Bayesian optimization of the scale saliency filter
Image and Vision Computing
Exploiting Information Theory for Filtering the Kadir Scale-Saliency Detector
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Occlusion cues for image scene layering
Computer Vision and Image Understanding
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Graph matching and clustering using kernel attributes
Neurocomputing
Junction assisted 3D pose retrieval of untextured 3D models in monocular images
Computer Vision and Image Understanding
Information Sciences: an International Journal
Accurate Junction Detection and Characterization in Natural Images
International Journal of Computer Vision
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We propose two Bayesian methods for junction classification which evolve from the Kona method: a region-based method and an edge-based method. Our region-based method computes a one-dimensional (1-D) profile where wedges are mapped to intervals with homogeneous intensity. These intervals are found through a growing-and-merging algorithm driven by a greedy rule. On the other hand, our edge-based method computes a different profile which maps wedge limits to peaks of contrast, and these peaks are found through thresholding followed by nonmaximum suppression. Experimental results show that both methods are more robust and efficient than the Kona method, and also that the edge-based method outperforms the region-based one.