Shift-map image editing

By Yael Pritch, Eitam Kav-Venaki, Shmuel Peleg. IEEE International Conference on Computer Vision, 2009

451 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/38fd97e5bd9ae77ee6d9245fad88a18385539691

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In Computer Vision and Pattern Recognition

Citations in the corpus

Context-aware saliency detection (2010, background): “The first is retargeting[1, 19, 16], where we show that our saliency can successfully mark the regions that should be kept untouched.”
Shift-Net: Image Inpainting via Deep Feature Rearrangement (2018, methodology): “Global image coherence has also been introduced to the Markov random ﬁeld (MRF) framework for improving visual quality [22, 32, 43].”
Synthesizing structured image hybrids (2010, background): “These allow a user to combine content from multiple source images [Agarwala et al. 2004] or to rearrange content within a given image [Cho et al. 2008; Pritch et al. 2009], possibly at interactive rates [Barnes et al. 2009].”
Statistics of Patch Offsets for Image Completion (2012, methodology): “way of Shift-map [15] (except that [15] has an extra gradient smoothness term).”
Image Inpainting : Overview and Recent Advances (2014, methodology): “The shift-map method [37] indeed treats inpainting as a global optimization on the entire image.”
Scale and object aware image retargeting for thumbnail browsing (2011, background): “[17] released a benchmark dataset, RetargetMe, together with the results from many state-of-the-art image retargeting methods [10, 16, 18, 19, 22, 24].”
Video Inpainting of Complex Scenes (2014, unknown): “C V ] 1 8 M ar 2 01 5”
Context-Aware Semantic Inpainting (2017, background): “Many algorithms on recovering holes in images or videos have been proposed [8], [9], [10], [11], [12], [13], [14], [15].”
Distributed message passing for large scale graphical models (2011, background): “The literature contains many impressive heuristic approximate inference algorithms that obtain reasonable solutions fast, but do have neither convergence nor optimality guarantees [2, 21].”
Transformation guided image completion (2013, methodology): “Most automatic techniques (such as [1, 7, 6, 22, 14, 8]) are able to handle a specific set of structural conditions, but mostly fine to mid-level textures.”
3D Photography Using Context-Aware Layered Depth Inpainting (2020, methodology): “…synthesis [9, 8], example-based methods complete the missing regions by transferring the contents from the known regions of the image, either through non-parametric patch-based synthesis [63, 1, 5, 20] or solving a Markov Random Field model using belief propagation [26] or graph cut [48, 28, 16].”
“Double-DIP”: Unsupervised Image Decomposition via Coupled Deep-Image-Priors (2018, background): “This strong internal patch recurrence was exploited for solving a large variety of computer vision tasks [9, 13, 16, 15, 19, 36, 29, 7, 11].”
Flow-edge Guided Video Completion (2020, methodology): “These methods find correspondences for content transfer either via patch-based synthesis [1, 39] or by solving a labeling problem with graph cuts [12, 32].”
Image completion using planar structure guidance (2014, methodology): “2009], or MRF models with patches as labels, which can be solved efficiently using belief propagation [Komodakis and Tziritas 2007] or graph cut [Pritch et al. 2009].”
A comparative study of image retargeting (2010, unknown): “…Seam-Carving ( SC ) [Rubinstein et al. 2008], Scale-and-Stretch ( SNS ) [Wang et al. 2008], Multi-operator ( MULTIOP ) [Ru-binstein et al. 2009], Shift-maps ( SM ) [Pritch et al. 2009], Streaming Video ( SV ) [Kr¨ahenb¨uhl et al. 2009], and Energy-based deformation ( LG ) [Karni et al. 2009] 1 .”
Learning to Incorporate Structure Knowledge for Image Inpainting (2020, background): “Barnes et al. 2009; Pritch, Kav-Venaki, and Peleg 2009; He and Sun 2012).”