Generalized Boundaries from Multiple Image Interpretations

By Marius Leordeanu, Rahul Sukthankar, Cristian Sminchisescu. 2012

54 citations recorded by the source corpus.

Boundary detection is a fundamental computer vision problem that is essential for a variety of tasks, such as contour and region segmentation, symmetry detection and object recognition and categorization. We propose a generalized formulation for boundary detection, with closed-form solution, applicable to the localization of different types of boundaries, such as object edges in natural images and occlusion boundaries from video. Our generalized boundary detection method (Gb) simultaneously combines low-level and mid-level image representations in a single eigenvalue problem and solves for the optimal continuous boundary orientation and strength. The closed-form solution to boundary detection enables our algorithm to achieve state-of-the-art results at a significantly lower computational cost than current methods. We also propose two complementary novel components that can seamlessly be combined with Gb: first, we introduce a soft-segmentation procedure that provides region input layers to our boundary detection algorithm for a significant improvement in accuracy, at negligible computational cost; second, we present an efficient method for contour grouping and reasoning, which when applied as a final post-processing stage, further increases the boundary detection performance.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Fast Edge Detection Using Structured Forests (2014, background): “Unfortunately, many visually salient edges do not correspond to color gradients, such as texture edges [34] and illusory contours [39].”
Oriented edge forests for boundary detection (2014, background): “via textons [15], sparse coding [22], or measures of self-similarity [14].”
SemiContour: A Semi-supervised Learning Approach for Contour Detection (2016, unknown): “OIS AP Human .80 .80 - Canny [7] .60 .64 .58 Felz-Hutt [15] .61 .64 .56 Normalized Cuts [10] .64 .68 .48 Mean Shift [9] .64 .68 .56 Gb [19] .69 .72 .72 gPb-owt-ucm [2] .73 .76 .70 ISCRA [28] - (.72) - (.75) - (.46) Sketch Tokens [20] .64(.73) .66(.75) .58(.78) SCG [35] .73(.74) .75(.76) .76(.77) SE…”
Richer Convolutional Features for Edge Detection (2016, background): “Although edge detection approaches using low-level features have made great improvement in these years [33], their limitations are”