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

Abstract

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.

Estimation of drinking water volume of laboratory animals based on image processing

“…It is necessary to obtain parameters such as the edge of the water bottle, the edge of the liquid surface, and the inclination angle of the water bottle to calculate the volume of water in the water bottle. In this paper, the cumulative probability Hough transform 31 – 34 is used to obtain key line segment's expression and the line segment's endpoint. Mark the pixel points in the extracted edge image, generate a set of lines passing through the point at different angles, and represent the set in polar coordinates.…”