A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized gradient vector flow external forces for active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Contour Extraction Using Hierarchical Shape Representation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Detection and recognition of contour parts based on shape similarity
Pattern Recognition
Representation and matching of articulated shapes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
RAGS: region-aided geometric snake
IEEE Transactions on Image Processing
A particle filter framework for contour detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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This paper describes a novel approach to extract object region from an image by tracking the enclosing contour. We assume that the image is not complex, and it can be roughly partitioned into two parts with an intensity threshold. A lot of images (for example medical images) are in accord with this assumption. Global constraint (threshold) and local constraint (gradient) are integrated in a particle filter framework. We utilize the filter to track the optimal contour path pixel by pixel. The processing time depends only on the contour length and the number of particles used. Thus the proposed method is significantly faster than the very popular and time consuming method: Active Contour Models ("Snakes"). Both Snakes and our method are targeted for similar applications. Experimental results illustrate the validity and advantages of our method.