Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active vision
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiotemporal Segmentation Based on Region Merging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized gradient vector flow external forces for active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Gradient vector flow deformable models
Handbook of medical imaging
Tracking Deformable Objects in the Plane Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Segmentation Methods Based on Partial Differential Equations and the Watershed Transformation
Mustererkennung 1998, 20. DAGM-Symposium
A Common Framework for Curve Evolution, Segmentation and Anisotropic Diffusion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Real-Time Algorithm for Medical Shape Recovery
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A class of constrained clustering algorithms for object boundary extraction
IEEE Transactions on Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
Deformable model with a complexity independent from image resolution
Computer Vision and Image Understanding
Multi-component heart reconstruction from volumetric imaging
Proceedings of the 2008 ACM symposium on Solid and physical modeling
Morphological multiscale decomposition of connected regions with emphasis on cell clusters
Computer Vision and Image Understanding
Deformable model with a complexity independent from image resolution
Computer Vision and Image Understanding
Geometric attraction-driven flow for image segmentation and boundary detection
Journal of Visual Communication and Image Representation
Automatic skeleton extraction and splitting of target objects
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Seeded region merging based on gradient vector flow for image segmentation
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Human action recognition based on skeleton splitting
Expert Systems with Applications: An International Journal
Adaptive diffusion flow active contours for image segmentation
Computer Vision and Image Understanding
| Hi-index | 0.01 |
In this paper, we present an approach for image segmentation, based on the existing Active Snake Model and Watershed-based Region Merging. Our algorithm includes initial segmentation using Normalized Gradient Vector Diffusion (NGVD) and region merging based on Region Adjacency Graph (RAG). We use a set of heat diffusion equations to generate a vector field over the image domain, which provides us with a natural way to define seeds as well as an external force to attract the active snakes. Then an initial segmentation of the original image can be obtained by a similar idea as seen in active snake model. Finally an RAG-based region merging technique is used to find the true segmentation as desired. The experimental results show that our NGVD-based region merging algorithm overcomes some problems as seen in classic active snake model. We will also see that our NGVD has several advantages over the traditional gradient vector diffusion.