A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed algorithms for the computation of noncooperative equilibria
Automatica (Journal of IFAC)
Visual reconstruction
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Segmentation of Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Region Growing and Edge Detection
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
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A common framework for image segmentation
International Journal of Computer Vision
Image selective smoothing and edge detection by nonlinear diffusion. II
SIAM Journal on Numerical Analysis
On Achievable Accuracy in Edge Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modular system for image analysis using a game-theoretic framework
Image and Vision Computing - Special issue: information processing in medical imaging 1991
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
International Journal of Computer Vision
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Digital Picture Processing
Computer Vision
Image Segmentation by Unifying Region and Boundary Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Integration of Image Segmentation Maps using Region and Edge Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Game-Theoretic Approach to Integration of Modules
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topologically adaptable snakes
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Image segmentation by reaction-diffusion bubbles
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
International Journal of Computer Vision
International Journal of Computer Vision
Adaptive Image Segmentation by Combining Photometric Invariant Region and Edge Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
International Journal of Computer Vision
International Journal of Computer Vision
Level Lines as Global Minimizers of Energy Functionals in Image Segmentation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Automatic Hybrid Segmentation of Dual Contrast Cardiac MR Data
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Model-Based Image Segmentation Using Local Self-Adapting Separation Criteria
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Estimation of 3D Surface Shape and Smooth Radiance from 2D Images: A Level Set Approach
Journal of Scientific Computing
Deformable Contour Method: A Constrained Optimization Approach
International Journal of Computer Vision
International Journal of Computer Vision
Globally adaptive region information for automatic color-texture image segmentation
Pattern Recognition Letters
Review: A comparative study of deformable contour methods on medical image segmentation
Image and Vision Computing
International Journal of Intelligent Systems Technologies and Applications
Effective Filtration Techniques for Gallbladder Ultrasound Images with Variable Contrast
Journal of Signal Processing Systems
MAPS: multiscale attention-based presegmentation of color images
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Integrating local distribution information with level set for boundary extraction
Journal of Visual Communication and Image Representation
Structure from motion for scenes without features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Shape context for image understanding
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
A review on automatic image annotation techniques
Pattern Recognition
Recognition tasks are imitation games
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
A bayesian approach for weighting boundary and region information for segmentation
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Facial expression recognition using game theory
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Facial expression recognition using game theory and particle swarm optimization
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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Robust segmentation of structures from an image is essential for a variety of image analysis problems. However, the conventional methods of region-based segmentation and gradient-based boundary finding are often frustrated by poor image quality. Here we propose a method to integrate the two approaches using game theory in an effort to form a unified approach that is robust to noise and poor initialization. This combines the perceptual notions of complete boundary information using edge data and shape priors with gray-level homogeneity using two computational modules. The novelty of the method is that this is a bidirectional framework, whereby both computational modules improve their results through mutual information sharing. A number of experiments were performed both on synthetic datasets and datasets of real images to evaluate the new approach and it is shown that the integrated method typically performs better than conventional gradient-based boundary finding.