Heekyung Yang

Feature-guided Convolution for Pencil Rendering

We re-render a photographic image as a simulated pencil drawing using two independent line integral convolution (LIC) algorithms that express tone and feature lines. The LIC for tone is then applied in the same direction across the image, while the LIC for features is applied in pixels close to each feature line in the direction of that line. Features are extracted using the coherent line scheme. Changing the direction and range of the LICs allows a wide range of pencil drawing style to be mimicked. We tested our algorithm on diverse images and obtained encouraging results.

Technical Section: Pencil rendering on 3D meshes using convolution

We produce various styles of pencil drawings from a 3D triangular mesh using a new two-phase approach based on convolution. First, we generate the noise particles and integration directions required for convolution on the mesh and project them onto the image space. We then use the improved convolution algorithm to integrate the projected noise along the generated integration directions. This scheme produces pencil drawings in different styles, including feature-conveying, monochrome tone-depicting and smooth color-depicting styles, examples of which are provided in this study. This rendering process is temporally coherent. Therefore, it can be used to create drawing-styled animations.

Color Pencil Rendering of Photographs

We produce color pencil drawings from photographs using feature strokes to create the salient linear elements of the picture, and hatching strokes to fill areas. Both types of strokes are generated using line integral convolution (LIC). We improve previous LIC schemes by using feature lines to determine the integration direction, and by introducing grayscale noise into each CMY color channel. Our scheme can produce visually pleasing color pencil drawings in various styles.

Texture-based Hatching for Color Image and Video

We present a texture-based hatching technique for color images and video. Whereas existing approaches produce monochrome hatching effects in considering of triangular mesh models by applying strokes of uniform size, our scheme produces color hatching effects from photographs and video using strokes with a range of sizes. We use a Delaunay triangulation to create a mesh of triangles with sizes that reflect the structure of an input image. At each vertex of this triangulation, the flow of the image is analyzed and a hatching texture is then created with the same alignment, based on real pencil strokes. This texture is given a modified version of a color sampled from the image, and then it is used to fill all the triangles adjoining the vertex. The three hatching textures that accumulate in each triangle are averaged and the result of this process across all the triangles forms the output image. We can also add a paper texture effect and enhance feature lines in the image. Our algorithm can also be applied to video. The results are visually pleasing hatching effects similar to those seen in color pencil drawings and oil paintings.

A texture-based approach for hatching color photographs

We present a texture-based approach that hatches color photographs. We use a Delaunay triangulation to create a mesh of triangles with sizes that reflect the structure of an input image. At each vertex of this triangulation, the flow of the image is analyzed and a hatching texture is then created with the same alignment, based on real pencil strokes. This texture is given a modified version of a color sampled from the image, and then it is used to fill all the triangles adjoining the vertex. The three hatching textures that accumulate in each triangle are averaged, and the result of this process across all the triangles form the output image. This method can produce visually pleasing hatching similar to that seen in colored-pencil strokes and oil paintings.

Importance-based approach for rough drawings

We present a framework for producing rough drawings from photographs. Depicting a scene using a series of lines is one of the most effective methods of visual communication. Our framework for rough drawing is comprised of three steps: extracting lines from images, estimating line importance, and producing strokes that express various styles. To extract lines, we employ the widely used difference-of-Gaussian filter approach to devise a fault-correcting line shift scheme. Line importance is estimated by combining gradient and saliency. To obtain an efficient saliency estimation, we propose a stochastic content-based method. Various styles of rough drawings are produced by convoluting adaptive stroke texture segments, which are prepared by sampling real stroke texture images. We test our framework on various images and compare our results with real artwork and other schemes.

Legorization with multi-height bricks from silhouette-fitted voxelization

We present a legorization framework that produces a LEGO model from user-specified 3D mesh model. Our framework is composed of two stages: voxelization and legorization. In the voxelization, input 3D mesh is converted to a voxel model. To preserve the shape of the 3D mesh, we devise a silhouette fitting process for the initial voxel model. For legorization, we propose three objectives: stability, aesthetics and efficiency. These objectives are expressed in a tiling equation, which builds a LEGO model using layer-by-layer approach. We legorize five models including characters and buildings to prove the excellence of our framework.

Enhancing emotion using an emotion model

In this paper, we enhance the emotion of visual contents based on a scheme established using Russells emotion model and Kansei engineering. We present a mapping between the emotions defined in Russells emotion space and the colour, tone and contrast. For an efficient mapping, we present a scheme that enables users to conveniently segment the contents into foreground and background and to apply our emotion motion selectively. The segmentation of the foreground from the background is implemented by the support of an image abstraction algorithm. This method can be applied to a variety of visual contents such as images, movies and animations. We finally prove the effectiveness of our scheme by executing a user test.

Affective Real-Time Video Abstraction Using CUDA

In this paper, we present a real-time video abstraction framework that responds to the affection of the contents. For abstraction, we extract edges and quantize the color of each frame of video in real-time in order to emphasize the important part of the frame. The key of our scheme is how to process each frame in real-time. This purpose is achieved by using CUDA environment, which is the GPU programming environment supported by nVidia, Inc. Using CUDA, our scheme can process more than eight frames per second in producing affectively abstracted images.

Simulation of Color Pencil Drawing using LIC

We present a novel approach for the simulation of color pencil effects using line integral convolution (LIC) to produce pencil drawings from images. Our key idea is to use a bilateral convolution filter to simulate the various effects of pencil strokes. Our filter resolves the drawbacks of the existing convolution-based schemes, and presents an intuitive control to mimic the properties of pencil strokes. We also present a scheme that determines stroke directions from the shapes to be drawn. Smooth tangent flows are used for the pixels close to feature lines, and partially parallel flows inside regions. The background is rendered using a flow of fixed direction. Using different styles of stroke directions increases the realism of the resulting images. This approach produces convincing pencil drawing effects from photographs.