Fundamentals of digital image processing
Fundamentals of digital image processing
Digital topology: introduction and survey
Computer Vision, Graphics, and Image Processing
ECCV 90 Proceedings of the first european conference on Computer vision
A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
3D imaging in medicine
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of 3-D Simple Points for Topology Preserving Transformations with Application to Thinning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional imaging
Active shape models—their training and application
Computer Vision and Image Understanding
The NURBS book
Statistical snakes: active region models
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
A fast, simple active contour algorithm for biomedical images
Pattern Recognition Letters
International Journal of Computer Vision
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum segmented image information thresholding
Graphical Models and Image Processing
User-steered image segmentation paradigms: live wire and live lane
Graphical Models and Image Processing
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Optical Flow Constraints on Deformable Models with Applications to Face Tracking
International Journal of Computer Vision
Optimum Image Thresholding via Class Uncertainty and Region Homogeneity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Digital Picture Processing
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
A Robust Snake Implementation; A Dual Active Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Medical Image Segmentation Using Topologically Adaptable Snakes
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Topologically adaptable snakes
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Lip Tracking for MPEG-4 Facial Animation
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Cortical Surface Reconstruction Using a Topology Preserving Geometric Deformable Model
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
RAGS: region-aided geometric snake
IEEE Transactions on Image Processing
Robust real-time segmentation of images and videos using a smooth-spline snake-based algorithm
IEEE Transactions on Image Processing
Active contour model with gradient directional information: directional snake
IEEE Transactions on Circuits and Systems for Video Technology
Graph search with appearance and shape information for 3-D prostate and bladder segmentation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Tensor scale: An analytic approach with efficient computation and applications
Computer Vision and Image Understanding
| Hi-index | 0.01 |
Object segmentation is of paramount interest in many imaging applications, especially, those involving numeric, symbolic, syntactic, or even high level cognitive knowledge perception. Among others, ''snake''-an ''active contour'' model-is a popular boundary-based segmentation approach where a smooth curve is continuously deformed to lock onto an object boundary. The dynamics of a snake is governed by different internal and external forces. A major limitation of the present framework has been the difficulty of incorporating object-intensity driven features into snake dynamics so as to prevent uncontrolled expansion/contraction once the snake leaks through a weak boundary region. In this paper, a local-intensity-driven ''adaptive force'' is introduced into the model using object class-uncertainty theory. Given a priori knowledge of object/background intensity distributions, class-uncertainty theory yields object/background classification of every location and establishes its confidence level. It has been demonstrated earlier that confidence level is high inside homogeneous regions and low near boundaries. In the current paper, object class-uncertainty theory has been applied to control snake deformation leading to a new adaptive force acting outward (expanding) inside intensity-defined object regions and inward (squeezing) inside background regions. It has been demonstrated that the method possesses potential to resist uncontrolled expansion of a snake contour (for an expanding type) inside background after leaking through a weak boundary. Further, it has been shown that the adaptive force operates in a complementary fashion with the image intensity gradient by reducing its strength near boundaries using the confidence level of classification. Another major contribution of this paper is the formulation of a ''hybrid snake'' (HS)-a new model, where an initial contour is gradually deformed over a hybrid energy surface composed of some direct energies (e.g., internal energies) and other indirect energies contributed by local contour displacements over a force-field (e.g., image or user-constrained force-field). Applications of the proposed adaptive force-enabled HS on different phantom and real images have been presented and comparisons have been made with a conventional snake (CS). Finally, a quantitative comparison based on computer-generated phantoms at various levels of blur and noise has been provided.