A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for clustering data
Algorithms for clustering data
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersnakes: Energy-Driven Watershed Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
IRGS: Image Segmentation Using Edge Penalties and Region Growing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
EdgeFlow: a technique for boundary detection and image segmentation
IEEE Transactions on Image Processing
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This paper proposes an algorithm that fuses edge information into Markov Random Fields (MRF) region growing based image segmentation. The idea is to segment the image in a way that takes edge information into consideration. This is achieved by modifying the energy function minimization process so that it would penalize merging regions that have real edges in the boundary between them. Experimental results confirming the hypothesis that the addition of edge information increases the precision of the segmentation by ensuring the conservation of the objects contours during the region growing.