A new surface interpolation technique for reconstructing 3D objects from serial corss-sections
Computer Vision, Graphics, and Image Processing
On active contour models and balloons
CVGIP: Image Understanding
CVGIP: Graphical Models and Image Processing
Dynamic NURBS with geometric constraints for interactive sculpting
ACM Transactions on Graphics (TOG) - Special issue on interactive sculpting
Handbook of pattern recognition and image processing (vol. 2)
Simultaneous surface approximation and segmentation of complex objects
Computer Vision and Image Understanding
Active Visual Inference of Surface Shape
Active Visual Inference of Surface Shape
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
Tracking Points on Deformable Objects Using Curvature Information
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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Energy minimizing contours or snakes are tools for delineating objects of interest in an image. Snakes are defined discretely or in continuous form with continuous snakes having advantages over discrete snakes. A continuous snake called B-snake has been previously defined using B-spline curves. In this paper, a continuous snake called R-snake is introduced that is based on rational Gaussian (RaG) curves and has advantages over B-snakes. Not only the stiffness of an R-snake can be varied continuously to delineate an object from coarse to fine, the stiffness of different parts of an R-snake can be adjusted to recover a shape with smooth as well as detailed parts. The formulation of R-snakes is presented and experimental results delineating various objects in synthetic and real images are presented and discussed.