Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
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
Machine vision
Nonlinear multiscale representations for image segmentation
Computer Vision and Image Understanding
Heuristic linking models in multiscale image segmentation
Computer Vision and Image Understanding
Energy Minimization of Contours Using Boundary Conditions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coarse-to-Fine Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Contours: Modeling and Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Snake Implementation; A Dual Active Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blurring Strategies for Image Segmentation Using a Multiscale Linking Model
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Extraction and Tracking of the Tongue Surface from Ultrasound Image Sequences
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Analysis of The Tongue Surface Movement Using A Spatiotemporally Coherent Deformable Model
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Comparison of Multiscale Representations for a Linking-Based Image Segmentation Model
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Computer-assisted endocardial border identification from a sequence of two-dimensional echocardiographic images
Spatiotemporal analysis of deformable contours
Spatiotemporal analysis of deformable contours
Optimal Polyline Tracking for Artery Motion Compensation in Coronary Angiography
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Topology preserving deformable image matching using constrained hierarchical parametric models
IEEE Transactions on Image Processing
An Information Fusion Framework for Robust Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Target positioning with dominant feature elements
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Manifold learning for object tracking with multiple motion dynamics
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Analytical dynamic programming tracker
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
An object tracking scheme based on local density
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Extraction of left ventricle borders with local and global priors from echocardiograms
Machine Vision and Applications
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This paper introduces a novel coarse-to-fine deformable contour optimization framework, which is composed of two main components. The first component uses scale-space and information theories to produce a coarser representation of the input image to be used in a coarse-to-fine optimization scheme. The employment of information theory ensures that maximal image information is propagated to the coarse images and employment of scale spaces provides a mechanism to change the image coarseness locally based on the deformable contour model definition. The second component of this framework uses a novel combination of dynamic programming and gradient descent methods to optimize the contour energy on coarser representations and then use the obtained coarse contour positions in finer optimizations. The motivation in using a combination of dynamic programming and gradient descent method is to take advantage of each method's efficiency and avoid their drawbacks. In order to verify the performance of this framework, we constructed a deformable contour model for the spatiotemporal tracking of closed contours and optimized the model energy under this framework. Experiments on this system performed using synthetic images and real world echocardiographic sequences demonstrated the effectiveness and practicality of this framework.