A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting

By Xue Bai, Guillermo Sapiro. 2007

518 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

SimpleClick: Interactive Image Segmentation with Simple Vision Transformers (2022, methodology): “Out-of-domain evaluation on three medical image datasets: ssTEM [15], BraTS [4], and OAIZIB [2].”
Iteratively Trained Interactive Segmentation (2018, background): “Graph cut [3] >20 >20 Geodesic matting [1] >20 >20 Random walker [13] 16.”
User-Friendly Interactive Image Segmentation Through Unified Combinatorial User Inputs (2010, background): “[14] X. Bai and G. Sapiro, “A geodesic framework for fast interactive image and video segmentation and matting,” inICCV, 2007, pp. 1–8.”
TouchCut: Fast image and video segmentation using single-touch interaction (2014, methodology): “The segmentation of objects from cluttered natural images remains a fundamental and inherently challenging Computer Vision problem.”
Geodesic graph cut for interactive image segmentation (2010, methodology): “Geodesic segmentation [2], like other seed-expansion approaches, can robustly segment long, thin structures with-”
Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation (2015, background): “Later, Bai et al. [81] further extended the solution to soft matting problem.”
Saliency-Aware Video Object Segmentation (2018, background): “The geodesic distance, which has been shown to be effective in many interactive computer vision applications [16], [17], [18], [19], has the power of abstracting object structure to efﬁciently determine its central regions by assigning higher saliency values to more representative regions.”
Ilastik: Interactive learning and segmentation toolkit (2011, background): “The majority of interactive segmentation approaches are based on user seeds [2, 3, 4, 5, 6, 7].”
Generative Image Segmentation Using Random Walks with Restart (2008, methodology): “In [5], the geodesic distance from the seeds was used for image segmentation.”
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input (2019, methodology): “Interactive Image Segmentation: Early interactive image segmentation methods mainly exploit boundary properties for segmentation [17, 29] while more recent approaches are based on graphical models, such as graph cut [4, 21, 33], random walker [11] and geodesic approaches [2, 7, 31].”
Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting (2009, background): “A preliminary version of this work was introduced in [6].”
Nonparametric higher-order learning for interactive segmentation (2010, background): “During the last decade, the main research directions on interactive segmentation algorithms are focused on Graph Cuts [4][16] to find the minimum cut between the foreground and background seeds via a maximum flow computation, Random Walker [9][12] to determine the labels via the seed propagation on ”
Ieee Transactions on Pattern Analysis and Machine Intelligence Segmentation of Moving Objects by Long Term Video Analysis (2014, unknown): “Interactive video segmentation methods can avoid over-segmentation, but they require significant user input [6], [52].”
Saliency-aware geodesic video object segmentation (2015, methodology): “In this paper, we try to introduce geodesic distance into our totally automatic segmentation framework, which is different with previous approaches [3, 25, 2, 10] that require careful user assistance.”
Learning an interactive segmentation system (2009, background): “For instance, geodesic distance based approaches [2, 12, 22] prefer brush strokes equidistant from the segmentation boundary as opposed to graph cuts based approaches [7, 20].”