766 research outputs found
cGAN-based Manga Colorization Using a Single Training Image
The Japanese comic format known as Manga is popular all over the world. It is
traditionally produced in black and white, and colorization is time consuming
and costly. Automatic colorization methods generally rely on greyscale values,
which are not present in manga. Furthermore, due to copyright protection,
colorized manga available for training is scarce. We propose a manga
colorization method based on conditional Generative Adversarial Networks
(cGAN). Unlike previous cGAN approaches that use many hundreds or thousands of
training images, our method requires only a single colorized reference image
for training, avoiding the need of a large dataset. Colorizing manga using
cGANs can produce blurry results with artifacts, and the resolution is limited.
We therefore also propose a method of segmentation and color-correction to
mitigate these issues. The final results are sharp, clear, and in high
resolution, and stay true to the character's original color scheme.Comment: 8 pages, 13 figure
A survey of comics research in computer science
Graphical novels such as comics and mangas are well known all over the world.
The digital transition started to change the way people are reading comics,
more and more on smartphones and tablets and less and less on paper. In the
recent years, a wide variety of research about comics has been proposed and
might change the way comics are created, distributed and read in future years.
Early work focuses on low level document image analysis: indeed comic books are
complex, they contains text, drawings, balloon, panels, onomatopoeia, etc.
Different fields of computer science covered research about user interaction
and content generation such as multimedia, artificial intelligence,
human-computer interaction, etc. with different sets of values. We propose in
this paper to review the previous research about comics in computer science, to
state what have been done and to give some insights about the main outlooks
Pixelated Semantic Colorization
While many image colorization algorithms have recently shown the capability
of producing plausible color versions from gray-scale photographs, they still
suffer from limited semantic understanding. To address this shortcoming, we
propose to exploit pixelated object semantics to guide image colorization. The
rationale is that human beings perceive and distinguish colors based on the
semantic categories of objects. Starting from an autoregressive model, we
generate image color distributions, from which diverse colored results are
sampled. We propose two ways to incorporate object semantics into the
colorization model: through a pixelated semantic embedding and a pixelated
semantic generator. Specifically, the proposed convolutional neural network
includes two branches. One branch learns what the object is, while the other
branch learns the object colors. The network jointly optimizes a color
embedding loss, a semantic segmentation loss and a color generation loss, in an
end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that
our network, when trained with semantic segmentation labels, produces more
realistic and finer results compared to the colorization state-of-the-art
Two Decades of Colorization and Decolorization for Images and Videos
Colorization is a computer-aided process, which aims to give color to a gray
image or video. It can be used to enhance black-and-white images, including
black-and-white photos, old-fashioned films, and scientific imaging results. On
the contrary, decolorization is to convert a color image or video into a
grayscale one. A grayscale image or video refers to an image or video with only
brightness information without color information. It is the basis of some
downstream image processing applications such as pattern recognition, image
segmentation, and image enhancement. Different from image decolorization, video
decolorization should not only consider the image contrast preservation in each
video frame, but also respect the temporal and spatial consistency between
video frames. Researchers were devoted to develop decolorization methods by
balancing spatial-temporal consistency and algorithm efficiency. With the
prevalance of the digital cameras and mobile phones, image and video
colorization and decolorization have been paid more and more attention by
researchers. This paper gives an overview of the progress of image and video
colorization and decolorization methods in the last two decades.Comment: 12 pages, 19 figure
Video browsing interfaces and applications: a review
We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other
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