Region-based structure line detection for cartoons

Abstract

Cartoons are a worldwide popular visual entertainment medium with a long history. Nowadays, with the boom of electronic devices, there is an increasing need to digitize old classic cartoons as a basis for further editing, including deformation, colorization, etc. To perform such editing, it is essential to extract the structure lines within cartoon images. Traditional edge detection methods are mainly based on gradients. These methods perform poorly in the face of compression artifacts and spatially-varying line colors, which cause gradient values to become unreliable. This paper presents the first approach to extract structure lines in cartoons based on regions. Our method starts by segmenting an image into regions, and then classifies them as edge regions and non-edge regions. Our second main contribution comprises three measures to estimate the likelihood of a region being a non-edge region. These measure darkness, local contrast, and shape. Since the likelihoods become unreliable as regions become smaller, we further classify regions using both likelihoods and the relationships to neighboring regions via a graph-cut formulation. Our method has been evaluated on a wide variety of cartoon images, and convincing results are obtained in all cases.

Automated inpainting of Tex Willer comics: adapting manga techniques for western comic transformation

“…Structural line extraction is essential for colorization and inpainting tasks in comics studies. Mao et al [20] introduced a novel method for detecting structural lines in low-quality cartoons. Additionally, Li et al [3] tackled the challenge of extracting structural lines from intricate manga patterns using convolutional neural networks (CNNs), which also helps in reducing manual labeling efforts and effectively suppressing various screen patterns to yield clear and smooth lines.…”

Shape correspondence for cel animation based on a shape association graph and spectral matching

“…A E represents the topological relationship information attached to the edge, that is, the type of topological relationship between the two regions. There are many ways to extract regions and construct a traditional RAG [23][24][25]. We follow the RAG extraction method proposed by Liu et al [10].…”

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

“…There has been a certain number of papers published in recent years. Mao et al [10] proposed an approach to extract lines based on regions for cartoons. Li et al [11] removed the halftone screen and extracted manga structural lines from black-white manga with CNNs.…”