Figure 1: We propose a video object cutout system that performs consistently well on a variety of examples which are difficult for previous approaches. See Section 6 and the accompanying video for more comparisons and results. Original videos in (d) and (f) courtesy of Artbeats. Although tremendous success has been achieved for interactive object cutout in still images, accurately extracting dynamic objects in video remains a very challenging problem. Previous video cutout systems present two major limitations: (1) reliance on global statistics, thus lacking the ability to deal with complex and diverse scenes; and (2) treating segmentation as a global optimization, thus lacking a practical workflow that can guarantee the convergence of the systems to the desired results. We present Video SnapCut, a robust video object cutout system that significantly advances the state-of-the-art. In our system segmentation is achieved by the collaboration of a set of local classifiers, each adaptively integrating multiple local image features. We show how this segmentation paradigm naturally supports local user editing and propagates them across time. The object cutout system is completed with a novel coherent video matting technique. A comprehensive evaluation and comparison is presented, demonstrating the effectiveness of the proposed system at achieving high quality results, as well as the robustness of the system against various types of inputs. 1

Figure 1: Left three: the user makes a selection by painting the object of interest with a brush (black-white circle) on a 24.5 megapixel image. Instant feedback (selection boundary or image effect) can be provided to the user during mouse dragging. Rightmost: composition and effect (sepia tone). Note that the blue scribbles are invisible to the user. They are drawn in the paper for illustration only. Abstract. In this paper, we present Paint Selection, a progressive painting-based tool for local selection in images. Paint Selection facilitates users to progressively make a selection by roughly painting the object of interest using a brush. More importantly, Paint Selection is efficient enough that instant feedback can be provided to users as they drag the mouse. We demonstrate that high quality selections can be quickly and effectively “painted ” on a variety of multi-megapixel images.

Figure 1: Two views of the graph of the same edge-avoiding wavelet centered at the shoulder of the Cameraman. The support of the wavelet is confined within the limits set by the strong edges around the upper body. We propose a new family of second-generation wavelets constructed using a robust data-prediction lifting scheme. The support of these new wavelets is constructed based on the edge content of the image and avoids having pixels from both sides of an edge. Multi-resolution analysis, based on these new edge-avoiding wavelets, shows a better decorrelation of the data compared to common linear translation-invariant multi-resolution analyses. The reduced inter-scale correlation allows us to avoid halo artifacts in band-independent multi-scale processing without taking any special precautions. We thus achieve nonlinear data-dependent multiscale edge-preserving image filtering and processing at computation times which are linear in the number of image pixels. The new wavelets encode, in their shape, the smoothness information of the image at every scale. We use this to derive a new edge-aware interpolation scheme that achieves results, previously computed by solving an inhomogeneous Laplace equation, through an explicit computation. We thus avoid the difficulties in solving large and poorly-conditioned systems of equations. We demonstrate the effectiveness of the new wavelet basis for various computational photography applications such as multi-scale dynamic-range compression, edge-preserving smoothing and detail enhancement, and image colorization.

We present a semi-automatic system that converts conventional video shots to stereoscopic video pairs. The system requires just a few user-scribbles in a sparse set of frames. The system combines a diffusion scheme, which takes into account the local saliency and the local motion at each video location, coupled with a classification scheme that assigns depth to image patches. The system tolerates both scene motion and camera motion. In typical shots, containing hundreds of frames, even in the face of significant motion, it is enough to mark scribbles on the first and last frames of the shot. Once marked, plausible stereo results are obtained in a matter of seconds, leading to a scalable video conversion system. Finally, we validate our results with ground truth stereo video. 1.

Figure 1: Affinity-based edit propagation methods such as [An and Pellacini 2008] allow one to change the appearance of an image or video (e.g., the color of the bird here) using only a few strokes, yet consuming prohibitive amount of time and memory for large data (e.g., 48 minutes and 23GB for this video containing 61M pixels). Our approximation scheme drastically reduces the cost of edit propagation methods (to 8 seconds and 22MB in this example) by exploring adaptive clustering in the affinity space. Video courtesy of BBC Motion Gallery (UK). Image/video editing by strokes has become increasingly popular due to the ease of interaction. Propagating the user inputs to the rest of the image/video, however, is often time and memory consuming especially for large data. We propose here an efficient scheme that allows affinity-based edit propagation to be computed on data containing tens of millions of pixels at interactive rate (in matter of seconds). The key in our scheme is a novel means for approximately solving the optimization problem involved in edit propagation, using adaptive clustering in a high-dimensional, affinity space. Our approximation significantly reduces the cost of existing affinitybased propagation methods while maintaining visual fidelity, and enables interactive stroke-based editing even on high resolution images and long video sequences using commodity computers. 1