Browsing by Author "Chu, Hung-Kuo"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Generating Color Scribble Images using Multi-layered Monochromatic Strokes Dithering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Lo, Yi-Hsiang; Lee, Ruen-Rone; Chu, Hung-Kuo; Alliez, Pierre and Pellacini, Fabio
    Color scribbling is a unique form of illustration where artists use compact, overlapping, and monochromatic scribbles at microscopic scale to create astonishing colorful images at macroscopic scale. The creation process is skill-demanded and time-consuming, which typically involves drawing monochromatic scribbles layer-by-layer to depict true-color subjects using a limited color palette delicately. In this work, we present a novel computational framework for automatic generation of color scribble images from arbitrary raster images. The core contribution of our work lies in a novel color dithering model tailormade for synthesizing a smooth color appearance using multiple layers of overlapped monochromatic strokes. Specifically, our system reconstructs the appearance of the input image by (i) generating layers of monochromatic scribbles based on a limited color palette derived from input image, and (ii) optimizing the drawing sequence among layers to minimize the visual color dissimilarity between dithered image and original image as well as the color banding artifacts. We demonstrate the effectiveness and robustness of our algorithm with various convincing results synthesized from a variety of input images with different stroke patterns. The experimental study further shows that our approach faithfully captures the scribble style and the color presentation at respectively microscopic and macroscopic scales, which is otherwise difficult for state-of-the-art methods.
  • Thumbnail Image
    Item
    Img2Logo: Generating Golden Ratio Logos from Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Hsiao, Kai-Wen; Yang, Yong-Liang; Chiu, Yung-Chih; Hu, Min-Chun; Yao, Chih-Yuan; Chu, Hung-Kuo; Myszkowski, Karol; Niessner, Matthias
    Logos are one of the most important graphic design forms that use an abstracted shape to clearly represent the spirit of a community. Among various styles of abstraction, a particular golden-ratio design is frequently employed by designers to create a concise and regular logo. In this context, designers utilize a set of circular arcs with golden ratios (i.e., all arcs are taken from circles whose radii form a geometric series based on the golden ratio) as the design elements to manually approximate a target shape. This error-prone process requires a large amount of time and effort, posing a significant challenge for design space exploration. In this work, we present a novel computational framework that can automatically generate golden ratio logo abstractions from an input image. Our framework is based on a set of carefully identified design principles and a constrained optimization formulation respecting these principles. We also propose a progressive approach that can efficiently solve the optimization problem, resulting in a sequence of abstractions that approximate the input at decreasing levels of detail. We evaluate our work by testing on images with different formats including real photos, clip arts, and line drawings. We also extensively validate the key components and compare our results with manual results by designers to demonstrate the effectiveness of our framework. Moreover, our framework can largely benefit design space exploration via easy specification of design parameters such as abstraction levels, golden circle sizes, etc.