A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active vision
A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based strategies for active contour models
International Journal of Computer Vision
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel simulated annealing for shape detection
Computer Vision and Image Understanding
Camera calibration without feature extraction
Computer Vision and Image Understanding
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multispectral Random Field Models for Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Automatic Model Construction and Pose Estimation From Photographs Using Triangular Splines
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Game-Theoretic Integration for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive segmentation with Intelligent Scissors
Graphical Models and Image Processing
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Invariant-based recognition of complex curved 3D objects from image contours
Computer Vision and Image Understanding
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Color Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking deformable templates using a shortest path algorithm
Computer Vision and Image Understanding
Deformable Shape Detection and Description via Model-Based Region Grouping
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Edge, Junction, and Corner Detection Using Color Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric Distributional Clustering for Image Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Tracking with the EM Contour Algorithm
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Image Segmentation Based on the Integration of Pixel Affinity and Deformable Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
3D pose estimation by fitting image gradients directly to polyhedral models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Bayesian Fusion of Color and Texture Segmentations
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
"Ratio Regions": A Technique for Image Segmentation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Subpixel determination of imperfect circles characteristics
Pattern Recognition
Contour tracking based on marginalized likelihood ratios
Image and Vision Computing
Real-time 3D face tracking with mutual information and active contours
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
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The task of fitting parametric curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as image segmentation, pose estimation, object tracking, and 3-D reconstruction. In this article, we propose the Contracting Curve Density (CCD) algorithm as a solution to the curve-fitting problem.The CCD algorithm extends the state-of-the-art in two important ways. First, it applies a novel likelihood function for the assessment of a fit between the curve model and the image data. This likelihood function can cope with highly inhomogeneous image regions, because it is formulated in terms of local image statistics. The local image statistics are learned on the fly from the vicinity of the expected curve. They provide therefore locally adapted criteria for separating the adjacent image regions. These local criteria replace often used predefined fixed criteria that rely on homogeneous image regions or specific edge properties. The second contribution is the use of blurred curve models as efficient means for iteratively optimizing the posterior density over possible model parameters. These blurred curve models enable the algorithm to trade-off two conflicting objectives, namely heaving a large area of convergence and achieving high accuracy.We apply the CCD algorithm to several challenging image segmentation and 3-D pose estimation problems. Our experiments with RGB images show that the CCD algorithm achieves a high level of robustness and sub-pixel accuracy even in the presence of severe texture, shading, clutter, partial occlusion, and strong changes of illumination.