Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge contours using multiple scales
Computer Vision, Graphics, and Image Processing
Performance characterization of image understanding algorithms
Performance characterization of image understanding algorithms
“Brownian strings”: segmenting images with stochastically deformable contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Contours: Modeling and Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Liver Definition in CT Using a Population-Based Shape Model
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Sectored Snakes: Evaluating Learned-Energy Segmentations
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Strings: Variational Deformable Models of Multivariate Continuous Boundary Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object tracking using mean shift and active contours
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Facial boundary detection with an active contour model
Pattern Recognition Letters
Locating object contours in complex background using improved snakes
Computer Vision and Image Understanding
Review: A comparative study of deformable contour methods on medical image segmentation
Image and Vision Computing
Continuous force field analysis for generalized gradient vector flow field
Pattern Recognition
Automated snake initialization for the segmentation of the prostate in ultrasound images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Pattern Recognition and Image Analysis
Journal of Visual Communication and Image Representation
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We describe how to teach deformable models to maximize image segmentation correctness based on user-specified criteria, and we present a method for evaluating which criteria work best. A traditional deformable model (“snake” in 2D) fails to find an object's boundary when the strongest nearby image edges are not the ones sought. But models can be trained to respond to other image features instead, by learning their probability distributions. The implementor must then decide on which of many image qualities to teach the model. To this end, we show how to evaluate the efficacy of any resulting deformable model, given a sampling of ground truth, a model of the range of shapes tried during optimization, and a measure of shape closeness. In the domain of abdominal CT images, we demonstrate such evaluation on a simple “sectoring” of a snake in which intensity and perpendicular gradient are observed over equal-length segments. This specific set of qualities shows a measured improvement over an objective function that is uniform around the shape, and it follows naturally from examination of the latter's failures due to image variations around the organ boundary.