Computer Vision and Image Understanding
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Mumford-Shah model on lattice
Image and Vision Computing
Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in N-D images
Computer Vision and Image Understanding
Detection and Tracking of Coronal Mass Ejections
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Track and cut: simultaneous tracking and segmentation of multiple objects with graph cuts
Journal on Image and Video Processing - Video Tracking in Complex Scenes for Surveillance Applications
Deformable Surface Augmentation in Spite of Self-Occlusions
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
A local-motion-based probabilistic model for visual tracking
Pattern Recognition
Narrow band region-based active contours and surfaces for 2D and 3D segmentation
Computer Vision and Image Understanding
Stabilization of parametric active contours using a tangential redistribution term
IEEE Transactions on Image Processing
Deconvolving Active Contours for Fluorescence Microscopy Images
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Active contours driven by local image fitting energy
Pattern Recognition
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
Detection and tracking of coronal mass ejections based on supervised segmentation and level set
Pattern Recognition Letters
Markov random field modeled level sets method for object tracking with moving cameras
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Cell population tracking and lineage construction with spatiotemporal context
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Discrete curvature calculation for fast level set segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Deform PF-MT: particle filter with mode tracker for tracking nonaffine contour deformations
IEEE Transactions on Image Processing
The segmentation of the body of tongue based on the improved level set in TCM
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Object tracking using parallel local colour histogram method
International Journal of Computational Vision and Robotics
Spatial color histogram based center voting method for subsequent object tracking and segmentation
Image and Vision Computing
Multiregion level set tracking with transformation invariant shape priors
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
An improved real-time contour tracking algorithm using fast level set method
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Semantic video content annotation at the object level
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
Level set evolution with locally linear classification for image segmentation
Pattern Recognition
Integrating tracking with fine object segmentation
Image and Vision Computing
Detecting, segmenting and tracking unknown objects using multi-label MRF inference
Computer Vision and Image Understanding
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In this paper we propose a novel implementation of the level set method that achieves real-time level-set-based video tracking. In our fast algorithm, the evolution of the curve is realized by simple operations such as switching elements between two linked lists and there is no need to solve any partial differential equations. Furthermore, a novel procedure based on Gaussian filtering is introduced to incorporate boundary smoothness regularization. By replacing the standard curve length penalty with this new smoothing procedure, further speedups are obtained. Another advantage of our fast algorithm is that the topology of the curves can be controlled easily. For the tracking of multiple objects, we extend our fast algorithm to maintain the desired topology for multiple object boundaries based on ideas from discrete topology. With our fast algorithm, a real-time system has been implemented on a standard PC and only a small fraction of the CPU power is used for tracking. Results from standard test sequences and our real-time system are presented.