Efficient affinity-based edit propagation using K-D tree

By Kun Xu, Yong Li, Tao Ju, Shimin Hu, Tian-Qiang Liu. 2009

166 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Rewriting a Deep Generative Model (2020, background): “Edit transfer methods propagate pixel edits to corresponding locations in other images of the same object or adjacent frames in the same video [2,77,28,14,13,81,21].”
Real-time user-guided image colorization with learned deep priors (2017, methodology): “To reduce user efforts, later methods focused on designing better similarity metrics (Luan et al. 2007; Qu et al. 2006) and utilizing long-range connections (An and Pellacini 2008; Xu et al. 2009).”
Let there be color! (2016, methodology): “…tonal editing, various afﬁnity-based methods have also been proposed, such as global optimization with all-pair constraints [An and Pel-lacini 2008; Xu et al. 2009], Radial Basis Function interpolation [Li et al. 2010], manifold learning [Chen et al. 2012], and stochastic modeling of appearance…”
Sparse Dictionary Learning for Edit Propagation of High-Resolution Images (2014, methodology): “We demonstrate through numerous experiments that our method significantly reduces memory cost while still maintaining high-fidelity editing results and avoiding the artifacts encountered by previous methods [17, 27].”
Light Field Image Processing: An Overview (2017, unknown): “This structure is the key difference with other methods targeting non-structured media, such as unstructured light fields [217], [218], video [209], [219], [220], [221] or image collections [222], [223], [224].”
Editing Conditional Radiance Fields (2021, background): “A closely related concept is edit propagation [3, 19, 23, 74, 77], which propagates sparse user edits on a single image to an entire photo collection or video.”