Saliency estimation has become a valuable tool in image processing. Yet, existing approaches exhibit considerable variation in methodology, and it is often difficult to attribute improvements in result quality to specific algorithm properties. In this paper we reconsider some of the design choices of previous methods and propose a conceptually clear and intuitive algorithm for contrast-based saliency estimation.Our algorithm consists of four basic steps. First, our method decomposes a given image into compact, perceptually homogeneous elements that abstract unnecessary detail. Based on this abstraction we compute two measures of contrast that rate the uniqueness and the spatial distribution of these elements. From the element contrast we then derive a saliency measure that produces a pixel-accurate saliency map which uniformly covers the objects of interest and consistently separates fore-and background.We show that the complete contrast and saliency estimation can be formulated in a unified way using highdimensional Gaussian filters. This contributes to the conceptual simplicity of our method and lends itself to a highly efficient implementation with linear complexity. In a detailed experimental evaluation we analyze the contribution of each individual feature and show that our method outperforms all state-of-the-art approaches.

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Vectorization provides a link between raster scans of pencil-andpaper drawings and modern digital processing algorithms that require accurate vector representations. Even when input drawings are comprised of clean, crisp lines, inherent ambiguities near junctions make vectorization deceptively difficult. As a consequence, current vectorization approaches often fail to faithfully capture the junctions of drawn strokes. We propose a vectorization algorithm specialized for clean line drawings that analyzes the drawing's topology in order to overcome junction ambiguities. A gradientbased pixel clustering technique facilitates topology computation. This topological information is exploited during centerline extraction by a new "reverse drawing" procedure that reconstructs all possible drawing states prior to the creation of a junction and then selects the most likely stroke configuration. For cases where the automatic result does not match the artist's interpretation, our drawing analysis enables an efficient user interface to easily adjust the junction location. We demonstrate results on professional examples and evaluate the vectorization quality with quantitative comparison to hand-traced centerlines as well as the results of leading commercial algorithms.

This paper addresses the problem of remapping the disparity range of stereoscopic images and video. Such operations are highly important for a variety of issues arising from the production, live broadcast, and consumption of 3D content. Our work is motivated by the observation that the displayed depth and the resulting 3D viewing experience are dictated by a complex combination of perceptual, technological, and artistic constraints. We first discuss the most important perceptual aspects of stereo vision and their implications for stereoscopic content creation. We then formalize these insights into a set of basic disparity mapping operators. These operators enable us to control and retarget the depth of a stereoscopic scene in a nonlinear and locally adaptive fashion. To implement our operators, we propose a new strategy based on stereoscopic warping of the input video streams. From a sparse set of stereo correspondences, our algorithm computes disparity and image-based saliency estimates, and uses them to compute a deformation of the input views so as to meet the target disparities. Our approach represents a practical solution for actual stereo production and display that does not require camera calibration, accurate dense depth maps, occlusion handling, or inpainting. We demonstrate the performance and versatility of our method using examples from live action post-production, 3D display size adaptation, and live broadcast. An additional user study and ground truth comparison further provide evidence for the quality and practical relevance of the presented work.

Figure 1: Two examples displaying results from our interactive framework for video retargeting. The still images from the animated short "Big Buck Bunny" compare the original with the retargeted one. The pictures on the right show two different rescales. Thanks to our interactive constraint editing, we can preserve the shape and position of important scene objects even under extreme rescalings. AbstractWe present a novel, integrated system for content-aware video retargeting. A simple and interactive framework combines key frame based constraint editing with numerous automatic algorithms for video analysis. This combination gives content producers high level control of the retargeting process. The central component of our framework is a non-uniform, pixel-accurate warp to the target resolution which considers automatic as well as interactively defined features. Automatic features comprise video saliency, edge preservation at the pixel resolution, and scene cut detection to enforce bilateral temporal coherence. Additional high level constraints can be added by the producer to guarantee a consistent scene composition across arbitrary output formats. For high quality video display we adopted a 2D version of EWA splatting eliminating aliasing artifacts known from previous work. Our method seamlessly integrates into postproduction and computes the reformatting in realtime. This allows us to retarget annotated video streams at a high quality to arbitary aspect ratios while retaining the intended cinematographic scene composition. For evaluation we conducted a user study which revealed a strong viewer preference for our method.