Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
An Active Contour Model without Edges
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Front Propagation and Level-Set Approach for Geodesic Active Stereovision
VS '98 Proceedings of the 1998 IEEE Workshop on Visual Surveillance
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Topological control of level set method depending on topology constraints
Pattern Recognition Letters
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
An Algorithm for Parking Lot Occupation Detection
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
Active Contours without Edges and with Simple Shape Priors
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Stereo Matching with Mumford-Shah Regularization and Occlusion Handling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational principles, surface evolution, PDEs, level set methods, and the stereo problem
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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In this paper, a new algorithm for bimodal depth segmentation is presented. The method separates the background and the planar objects of arbitrary shapes lying in a certain height above the background using the information from the stereo image pair (more exactly, the background and the objects may lie on two distinct general planes). The problem is solved as a problem of minimising a functional. A new functional is proposed for this purpose that is based on evaluating the mismatches between the images, which contrasts with the usual approaches that evaluate the matches. We explain the motivation for such an approach. The minimisation is carried out by making use of the Euler-Lagrange equation and the level-set function. The experiments show the promising results on noisy synthetic images as well as on real-life images. An example of the practical application of the method is also presented.