Level Set Methods and Dynamic Implicit Surfaces, 1st ed. In International Conference on Computer Vision Theory and Applications (VISAPP'09). Fast approximate nearest neighbors with automatic algorithm configuration. Interactive image segmentation based on level sets of probabilities. Shape classification using the inner-distance. Video segmentation by non-local consensus voting. Structured forests for fast edge detection. A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape.

Cremers, D., Rousson, M., and Deriche, R.

Chuang, Y.-Y., Agarwala, A., Curless, B., Salesin, D.

Chuang, Y.-Y., Curless, B., Salesin, D.

Large displacement optical flow from nearest neighbor fields.

Chen, Z., Jin, H., Lin, Z., Cohen, S., and Wu, Y.

Caselles, V., Kimmel, R., and Sapiro, G.

High accuracy optical flow estimation based on a theory for warping.

Brox, T., Bruhn, A., Papenberg, N., and Weickert, J.

Computer Vision and Image Understanding 110, 3, 346-359.

Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L.PatchMatch: A randomized correspondence algorithm for structural image editing. Barnes, C., Shechtman, E., Finkelstein, A., and Goldman, D.Fast edge-preserving patchmatch for large displacement optical flow. Dynamic color flow: A motion-adaptive color model for object segmentation in video. Video SnapCut: Robust video object cutout using localized classifiers. Bai, X., Wang, J., Simons, D., and Sapiro, G.Keyframe-based tracking for rotoscoping and animation. Agarwala, A., Hertzmann, A., Salesin, D.Thus, it reduces the required amount of user effort, and provides a basis for an effective interactive video object cutout tool.

Our results demonstrate that the proposed method is significantly more accurate than the existing state-of-the-art on a wide variety of video sequences. The resulting mask transfer method may also be used for coherently interpolating the foreground masks between two distant source frames. A modified level set method is then applied to produce a clean mask, based on the pixel labels and the S-edges computed by the previous two steps. The same split-NNF is also used to aid a novel edge classifier in detecting silhouette edges (S-edges) that separate the foreground from the background. These NNFs are then used to jointly predict a coherent labeling of the pixels in the target frame. Observing that the background and foreground regions typically exhibit different motions, we leverage these differences by computing two separate nearest-neighbor fields (split-NNF) from the target to the source frame. Given a source frame for which a foreground mask is already available, we compute an estimate of the foreground mask at another, typically non-successive, target frame. We introduce JumpCut, a new mask transfer and interpolation method for interactive video cutout.

Jumpcut video