Image Segmentation and Selective Smoothing Based on Variational Framework
Journal of Signal Processing Systems
External Force for Active Contours: Gradient Vector Convolution
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Left Ventricle Segmentation Using Model Fitting and Active Surfaces
Journal of Signal Processing Systems
Arranging and interpolating sparse unorganized feature points with geodesic circular arc
IEEE Transactions on Image Processing
An Integral Active Contour Model for Convex Hull and Boundary Extraction
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Poisson inverse gradient approach to vascular myocyte detection and segmentation
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Tracking multiple objects using moving snakes
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Object segmentation by traversing a pose-shape manifold
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Efficient numerical schemes for gradient vector flow
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Coarse-to-fine boundary location with a SOM-like method
IEEE Transactions on Neural Networks
A new adaptive B-spline VFC snake for object contour extraction
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
Pattern Recognition Letters
Brain volumetry: An active contour model-based segmentation followed by SVM-based classification
Computers in Biology and Medicine
Efficient numerical schemes for gradient vector flow
Pattern Recognition
Convolutional virtual electric field external force for active contours
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Harris function based active contour external force for image segmentation
Pattern Recognition Letters
An intelligent tool for anatomical object segmentation using deformable surfaces
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
ITIB'12 Proceedings of the Third international conference on Information Technologies in Biomedicine
Fast gradient vector flow computation based on augmented Lagrangian method
Pattern Recognition Letters
A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces
Computer Methods and Programs in Biomedicine
Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model
Computer Vision and Image Understanding
Adaptive diffusion flow active contours for image segmentation
Computer Vision and Image Understanding
Extended Topological Active Nets
Image and Vision Computing
Journal of Visual Communication and Image Representation
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Snakes, or active contours, have been widely used in image processing applications. Typical roadblocks to consistent performance include limited capture range, noise sensitivity, and poor convergence to concavities. This paper proposes a new external force for active contours, called vector field convolution (VFC), to address these problems. VFC is calculated by convolving the edge map generated from the image with the user-defined vector field kernel. We propose two structures for the magnitude function of the vector field kernel, and we provide an analytical method to estimate the parameter of the magnitude function. Mixed VFC is introduced to alleviate the possible leakage problem caused by choosing inappropriate parameters. We also demonstrate that the standard external force and the gradient vector flow (GVF) external force are special cases of VFC in certain scenarios. Examples and comparisons with GVF are presented in this paper to show the advantages of this innovation, including superior noise robustness, reduced computational cost, and the flexibility of tailoring the force field.