Video object cut and paste

By Yin Li, Jian Sun, Harry Shum. ACM Transactions on Graphics, 2005

356 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0b0f1a779ba69db6fd85cb4ce26afa84cfc09b0e

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In Computer Vision and Pattern Recognition

Citations in the corpus

Click Carving: Segmenting Objects in Video with Point Clicks (2016, methodology): “It outperforms all similarly fast methods, and is competitive or better than those requiring 2 to 12 times the effort.”
CUDA cuts: Fast graph cuts on the GPU (2008, methodology): “Graph-cuts have since then been applied to several Computer Vision problems like image and video segmentation [29, 27], stereo and motion [5, 31], multicamera scene reconstruction [24, 20], etc.”
Graph Cuts and Efficient N-D Image Segmentation (2006, methodology): “…dynamic applications (Kohli and Torr, 2005; Juan and Boykov, 2006) (flow-and cut-recycling), extraction of moving or forground objects from video (Li et al., 2005; Wang et al., 2005), simultaneous segmentation of multiple objects (Li et al., 2006), combining segmentation with 3D pose estimation…”
Efficient hierarchical graph-based video segmentation (2010, background): “Interactive object segmentation has recently shown significant progress [1, 2, 9, 13, 16].”
TouchCut: Fast image and video segmentation using single-touch interaction (2014, unknown): “7.”
Beyond pixels: exploring new representations and applications for motion analysis (2009, unknown): “Interactive object segmentation in video sequences has recently been explored in computer graphics, including contour-based video rotoscoping [3], soft alpha matting [27], oversegmentation [122] and 3D graph cut [59].”
FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos (2017, background): “In recent years, video object segmentation has received significant attention, with great progress on fully automatic algorithms [15, 56, 13, 23, 30, 58, 24, 35, 10], propagation methods [40, 47, 11, 50, 19, 53], and interactive methods [51, 26, 2, 42].”
Human Instance Segmentation from Video using Detector-based Conditional Random Fields (2011, background): “Another prominent application area is video object-cutout and paste [2, 15], where human instances from one environment can be segmented, and pasted onto another environment.”
How Hierarchical Topics Evolve in Large Text Corpora (2014, methodology): “The first three metrics are inspired by the graph cut technique [17] and the fourth one is designed for RoseRiver to preserve stability between adjacent sets of tree cuts.”
iLamps: geometrically aware and self-configuring projectors (2003, methodology): “Furthermore, as researchers have noted [Li et al. 2005], Bayesian Matting fails for highly textured areas where local spatial coherence or smoothness assumptions are violated.”
Geodesic graph cut for interactive image segmentation (2010, background): “Because it can easily operate on multi-dimensional images, the graph-cut approach has also been applied to videos [17,31] and image volumes [1,4,19].”