Interactive video cutout

Wang, Jue; Bhat, P. N.; Colburn, Alex; Agrawala, Maneesh; Cohen, Michael F.

doi:10.1145/1186822.1073233

Cited by 102 publications

(83 citation statements)

References 24 publications

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“…Different from unsupervised video segmentation, interactive video segmentation focused on extracting foreground object in clutter background with simple user interventions (often just one scribble for the object and one for the background). Wang et al [9] introduced a hierarchical mean-shift preprocess to reduce the number of nodes for efficient computation, and extended 2D alpha matting scheme to 3D video volumes. Bai et al [10] presented an interactive framework for soft segmentation and matting of natural images and videos.…”

Section: Trends In Engineering and Technology (Nctet-2k17) Internatiomentioning

confidence: 99%

An Effective Approach to Streaming Video Segmentation with Sub-Optimal Low Rank Decomposition

Sujatha¹,

Babu²

2017

Nctet-2k17

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Abstract-This paper explores how to perform powerful and proficient video division while smothering the impacts of information commotions and additionally debasements, and a viable approach is acquainted with this end. Initial, a general calculation, called problematic low-rank decay (SOLD), is proposed to seek after the low-rank portrayal for video division. Given the information framework shaped by supervoxel elements of a watched video succession, SOLD looks for a problematic arrangement by making the lattice rank unequivocally decided. Specifically, the portrayal coefficient grid with the settled rank can be disintegrated into two sub-networks of low rank, and after that we iteratively streamline them with shut frame arrangements. Additionally, we join a discriminative replication earlier into SOLD in light of the perception that little size video designs have a tendency to repeat regularly inside a similar question. Second, in light of SOLD, we introduce an effective deduction calculation to perform gushing video division in both unsupervised and intuitive situations. All the more particularly, the obliged standardized cut calculation is embraced by joining the low-rank portrayal with other low level signs and fleeting reliable imperatives for spatiotemporal division. Broad analyses on two open testing informational collections VSB100 and Seg Track propose that our approach beats other video division approaches in both exactness and effectiveness.

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Section: Trends In Engineering and Technology (Nctet-2k17) Internatiomentioning

confidence: 99%

An Effective Approach to Streaming Video Segmentation with Sub-Optimal Low Rank Decomposition

Sujatha¹,

Babu²

2017

Nctet-2k17

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“…Temporally consistent segmentation One approach to video segmentation is to concatenate video frames along the temporal axis and then cast the problem as a volume segmentation problem [4,19,26]. In [4], a volumetric neighbourhood is defined by connecting adjacent pixels in both spatial and temporal domain producing a regular graph on which a binary graph-cut algorithm can be applied.…”

Section: Related Workmentioning

confidence: 99%

Space-Time Joint Multi-layer Segmentation and Depth Estimation

Guillemaut

Hilton

2012

2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization &Amp; Transmission

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Video-based segmentation and reconstruction techniques are predominantly extensions of techniques developed for the image domain treating each frame independently. These approaches ignore the temporal information contained in input videos which can lead to incoherent results. We propose a framework for joint segmentation and reconstruction which explicitly enforces temporal consistency by formulating the problem as an energy minimisation generalised to groups of frames. The main idea is to use optical flow in combination with a confidence measure to impose robust temporal smoothness constraints. Optimisation is performed using recent advances in the field of graphcuts combined with practical considerations to reduce runtime and memory consumption. Experimental results with real sequences containing rapid motion demonstrate that the method is able to improve spatio-temporal coherence both in terms of segmentation and reconstruction without introducing any degradation in regions where optical flow fails due to fast motion.

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“…Feature tracking is also much cheaper to compute and per-vertex data is easier to process on GPUs. Video representations The video mesh builds upon and extends layer-based video representations [1,37], video cube segmentation [35], and video cutouts [20]. Commercial packages use stacks of layers to represent and composite objects.…”

Section: Related Workmentioning

confidence: 99%

The video mesh: A data structure for image-based three-dimensional video editing

Chen¹,

Paris

Wang

et al. 2011

2011 IEEE International Conference on Computational Photography (ICCP)

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This paper introduces the video mesh, a data structure for representing video as 2.5D "paper cutouts." The video mesh allows interactive editing of moving objects and modeling of depth, which enables 3D effects and post-exposure camera control. The video mesh sparsely encodes optical flow as well as depth, and handles occlusion using local layering and alpha mattes. Motion is described by a sparse set of points tracked over time. Each point also stores a depth value. The video mesh is a triangulation over this point set and per-pixel information is obtained by interpolation. The user rotoscopes occluding contours and we introduce an algorithm to cut the video mesh along them. Object boundaries are refined with per-pixel alpha values. The video mesh is at its core a set of texture mapped triangles, we leverage graphics hardware to enable interactive editing and rendering of a variety of effects. We demonstrate the effectiveness of our representation with special effects such as 3D viewpoint changes, object insertion, depth-offield manipulation, and 2D to 3D video conversion.

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Interactive video cutout

Cited by 102 publications

References 24 publications

An Effective Approach to Streaming Video Segmentation with Sub-Optimal Low Rank Decomposition

An Effective Approach to Streaming Video Segmentation with Sub-Optimal Low Rank Decomposition

Space-Time Joint Multi-layer Segmentation and Depth Estimation

The video mesh: A data structure for image-based three-dimensional video editing

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