Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Active shape models—their training and application
Computer Vision and Image Understanding
Wavelet-Based Affine Invariant Representation: A Tool for Recognizing Planar Objects in 3D Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov models vs. syntactic modeling in object recognition
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Nonstationary autoregressive modeling of object contours
IEEE Transactions on Signal Processing
Affine-invariant B-spline moments for curve matching
IEEE Transactions on Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
2D Affine-Invariant Contour Matching Using B-Spline Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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A close-form B-Snake model using statistics information for 2D objects segmentation is presented in this paper. We called it Dynamic B-Snake Model (DBM). It is able to model the features of the object in training set and guide the B-Snake in the deforming procedure. Compared to other deformable models, the DBM maintains the smoothness of curve while still remain compact representation. Moreover, a method of Minimum Mean Square Error (MMSE) is developed to iteratively estimate the position of those control points in the B-Snake model. As it deforms the segments of the B-Spline at a time, instead of individual points, it is very robust against local minima. Furthermore, in order to use available statistical information about the desired object shape, the Principal Component Analysis (PCA) is applied to model the distribution of knot points of training samples. This allows the deformation of B-Snake to synthesize the shape similar to those in the training set. By applying the proposed B-Snake model to medical images, it is shown that our method is more robust and accurate in comparing with the traditional Snake and Active Shape Model(ASM).