This paper presents a novel algorithm to generate micrography QR codes, a novel machine-readable graphic generated by embedding a QR code within a micrography image. The unique structure of micrography makes it incompatible with existing methods used to combine QR codes with natural or halftone images. We exploited the high-frequency nature of micrography in the design of a novel deformation model that enables the skillful warping of individual letters and adjustment of font weights to enable the embedding of a QR code within a micrography. The entire process is supervised by a set of visual quality metrics tailored specifically for micrography, in conjunction with a novel QR code quality measure aimed at striking a balance between visual fidelity and decoding robustness. The proposed QR code quality measure is based on probabilistic models learned from decoding experiments using popular decoders with synthetic QR codes to capture the various forms of distortion that result from image embedding. Experiment results demonstrate the efficacy of the proposed method in generating micrography QR codes of high quality from a wide variety of inputs. The ability to embed QR codes with multiple scales makes it possible to produce a wide range of diverse designs. Experiments and user studies were conducted to evaluate the proposed method from a qualitative as well as quantitative perspective.

Abstract 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 tailor‐made 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.

Line drawing is a style of image abstraction where the perceptual content of the image is conveyed using distinct straight or curved lines. However, extracting semantically salient lines is not trivial and mastered only by skilled artists. While many parametric filters have successfully extracted accurate and coherent lines, their results are sensitive to parameter choice and easily lead to either an excessive or insufficient number of lines. In this work, we present an interactive system to generate concise line abstractions of arbitrary images via a few user specified strokes. Specifically, the user simply has to provide a few intuitive strokes on the input images, including tracing roughly along edges and scribbling on the region of interest, through a sketching interface. The system then automatically extracts lines that are long, coherent and share similar textural structures to form a corresponding highly detailed line drawing. We have tested our system with a wide variety of images. Our experimental results show that our system outperforms state-of-the-art techniques in terms of quality and efficiency.

Ambiguous figure-ground images, mostly represented as binary images, are fascinating as they present viewers a visual phenomena of perceiving multiple interpretations from a single image. In one possible interpretation, the white region is seen as a foreground figure while the black region is treated as shapeless background. Such perception can reverse instantly at any moment. In this paper, we investigate the theory behind this ambiguous perception and present an automatic algorithm to generate such images. We model the problem as a binary image composition using two object contours and approach it through a three-stage pipeline. The algorithm first performs a partial shape matching to find a good partial contour matching between objects. This matching is based on a content-aware shape matching metric, which captures features of ambiguous figure-ground images. Then we combine matched contours into a compound contour using an adaptive contour deformation, followed by computing an optimal cropping window and image binarization for the compound contour that maximize the completeness of object contours in the final composition. We have tested our system using a wide range of input objects and generated a large number of convincing examples with or without user guidance. The efficiency of our system and quality of results are verified through an extensive experimental study.

Grass, lawn, and meadow are common features of outdoor scenes. However, animating the grass motion under the influence of wind could be a daunting task especially if the subtle variations between grass blades are considered. Collectively, those variations of individual grass blades may produce interesting phenomena, such as the eye catching wave-like motion of a meadow. In this work, we develop a framework that simulates the grass dynamics under a plausible wind field. We first test it with a small bunch of grass blades (Figure 1) and validate it with the motion of real-world grass. By utilizing the tessellation shaders and geometry shaders of modern GPUs, we make our grass model as efficient as possible so that it scales well to a large meadow, which is also exposed to a plausible wind field simulation. As a result, our grass simulation runs in real time for meadow scenes consisting of hundreds of thousands of grass blades, and produces convincing wave-like motions when the wind blows over the meadow.

Circular scribble art is a kind of line drawing where the seemingly random, noisy and shapeless circular scribbles at microscopic scale constitute astonishing grayscale images at macroscopic scale. Such a delicate skill has rendered the creation of circular scribble art a tedious and time-consuming task even for gifted artists. In this work, we present a novel method for automatic synthesis of circular scribble art. The synthesis problem is modeled as tracing along a virtual path using a parametric circular curve. To reproduce the tone and important edge structure of input grayscale images, the system adaptively adjusts the density and structure of virtual path, and dynamically controls the size, drawing speed and orientation of parametric circular curve during the synthesis. We demonstrate the potential of our system using several circular scribble images synthesized from a wide variety of grayscale images. A preliminary experimental studying is conducted to qualitatively and quantitatively evaluate our method. Results report that our method is efficient and generates convincing results comparable to artistic artworks.

LEGO®, a popular brick-based toy construction system, provides an affordable and convenient way of fabricating geometric shapes. However, building arbitrary shapes using LEGO bricks with restrictive colors and sizes is not trivial. It requires careful design process to produce appealing, stable and constructable brick sculptures. In this work, we investigate the novel problem of constructing brick sculptures from pixel art images. In contrast to previous efforts that focus on 3D models, pixel art contains rich visual contents for generating engaging LEGO designs. On the other hand, the characteristics of pixel art and corresponding brick sculpture pose new challenges to the design process. We present Pixel2Brick, a novel computational framework to automatically construct brick sculptures from pixel art. This is based on implementing a set of design guidelines concerning the visual quality as well as the structural stability of built sculptures. We demonstrate the effectiveness of our framework with various brick sculptures (both real and virtual) generated from a variety of pixel art images. Experimental results show that our framework is efficient and gains significant improvements over state-of-the-arts.

QR code is a popular form of barcode pattern that is ubiquitously used to tag information to products or for linking advertisements. While, on one hand, it is essential to keep the patterns machine-readable; on the other hand, even small changes to the patterns can easily render them unreadable. Hence, in absence of any computational support, such QR codes appear as random collections of black/white modules, and are often visually unpleasant. We propose an approach to produce high quality visual QR codes, which we call halftone QR codes , that are still machine-readable. First, we build a pattern readability function wherein we learn a probability distribution of what modules can be replaced by which other modules. Then, given a text tag, we express the input image in terms of the learned dictionary to encode the source text. We demonstrate that our approach produces high quality results on a range of inputs and under different distortion effects.