On active contour models and balloons
CVGIP: Image Understanding
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Global Minimum for Active Contour Models: A Minimal Path Approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Learning parameter tuning for object extraction
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
IEEE Transactions on Image Processing
Level Learning Set: A Novel Classifier Based on Active Contour Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
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This paper proposes a novel active contour model for image object recognition using neural networks as a dynamic information fusion kernel. It first learns feature fusion strategies from training data by searching for an optimal fusion model at each marching step of the active contour model. A recurrent neural network is then employed to learn the fusion strategy knowledge. The learned knowledge is then applied to guide another linear neural network to fuse the features, which determine the marching procedures of an active contour model for object recognition. We test our model on both artificial and real image data sets and compare the results to those of a standard active model, with promising outcomes.