A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Game-Theoretic Integration for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Model-Based Image Segmentation Using Local Self-Adapting Separation Criteria
Proceedings of the 23rd DAGM-Symposium on 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
Model-Based Image Segmentation Using Local Self-Adapting Separation Criteria
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
| Hi-index | 0.00 |
In this paper we address the problem of model-based image segmentation by fitting deformable models to the image data. From uncertain a priori knowledge of the model parameters an initial probability distribution of the model edge in the image is obtained. From the vicinity of the surmised edge local statistics are learned for both sides of the edge. These local statistics provide locally adapted criteria to distinguish the two sides of the edge even in the presence of spatially changing properties such as texture, shading, or color. Based on the local statistics the model parameters are iteratively refined using a MAP estimation. Experiments with RGB images show that the method is capable of achieving high subpixel accuracy even in the presence of texture, shading, clutter, and partial occlusion.