Introduction to algorithms
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
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
International Journal of Computer Vision
User-steered image segmentation paradigms: live wire and live lane
Graphical Models and Image Processing
Interactive segmentation with Intelligent Scissors
Graphical Models and Image Processing
Globally Optimal Regions and Boundaries as Minimum Ratio Weight Cycles
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Bayesian Object Localisation in Images
International Journal of Computer Vision
Digital Picture Processing
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
Real-Time Interactive Path Extraction with on-the-Fly Adaptation of the External Forces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Bayesian Network Framework for Real-Time Object Selection
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
Intrinsic Bayesian Active Contours for Extraction of Object Boundaries in Images
International Journal of Computer Vision
A Unifying View of Contour Length Bias Correction
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
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Bayesian boundary models often assume that the evidence for each contour is derived from the entire image. Consequently, the normalization term in the Bayes rule is the same for every contour and becomes irrelevant when seeking the optimal. However, in practice these models only use the vicinity of a contour, making the normalization term contour-specific. We propose a formulation that acknowledges the normalization term and includes it in the optimization. We show that it can be interpreted as a confidence measure promoting contours which are far better than other nearby candidate contours. We validate our approach in an interactive boundary delineation setting and demonstrate that complex boundaries can be extracted with significantly smaller amount of user input than when traditional Bayesian models are employed.