A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using deformable surfaces to segment 3-D images and infer differential structures
CVGIP: Image Understanding
Region-based strategies for active contour models
International Journal of Computer Vision
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
“Brownian strings”: segmenting images with stochastically deformable contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
An unbiased active contour algorithm for object tracking
Pattern Recognition Letters
Control of polygonal mesh resolution for 3-D computer vision
Graphical Models and Image Processing
Constrained active region models for fast tracking in color image sequences
Computer Vision and Image Understanding
Local topological parameters in a tetrahedral representation
CVGIP: Graphical Models and Image Processing
Implicit fairing of irregular meshes using diffusion and curvature flow
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
The velocity snake: deformable contour for tracking in Spatio-Velocity space
Computer Vision and Image Understanding
Unification of distance and volume optimization in surface simplification
Graphical Models and Image Processing
Shape and topology constraints on parametric active contours
Computer Vision and Image Understanding
Tracking Deformable Objects in the Plane Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-Rigid Motion Analysis in Medical Images: a Physically Based Approach
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Segmentation of Medical Image Objects Using Deformable Shape Loci
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Interactive Medical Image Segmentation with United Snakes
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Improving snake performance via a dual active contour
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Bayesian Analysis of Cell Nucleus Segmentation by a Viterbi Search Based Active Contour
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Image-based change detection of areal objects using differential snakes
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Deforming meshes that split and merge
ACM SIGGRAPH 2009 papers
A Combinatorial Method for Topology Adaptations in 3D Deformable Models
International Journal of Computer Vision
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
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We present a discrete contour model for the segmentation of image data with any dimension of image domain and value range. The model consists of a representation using simplex meshes and a mechanical formulation of influences that drive an iterative segmentation. The object's representation as well as the influences are valid for any dimension of the image domain. The image influences introduced here, can combine information from independent channels of higher-dimensional value ranges. Additionally, the topology of the model automatically adapts to objects contained in images. Noncontextual tests have validated the ability of the model to reproducibly delineate synthetic objects. In particular, images with a signal to noise ratio of SNR \leq 0.5 are delineated within two pixels of their ground truth contour. Contextual validations have shown the applicability of the model for medical image analysis in image domains of two, three, and four dimensions in single as well as multichannel value ranges.