learning-based contour detection

under construction...

Random Decision Forest-based Contour Detection

}Edges: Significant local changes in image; occur on the boundary btw 2 different regions in an image.

}Contour: Representation of linked edges for a region boundary.

◦Closed: Correspond to region boundaries; filling algorithm determines the pixels in the region.

◦Open: part of a region boundary; gaps’ formation due to high edge-detection threshold or weak contrast.

◦occur when line fragments are linked together, as in drawing or handwriting.

}Contour Representation:

◦Ordered list of Edges (chains codes)

◦Curve- model for a contour (piecewise line segments or cubic splines)

}Local edge detection

◦Problems - false targets, misses

}One solution: use other cues (image segmentation)

◦Texture: Sharp changes in orientation, scale of textures

◦Motion: >=2 Frames

◦Disparity: Stereo

}Regional Approaches (split-merge, watershed, mean shift, ...)

– ◦Use regional info, optimize labelling of regional tokens, e.g. clustering

– ◦Depending on uniformity in object region

}Active Contour Models (snakes)

◦Use regional (external) & boundary (internal) info, optimize deformation of model

◦Sensitivity to initialization, too smooth

}Level Set (implicit active contour)

◦–handle topological changes naturally

◦–not robust to boundary gaps

}Contour Grouping

–◦–Use boundary info (& regional info), optimize grouping of contour fragments

}Learning-based: Boundary Detection.

}How is Grouping Done in Human Vision?

◦Proximity

◦Similarity

◦Brightness

◦Contrast

◦Good continuation

◦Parallelism

◦Co-circularity

}Sketch Tokens(like "Shapeme") for Contour Detection with Random Forrest

◦Definition: straight lines, t-junctions, y-junctions, corners, curves, parallel lines;

◦Learned (k-means clustering) from patches of human generated contours: a number of classes in hundreds, Daisy (MSR) descriptors used for shift invariance;

◦Low-level image features: gradient, color, orientation, etc.;

◦Classifier: Random decision forest for sketch token labeling from image patches.

References

}X. Ren, and J. Malik. "Learning a Classification Model for Segmentation", ICCV’03

}D. Martin, C. Fowlkes, and J. Malik. "Learning to detect natural Image boundaries using local brightness, color, and texture cues", IEEE T-PAMI 2004

}P. Doll´ar, Z. Tu, and S. Belongie, “Supervised learning of edges and object boundaries”, CVPR, 2005

}Ren, Fowlkes, Malik. "Figure/Ground assignment in natural images“, ECCV 2006

}Mairal1, M. Leordeanu, F. Bach1, M. Hebert, J. Ponce, “Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation”, ECCV’08.

}I. Kokkinos, “Highly Accurate Boundary Detection and Grouping”. CVPR 2010.

}X. Ren and L. Bo, “Discriminatively Trained Sparse Code Gradients for Contour Detection”, NIPS’12.

}J Lim, C. L. Zitnick, P Dollar, “Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection”, CVPR, 2013