Recovering Occlusion Boundaries from an Image

By Derek Hoiem, Alexei A. Efros, Martial Hebert. 2011

197 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

On the Binding Problem in Artificial Neural Networks (2020, methodology): “, 2017), to learn boundary detection (Martin et al., 2004; Hoiem et al., 2011), or as a means of top-down feedback (Mobahi et al.”
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation (2015, background): “MCG is efﬁcient, its good generalization power makes it parameter free in practice, and it provides a ranked set of proposals that are competitive in all regimes of number of proposals.”
Instance Segmentation of Indoor Scenes Using a Coverage Loss (2014, methodology): “During learning, we will seek to maximize the Coverage Score [10], a measure of how similar two segmentations are, between our inferred semantic/instance segments and those produced by human annotators.”
Category Independent Object Proposals (2010, methodology): “, [16, 4, 17]) or multiple overlapping segmentations (e.”
Unfolding an Indoor Origami World (2014, background): “Other work in single-image layout prediction has drawn inspiration from classic line-labeling approaches, but has focused on the task of finding occlusion boundaries [13,15,24].”
Image Segmentation by Cascaded Region Agglomeration (2013, background): “Each stage employs a probabilistic model adapted to the scale at which it operates.”
Indoor Scene Understanding with RGB-D Images: Bottom-up Segmentation, Object Detection and Semantic Segmentation (2015, methodology): “We formulate contour detection as a binary pixel classification problem where the goal is to separate contour from non-contour pixels, an approach commonly adopted in the literature [3,23,37].”
Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation (2015, background): “Category Independent Object Proposal samples foreground seeds from occlusion boundaries inferred by [97].”
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation (2017, background): “Endres and Hoiem [17] base their category-independent object proposals on an iterative generation of a hierarchy of regions, based on the contour detector of [18] and occlusion boundaries of [26].”
Semantic contours from inverse detectors (2011, background): “For example, grouping edge pixels based on mid-level Gestalt cues, e.g. [18] [23] [12], or recovering occlusion boundaries from a single image [11], or from a sequence [21].”
Multiscale Combinatorial Grouping (2014, background): “Endres and Hoiem [10] base their category-independent object proposals on an iterative generation of a hierarchy of regions, based on the contour detector of [4] and occlusion boundaries of [14].”
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images (2013, methodology): “We formulate contour detection as a binary pixel classification problem where the goal is to separate contour from non-contour pixels, an approach commonly adopted in the literature [22, 12, 2].”
Towards unified depth and semantic prediction from a single image (2015, methodology): “Several methods try to getter better semantic segmentation results given RGB-D data [28] [31] [12] [13], while others take the predicted semantic labels to estimate depth [23] [15].”