation techniques
Pattern Recognition Letters
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Computer vision for fruit harvesting robots state of the art and challenges ahead
International Journal of Computational Vision and Robotics
Real-Time 3d image segmentation by user-constrained template deformation
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Journal of Mathematical Imaging and Vision
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In this paper, we present a new way of constraining the evolution of an active contour with respect to a set of fixed reference shapes. This approach is based on a description of shapes by the Legendre moments computed from their characteristic function. This provides a region-based representation that can handle arbitrary shape topologies. Moreover, exploiting the properties of moments, it is possible to include intrinsic affine invariance in the descriptor, which solves the issue of shape alignment without increasing the number of d.o.f. of the initial problem and allows introducing geometric shape variabilities. Our new shape prior is based on a distance, in terms of descriptors, between the evolving curve and the reference shapes. Minimizing the corresponding shape energy leads to a geometric flow that does not rely on any particular representation of the contour and can be implemented with any contour evolution algorithm. We introduce our prior into a two-class segmentation functional, showing its benefits on segmentation results in presence of severe occlusions and clutter. Examples illustrate the ability of the model to deal with large affine deformation and to take into account a set of reference shapes of different topologies.