Interactive video cutout

By Jue Wang, Pravin Bhat, Alex Colburn, Maneesh Agrawala, Michael F. Cohen. ACM Transactions on Graphics, 2005

395 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/00e79a637dbcd436f72102187b3dee473dc49ab4

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In Computer Vision and Pattern Recognition

Citations in the corpus

Event Detection in Crowded Videos (2007, methodology): “For each event, we create the model from a single instance by interactively segmenting the spatio-temporal volume using a graph-cut tool similar to [28].”
Associative hierarchical CRFs for object class image segmentation (2009, background): “This quantisation of the image allows the computation of powerful region-based features which are partially invariant to scale [31].”
Modeling the Temporal Extent of Actions (2010, background): “Other work such as [5] and [6], focus on assisting users in the painstaking task of delineating the exact time and location of actions in videos.”
Nonlocal Linear Image Regularization and Supervised Segmentation (2007, methodology): “Improvements and extensions to the method were proposed in [38, 50, 59].”
Semantic object classes in video: A high-definition ground truth database (2009, methodology): “In the context of video based object analysis, many advanced techniques have been proposed for object segmentation (Marcotegui et al., 1999; Deng and Manjunath, 2001; Patras et al., 2003; Wang et al., 2005; Agarwala et al., 2004).”
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].”
Robust Higher Order Potentials for Enforcing Label Consistency (2008, background): “They have been shown to give decent results which have proved to be useful for many applications [10, 11, 32].”
Beyond pixels: exploring new representations and applications for motion analysis (2009, neutral): “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].”
Click Carving: Segmenting Objects in Video with Point Clicks (2016, methodology): “2015), to human-in-the-loop methods where a user closely guides the system towards a good segmentation output (Wang et al. 2005; Li, Sun, and Shum 2005; Bai et al. 2009; Shankar Nagaraja, Schmidt, and Brox 2015).”
Graph Cuts and Efficient N-D Image Segmentation (2006, methodology): “…(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 (Bray et al.,…”
Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting (2009, background): “Interactive video cutout, [28], presents a system where the user draws scribbles in 3D space.”
TouchCut: Fast image and video segmentation using single-touch interaction (2014, neutral): “7.”
iLamps: geometrically aware and self-configuring projectors (2003, methodology): “These methods have also been extended to video [Wang et al. 2005; Li et al. 2005].”
A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting (2007, background): “Interactive video cutout, [18], presents a system where the user draws scribbles in 3D space.”
Fast Video Object Segmentation by Reference-Guided Mask Propagation (2018, methodology): “For professional video editing applications, interactive methods are often preferred over automatic methods [41, 11, 2, 25].”