DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels

In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals. Although several advances have been reported in the recent years for these tasks, the estimated information is often imprecise particularly near depth discontinuities or creases. Studies have however shown that precisely such depth edges carry critical cues for the perception of shape, and play important roles in tasks like depth-based segmentation or foreground selection. Unfortunately, the currently extracted channels often carry conflicting signals, making it difficult for subsequent applications to effectively use them. In this paper, we focus on the problem of obtaining high-precision depth edges (i.e., depth contours and creases) by jointly analyzing such unreliable information channels. We propose DepthCut, a data-driven fusion of the channels using a convolutional neural network trained on a large dataset with known depth. The resulting depth edges can be used for segmentation, decomposing a scene into depth layers with relatively flat depth, or improving the accuracy of the depth estimate near depth edges by constraining its gradients to agree with these edges. Quantitatively, we compare against 15 variants of baselines and demonstrate that our depth edges result in an improved segmentation performance and an improved depth estimate near depth edges compared to data-agnostic channel fusion. Qualitatively, we demonstrate that the depth edges result in superior segmentation and depth orderings.Comment: 12 page

Implementing non-photorealistic rendreing enhancements with real-time performance

We describe quality and performance enhancements, which work in real-time, to all well-known Non-photorealistic (NPR) rendering styles for use in an interactive context. These include Comic rendering, Sketch rendering, Hatching and Painterly rendering, but we also attempt and justify a widening of the established definition of what is considered NPR. In the individual Chapters, we identify typical stylistic elements of the different NPR styles. We list problems that need to be solved in order to implement the various renderers. Standard solutions available in the literature are introduced and in all cases extended and optimised. In particular, we extend the lighting model of the comic renderer to include a specular component and introduce multiple inter-related but independent geometric approximations which greatly improve rendering performance. We implement two completely different solutions to random perturbation sketching, solve temporal coherence issues for coal sketching and find an unexpected use for 3D textures to implement hatch-shading. Textured brushes of painterly rendering are extended by properties such as stroke-direction and texture, motion, paint capacity, opacity and emission, making them more flexible and versatile. Brushes are also provided with a minimal amount of intelligence, so that they can help in maximising screen coverage of brushes. We furthermore devise a completely new NPR style, which we call super-realistic and show how sample images can be tweened in real-time to produce an image-based six degree-of-freedom renderer performing at roughly 450 frames per second. Performance values for our other renderers all lie between 10 and over 400 frames per second on homePC hardware, justifying our real-time claim. A large number of sample screen-shots, illustrations and animations demonstrate the visual fidelity of our rendered images. In essence, we successfully achieve our attempted goals of increasing the creative, expressive and communicative potential of individual NPR styles, increasing performance of most of them, adding original and interesting visual qualities, and exploring new techniques or existing ones in novel ways.KMBT_363Adobe Acrobat 9.54 Paper Capture Plug-i

Testing Effects of Color Constancy for Images displayed on CRT Devices

In this report we examine the effects of color constancy for images displayed on computer monitors, particularly CRT devices. The motivation for this work is based upon Edwin Land's Retinex Theory which attempts to explain color constancy in human perception. Using Land's theory, we can create images with a severely undersampled color space (down to a full omission of one color component) that are still appropriately interpreted by the human observer. Possible applications of this are compression schemes as well as image enhancement techniques, some of which are already known. We present a simple web-based testing scheme to verify the viability of displaying duo-chrome (e.g. extreme color undersampling) images on CRT devices in different environmental settings. Some results and possible interpretations of these results are also presented

Bomba

Ömer Seyfettin'in İttihat'ta tefrika edilen Bomba adlı romanıArşivdeki eksikler nedeniyle tefrikanın tam metni verilememiştir. Bkz. "Tefrika Bilgi Formu"

How2Sketch: Generating Easy-To-Follow Tutorials for Sketching 3D Objects

Accurately drawing 3D objects is difficult for untrained individuals, as it requires an understanding of perspective and its effects on geometry and proportions. Step-by-step tutorials break the complex task of sketching an entire object down into easy-to-follow steps that even a novice can follow. However, creating such tutorials requires expert knowledge and is a time-consuming task. As a result, the availability of tutorials for a given object or viewpoint is limited. How2Sketch addresses this problem by automatically generating easy-to-follow tutorials for arbitrary 3D objects. Given a segmented 3D model and a camera viewpoint,it computes a sequence of steps for constructing a drawing scaffold comprised of geometric primitives, which helps the user draw the final contours in correct perspective and proportion. To make the drawing scaffold easy to construct, the algorithm solves for an ordering among the scaffolding primitives and explicitly makes small geometric modifications to the size and location of the object parts to simplify relative positioning. Technically, we formulate this scaffold construction as a single selection problem that simultaneously solves for the ordering and geometric changes of the primitives. We demonstrate our algorithm for generating tutorials on a variety of man-made objects and evaluate how easily the tutorials can be followed with a user study

Diffusion Curves: A Vector Representation for Smooth-Shaded Images

International audienceWe describe a new vector-based primitive for creating smooth-shaded images, called the diffusion curve. A diffusion curve partitions the space through which it is drawn, defining different colors on either side. These colors may vary smoothly along the curve. In addition, the sharpness of the color transition from one side of the curve to the other can be controlled. Given a set of diffusion curves, the final image is constructed by solving a Poisson equation whose constraints are specified by the set of gradients across all diffusion curves. Like all vector-based primitives, diffusion curves conveniently support a variety of operations, including geometry-based editing, keyframe animation, and ready stylization. Moreover, their representation is compact and inherently resolution-independent. We describe a GPU-based implementation for rendering images defined by a set of diffusion curves in realtime. We then demonstrate an interactive drawing system for allowing artists to create artworks using diffusion curves, either by drawing the curves in a freehand style, or by tracing existing imagery. The system is simple and intuitive: we show results created by artists after just a few minutes of instruction. Furthermore, we describe a completely automatic conversion process for taking an image and turning it into a set of diffusion curves that closely approximate the original image content

Artistic minimal rendering with lines and blocks

Many non-photorealistic rendering techniques exist to produce artistic effects from given images. Inspired by various artists, interesting effects can be produced by using a minimal rendering, where the minimum refers to the number of tones as well as the number and complexity of the primitives used for rendering. Our method is based on various computer vision techniques, and uses a combination of refined lines and blocks (potentially simplified), as well as a small number of tones, to produce abstracted artistic rendering with sufficient elements from the original image. We also considered a variety of methods to produce different artistic styles, such as colour and 2-tone drawings, and use semantic information to improve renderings for faces. By changing some intuitive parameters a wide range of visually pleasing results can be produced. Our method is fully automatic. We demonstrate the effectiveness of our method with extensive experiments and a user study

Benchmarking non-photorealistic rendering of portraits

We present a set of images for helping NPR practitioners evaluate their image-based portrait stylisation algorithms. Using a standard set both facilitates comparisons with other methods and helps ensure that presented results are representative. We give two levels of difficulty, each consisting of 20 images selected systematically so as to provide good coverage of several possible portrait characteristics. We applied three existing portrait-specific stylisation algorithms, two general-purpose stylisation algorithms, and one general learning based stylisation algorithm to the first level of the benchmark, corresponding to the type of constrained images that have often been used in portrait-specific work. We found that the existing methods are generally effective on this new image set, demonstrating that level one of the benchmark is tractable; challenges remain at level two. Results revealed several advantages conferred by portrait-specific algorithms over general-purpose algorithms: portrait-specific algorithms can use domain-specific information to preserve key details such as eyes and to eliminate extraneous details, and they have more scope for semantically meaningful abstraction due to the underlying face model. Finally, we provide some thoughts on systematically extending the benchmark to higher levels of difficulty