Closure-aware sketch simplification

Liu, Xueting; Wong, Tien‐Tsin; Heng, Pheng‐Ann

doi:10.1145/2816795.2818067

Cited by 51 publications

(59 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing approaches for line drawing vectorization often roughly follow a three‐step procedure. First, an input image is simplified by removing needless strokes (or adding meaningful strokes) [BTS05, BCF*07, OK11, LWH15, SSISI16, SISSI17]. Second, the topological structure of the drawing is identified, which includes fitting path segment and finding junction points at which the paths intersect [Che09,NSS*12,JF14].…”

Section: Related Workmentioning

confidence: 99%

Semantic Segmentation for Line Drawing Vectorization Using Neural Networks

Kim

Wang

Öztireli

et al. 2018

Computer Graphics Forum

View full text Add to dashboard Cite

In this work, we present a method to vectorize raster images of line art. Inverting the rasterization procedure is inherently ill‐conditioned, as there exist many possible vector images that could yield the same raster image. However, not all of these vector images are equally useful to the user, especially if performing further edits is desired. We therefore define the problem of computing an instance segmentation of the most likely set of paths that could have created the raster image. Once the segmentation is computed, we use existing vectorization approaches to vectorize each path, and then combine all paths into the final output vector image. To determine which set of paths is most likely, we train a pair of neural networks to provide semantic clues that help resolve ambiguities at intersection and overlap regions. These predictions are made considering the full context of the image, and are then globally combined by solving a Markov Random Field (MRF). We demonstrate the flexibility of our method by generating results on character datasets, a synthetic random line dataset, and a dataset composed of human drawn sketches. For all cases, our system accurately recovers paths that adhere to the semantics of the drawings.

show abstract

Section: Related Workmentioning

confidence: 99%

Semantic Segmentation for Line Drawing Vectorization Using Neural Networks

Kim

Wang

Öztireli

et al. 2018

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…Their network structural features were similar to that of U-Net [12], and they emphasized the establishment of a targeted training database, which would include targeted patterns. Liu et al [13] developed a clear cartoon sketch simplification algorithm for sketch simplification with closure aware features.…”

Section: Related Workmentioning

confidence: 99%

Progressive Full Data Convolutional Neural Networks for Line Extraction from Anime-Style Illustrations

Xin

Wong

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Anime-style comics are popular world-wide and an important industry in Asia. However, the output quantity and quality control of art workers have become the biggest obstacle to industrialization, and it is time consuming to produce new manga without the help of an intelligence assisted tool. As deep learning techniques have achieved great successes in different areas, it is worth exploring them to develop algorithms and systems for computational manga. Extracting line drawings from finished illustrations is one of the main tasks in drawing a manuscript and also a crucial task in the common painting process. However, traditional filters such as Sobel, Laplace, and Canny cannot output good results and require manual adjustments of the parameters. In order to address these problems, in this paper, we propose progressive full data convolutional neural networks for extracting lines from anime-style illustrations. Experimental results show that our progressive full data convolutional neural networks not only can learn as much as processing methods for the detailed regions, but also can accomplish the target extraction work with only a small training dataset.

show abstract

“…Fully accepted general solutions do not yet exist, and segmentation remains an area of active research even for the 2D case Larsson et al 2015;. In parallel, interactive sketch segmentation and/or completion methods based on human input were proposed to fill in drawings [Pessoa and Weerd 2003], to select layers for shape manipulation [Igarashi and Mitani 2010], to segment sketchy drawings [Noris et al 2012] or to simplify them [Liu et al 2015;Mi et al 2009].…”

Section: Related Workmentioning

confidence: 99%

Structuring and layering contour drawings of organic shapes

Parakkat

Cani

Barthe

2018

Proceedings of the Joint Symposium on Computational Aesthetics and Sketch-Based Interfaces and Modeling and Non-Photorealistic

View full text Add to dashboard Cite

Complex vector drawings serve as convenient and expressive visual representations, but they remain difficult to edit or manipulate. For clean-line vector drawings of smooth organic shapes, we describe a method to automatically extract a layered structure for the drawn object from the current or nearby viewpoints. The layers correspond to salient regions of the drawing, which are often naturally associated to 'parts' of the underlying shape. We present a method that automatically extracts salient structure, organized as parts with relative depth orderings, from clean-line vector drawings of smooth organic shapes. Our method handles drawings that contain complex internal contours with T-junctions indicative of occlusions, as well as internal curves that may either be expressive strokes or substructures. To extract the structure, we introduce a new partaware metric for complex 2D drawings, the radial variation metric, which is used to identify salient sub-parts. These sub-parts are then considered in a priority-ordered fashion, which enables us to identify and recursively process new shape parts while keeping track of Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. Expressive '18, August 17-19, 2018, Victoria, BC, Canada © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-5892-7/18/08. . . $15.00 https://doi.org/10.1145/3229147.3229155 their relative depth ordering. The output is represented in terms of scalable vector graphics layers, thereby enabling meaningful editing and manipulation. We evaluate the method on multiple input drawings and show that the structure we compute is convenient for subsequent posing and animation from nearby viewpoints.

show abstract

Closure-aware sketch simplification

Cited by 51 publications

References 26 publications

Semantic Segmentation for Line Drawing Vectorization Using Neural Networks

Semantic Segmentation for Line Drawing Vectorization Using Neural Networks

Progressive Full Data Convolutional Neural Networks for Line Extraction from Anime-Style Illustrations

Structuring and layering contour drawings of organic shapes

Contact Info

Product

Resources

About