Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
Sensor-Based Pedestrian Protection
IEEE Intelligent Systems
International Journal of Computer Vision
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Multi-Feature Hierarchical Template Matching Using Distance Transforms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Natural Image Statistics for Natural Image Segmentation
International Journal of Computer Vision
Towards recognition-based variational segmentation using shape priors and dynamic labeling
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Shape matching by variational computation of geodesics on a manifold
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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We present an integrated two-level approach to computationally analyzing image sequences of static scenes by variational segmentation. At the top level, estimated models of object appearance and background are probabilistically fused to obtain an a-posteriori probability for the occupancy of each pixel. The data-association strategy handles object occlusions explicitly. At the lower level, object models are inferred by variational segmentation based on image data and statistical shape priors. The use of shape priors allows to distinguish between recognition of known objects and segmentation of unknown objects. The object models are sufficiently flexible to enable the integration of general cues like advanced shape distances. At the same time, they are highly constrained from the optimization viewpoint: the globally optimal parameters can be computed at each time instant by dynamic programming. The novelty of our approach is the integration of state-of-the-art variational segmentation into a probabilistic framework for static scene analysis that combines both on-line learning and prior knowledge of various aspects of object appearance.