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Volume 3 No. 1
28 March 2017

Shi-Min Hu

2017, 3(1): 1-1.   doi:10.1007/s41095-017-0079-3
Abstract ( 256 HTML ( 3   PDF(214KB) ( 149 )   Save

Connelly Barnes,Fang-Lue Zhang

2017, 3(1): 3-20.   doi:10.1007/s41095-016-0064-2
Abstract ( 335 HTML ( 5   PDF(63300KB) ( 45 )   Save

This paper surveys the state-of-the-art of research in patch-based synthesis.?Patch-based methods synthesize output images by copying small regions from exemplar imagery.?This line of research originated from an area called “texture synthesis”, which focused on creating regular or semi-regular textures from small exemplars. However, more recently, much research has focused on synthesis of larger and more diverse imagery, such as photos, photo collections, videos, and light fields. Additionall...

Hideki Todo,Yasushi Yamaguchi

2017, 3(1): 21-31.   doi:10.1007/s41095-016-0066-0
Abstract ( 380 HTML ( 0   PDF(19064KB) ( 35 )   Save

Although many photorealistic relighting methods provide a way to change the illumination of objects in a digital photograph, it is currently difficult to relight digital illustrations having a cartoon shading style.?The main difference between photorealistic and cartoon shading styles is that cartoon shading is characterized by soft color quantization and nonlinear color variations that cause noticeable reconstruction errors under a physical reflectance assumption, such as Lambertian reflecti...

Shuang Liu,Yongqiang Zhang,Xiaosong Yang,Daming Shi,Jian J. Zhang

2017, 3(1): 33-47.   doi:10.1007/s41095-016-0068-y
Abstract ( 443 HTML ( 4   PDF(7774KB) ( 60 )   Save

We present a novel approach for automatically detecting and tracking facial landmarks across poses and expressions from in-the-wild monocular video data, e.g., YouTube videos and smartphone recordings.?Our method does not require any calibration or manual adjustment for new individual input videos or actors. Firstly, we propose a method of robust 2D facial landmark detection across poses, by combining shape-face canonical-correlation analysis with a global supervised descent method. Since 2D ...

Akihiko Murai,Q. Youn Hong,Katsu Yamane,Jessica K. Hodgins

2017, 3(1): 49-60.   doi:10.1007/s41095-016-0065-1
Abstract ( 451 HTML ( 1   PDF(2954KB) ( 442 )   Save

Deformation of skin and muscle is essential for bringing an animated character to life. This deformation is difficult to animate in a realistic fashion using traditional techniques because of the subtlety of the skin deformations that must move appropriately for the character design. In this paper, we present an algorithm that generates natural, dynamic, and detailed skin deformation (movement and jiggle) from joint angle data sequences. The algorithm has two steps: identification of paramete...

Xueting Liu,Chengze Li,Tien-Tsin Wong

2017, 3(1): 61-71.   doi:10.1007/s41095-016-0069-x
Abstract ( 493 HTML ( 0   PDF(10529KB) ( 23 )   Save

Due to the lack of color in manga (Japanese comics), black-and-white textures are often used to enrich visual experience.?With the rising need to digitize manga, segmenting texture regions from manga has become an indispensable basis for almost all manga processing, from vectorization to colorization. Unfortunately, such texture segmentation is not easy since textures in manga are composed of lines and exhibit similar features to structural lines (contour lines).?So currently, texture segment...

Junlei Zhang,Dianguang Gai,Xin Zhang,Xuemei Li

2017, 3(1): 73-82.   doi:10.1007/s41095-016-0070-4
Abstract ( 262 HTML ( 0   PDF(5462KB) ( 214 )   Save

Example-based super-resolution algorithms, which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution.?Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we c...

Lin Chen,Meng Yang

2017, 3(1): 83-94.   doi:10.1007/s41095-016-0073-1
Abstract ( 279 HTML ( 0   PDF(691KB) ( 190 )   Save

Sparse coding and supervised dictionary learning have rapidly developed in recent years, and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification, which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers....