Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Object boundary detection in images using a semantic ontology
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Benchmarking Image Segmentation Algorithms
International Journal of Computer Vision
Robust Segmentation by Cutting across a Stack of Gamma Transformed Images
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Edge-Preserving Laplacian Pyramid
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Segmentation subject to stitching constraints: finding many small structures in a large image
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Segmentation of Natural Images by Texture and Boundary Compression
International Journal of Computer Vision
Natural image segmentation with adaptive texture and boundary encoding
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
SLEDGE: Sequential Labeling of Image Edges for Boundary Detection
International Journal of Computer Vision
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Perceptual organization is scale-invariant. In turn, a segmentation that separates features consistently at all scales is the desired one that reveals the underlying structural organization of an image. Addressing cross-scale correspondence with interior pixels, we develop this intuition into a general segmenter that handles texture and illusory contours through edges entirely without any explicit characterization of texture or curvilinearity. Experimental results demonstrate that our method not only performs on par with either texture segmentation or boundary completion methods on their specialized examples, but also works well on a variety of real images.