Contour Grouping with Partial Shape Similarity
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Shape Based Detection and Top-Down Delineation Using Image Segments
International Journal of Computer Vision
Patch Growing: Object segmentation using spatial coherence of local patches
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
POSIT: Part-based object segmentation without intensive training
Pattern Recognition
Patch Growing: Object segmentation using spatial coherence of local patches
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Appearance contrast for fast, robust trail-following
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
International Journal of Computer Vision
Bottom-up recognition and parsing of the human body
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
International Journal of Computer Vision
Multi-scale stacked sequential learning
Pattern Recognition
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
Recursive Compositional Models for Vision: Description and Review of Recent Work
Journal of Mathematical Imaging and Vision
An Efficient Approach to Semantic Segmentation
International Journal of Computer Vision
Inference Methods for CRFs with Co-occurrence Statistics
International Journal of Computer Vision
A boosting approach for the simultaneous detection and segmentation of generic objects
Pattern Recognition Letters
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We construct a Bayesian model that integrates topdown with bottom-up criteria, capitalizing on their relative merits to obtain figure-ground segmentation that is shape-specific and texture invariant. A hierarchy of bottom-up segments in multiple scales is used to construct a prior on all possible figure-ground segmentations of the image. This prior is used by our top-down part to query and detect object parts in the image using stored shape templates. The detected parts are integrated to produce a global approximation for the object's shape, which is then used by an inference algorithm to produce the final segmentation. Experiments with a large sample of horse and runner images demonstrate strong figure-ground segmentation despite high object and background variability. The segmentations are robust to changes in appearance since the matching component depends on shape criteria alone. The model may be useful for additional visual tasks requiring labeling, such as the segmentation of multiple scene objects.