Automatic Construction of 2D Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Exact Method for Computing the Area Moments of Wavelet and Spline Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Isophotes Selection and Reaction-Diffusion Model for Object Boundaries Estimation
International Journal of Computer Vision
Optimal Level Curves and Global Minimizers of Cost Functionals in Image Segmentation
Journal of Mathematical Imaging and Vision
Parallel Image Matching on PC Cluster
Proceedings of the 8th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Influence of the Noise Model on Level Set Active Contour Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast Definition for Optical Coherent Polarimetric Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-Theoretic Active Polygons for Unsupervised Texture Segmentation
International Journal of Computer Vision
Object Contour Extraction Using Adaptive B-Snake Model
Journal of Mathematical Imaging and Vision
Probabilistic deformable models for weld defact contour estimation in radiography
Machine Graphics & Vision International Journal
Nonparametric Level-Set Segmentation Based on the Minimization of the Stochastic Complexity
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Dynamic B-snake model for complex objects segmentation
Image and Vision Computing
Energy-based reconstruction of 3D curves for quality control
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Polarimetric SAR image segmentation with B-splines and a new statistical model
Multidimensional Systems and Signal Processing
Smooth contour coding with minimal description length active grid segmentation techniques
Pattern Recognition Letters
Combined geometric-texture image classification
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
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This paper describes a new approach to adaptive estimation of parametric deformable contours based on B-spline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region-based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion, the result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequate and good performance of the approach