Subpixel Measurements Using a Moment-Based Edge Operator
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A level set approach for computing solutions to incompressible two-phase flow
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Game-Theoretic Integration for Image Segmentation
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An Operator Which Locates Edges in Digitized Pictures
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A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
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Deformable Contour Method: A Constrained Optimization Approach
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Level Set Evolution without Re-Initialization: A New Variational Formulation
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Fast Global Minimization of the Active Contour/Snake Model
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On convergence properties of the em algorithm for gaussian mixtures
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The piecewise smooth Mumford-Shah functional on an arbitrary graph
IEEE Transactions on Image Processing
On the statistical interpretation of the piecewise smooth Mumford-Shah functional
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Efficient segmentation of piecewise smooth images
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Integrated active contours for texture segmentation
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
A spatially constrained mixture model for image segmentation
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Small object detection in cluttered image using a correlation based active contour model
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This paper presents a general object boundary extraction model for piecewise smooth images, which incorporates local intensity distribution information into an edge-based implicit active contour. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different region-based operators: a Gaussian mixture model (GMM)-based intensity distribution estimator and the Hueckel operator. We propose the local distribution fitting model for more accurate segmentation, which incorporates the operator outcomes into the recent local binary fitting (LBF) model. The GMM and the Hueckel model parameters are estimated before contour evolution, which enables the use of the proposed model without the need for initial contour selection, i.e., the level set function is initialized with a random constant instead of a distance map. Thus our model essentially alleviates the initialization sensitivity problem of most active contours. Experiments on synthetic and real images show the improved performance of our approach over the LBF model.