Object detection by global contour shape
Pattern Recognition
Appearance contrast for fast, robust trail-following
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Bayesian inference for layer representation with mixed Markov random field
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Contour grouping and abstraction using simple part models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Spatiotemporal contour grouping using abstract part models
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Occlusion cues for image scene layering
Computer Vision and Image Understanding
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A method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multiscale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework, and object segmentation and recognition are accomplished simultaneously by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. Within each sampling move during the simulation process, probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized cluster sampling algorithm [1], combined with a large sampling jump and other implementation improvements, greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images.