Vectorising Bitmaps into Semi‐Transparent Gradient Layers

Richardt, Christian; López‐Moreno, Jorge; Bousseau, Adrien; Agrawala, Maneesh; Drettakis, George

doi:10.1111/cgf.12408

Cited by 26 publications

(13 citation statements)

References 30 publications

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“…Our idea of minimizing the complexity of the output representation also has great potential for the vectorization of color images, in particular to extract layers that compactly represent transparency and occlusion effects [Richardt et al 2014]. However, this new domain raises specific challenges, since the optimization should evaluate many interpretations of the shape and color of image regions.…”

Section: Discussionmentioning

confidence: 99%

Fidelity vs. simplicity

2016

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Inria (a) Input rough sketch (b) [Noris et al. 2013] (c) Sketch filtered by [Bartolo et al. 2007] followed by [Noris et al. 2013] (d) Our automatic result Figure 1: Rough sketches often contain overlapping strokes (a), which existing vectorization algorithms [Noris et al. 2013] represent as multiple curves (b). Pre-filtering the drawing with the method of Bartolo et al. [2007] improves the vectorization, but produces spurious curve segments at junctions (c). Since existing algorithms analyze junctions locally, they cannot recover the proper topology of these seemingly similar line configurations. By adopting a global formulation that optimizes for both fidelity to the input sketch and simplicity of the output curve network, our algorithm recovers proper topology while significantly reducing the overall number of curves and control points. Design sketch after Sori Yanagi's "Butterfly" stool.

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Section: Discussionmentioning

confidence: 99%

Fidelity vs. simplicity

2016

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“…Moreover, identifying the order of brushstrokes and their individual colors is important as well for authentication purposes. In fact, it is not only a request from painting authentication, but also is the basis of many existing layer decomposition methods [7,30,33,34,37]. However, it still remains challenging since overlapped strokes result in mixed color, and transparency change leads to blur.…”

Section: Q2mentioning

confidence: 99%

Fast Accurate and Automatic Brushstroke Extraction

Fu¹,

Yeh

et al. 2021

ACM Trans. Multimedia Comput. Commun. Appl.

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Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.

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“…Without layers, simple edits may become very challenging. [32] presents an approach to produce editable vector graphics, in which the selected region is decomposed into a linear or radial gradient and the residual, background pixels. [42] aims at decomposing Chinese paintings into a collection of layered brushstrokes with an assumption that at most two strokes are overlapping and there is minimally varying transparency.…”

Section: Brushstroke Extractionmentioning

confidence: 99%

High Relief from Brush Painting

Yeh

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Relief is an art form part way between 3D sculpture and 2D painting. We present a novel approach for generating a texture-mapped high-relief model from a single brush painting. Our aim is to extract the brushstrokes from a painting and generate the individual corresponding relief proxies rather than recovering the exact depth map from the painting, which is a tricky computer vision problem, requiring assumptions that are rarely satisfied. The relief proxies of brushstrokes are then combined together to form a 2.5D high-relief model. To extract brushstrokes from 2D paintings, we apply layer decomposition and stroke segmentation by imposing boundary constraints. The segmented brushstrokes preserve the style of the input painting. By inflation and a displacement map of each brushstroke, the features of brushstrokes are preserved by the resultant high-relief model of the painting. We demonstrate that our approach is able to produce convincing high-reliefs from a variety of paintings(with humans, animals, flowers, etc.). As a secondary application, we show how our brushstroke extraction algorithm could be used for image editing. As a result, our brushstroke extraction algorithm is specifically geared towards paintings with each brushstroke drawn very purposefully, such as Chinese paintings, Rosemailing paintings, etc.

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Vectorising Bitmaps into Semi‐Transparent Gradient Layers

Cited by 26 publications

References 30 publications

Fidelity vs. simplicity

Fidelity vs. simplicity

Fast Accurate and Automatic Brushstroke Extraction

High Relief from Brush Painting

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