Machine Vision and Applications
Region-based strategies for active contour models
International Journal of Computer Vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and topology constraints on parametric active contours
Computer Vision and Image Understanding
Subjective Surfaces: A Geometric Model for Boundary Completion
International Journal of Computer Vision
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized Laplacian Zero Crossings as Optimal Edge Integrators
International Journal of Computer Vision
A Robust Active Contour Model for Natural Scene Contour Extraction with Automatic Thresholding
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
Generalized Gradients: Priors on Minimization Flows
International Journal of Computer Vision
MAC: Magnetostatic Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distraction Free Evolution of Active Contours
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Hi-index | 0.00 |
Active contours are a popular class of variational models used in computer vision for tracking and segmentation. The variational model consists of a data-fitting and a regularisation term. Depending on the data-fitting term, active contour models are classified as either gradient or region based models. An often overlooked but crucial aspect of these models is that these two terms are weighted by a manually set constant weight. This constant weight often leads to incorrect segmentation, particularly for gradient based energies. This failure rate is high in the presence of strong gradients nearby the target or when the object gradient is not uniformly strong. In such circumstances, setting the weight becomes a critical and often unsatisfying task. In this work, we propose a new spatially varying and dynamic curve evolution term for robust segmentation of gradient based models. In contrast to the majority of the existing work in literature which focuses on defining new data-fitting terms, the evolution term proposed here is related to the regularisation of evolution. The intuition here is that in images although object boundaries are generally continuous, the magnitude of the gradient map so generated is not uniformly strong. Therefore, any energy formulation which fixes the weights of the data-fitting and regularisation term will run into the problems mentioned above. In this work, we propose an energy term which defines the regularisation term in a spatially varying manner. The advantage of this term is that it is independent of the image based data-fitting energy term and hence can be plugged into the vast variety of the existing gradient based active contour models.