For camera-based image capturing, the impact of exposure or camera parameters (ISO sensitivity, shutter speed, and aperture F-number) on imaging quality is decisive. Such parameters interact in a coupled manner during the imaging process to determine the exposure quality and the degree of blur in a photograph. Naturally, decoupling such parameters from images holds significant value for applications like image quality assessment and illumination optimization. However, there has been no systematic research dedicated to this topic. In this paper, we propose a new benchmark, Cam-Bench, for estimating camera parameters on images directly. It collects an image dataset Cam-10K with various indoor scenes and accurate labels of camera parameters. Based on Cam-10K, we propose a camera parameter estimation network to decouple and regress recorded exposure information. To the best of our knowledge, Cam-Bench is the first benchmark for camera parameter estimation. Experiments demonstrate that it can enhance the performance of various downstream applications.The source code has been made publicly available at: https://github.com/pengquanhong/CamBench.

High-quality image generation is an important topic in digital visualization. As a sub-topic of the research, color transfer is to produce a high-quality image with ideal color scheme learned from the reference one. In this article, we investigate the mainstream methods of color transfer to provide a survey that introduces the related theories and frameworks. Such methods can be divided into three categories: statistical color transfer, semantic-based color transfer, and color transfer for special target. For these mainstream technical routes, we discuss the related research background, technical details, and representative methods. We also exhibit some new trends of the topic according to recent progress. Based on the comparisons, we discuss the unsolved issues of color transfer and potential solutions in future work.

"Thangka", is a word in the Tibetan language that refers to a kind of scroll painting mounted on silk. Thangka art, which is a very cherished and intangible cultural heritage, has a long history and distinctive characteristics. As an important prerequisite for digital protection, the research on how to classify the Thangka images quickly and accurately has become a problem of concern to scholars from in various fields. Due to the high similarity and complex structure of the Thangka image, manual classification needs to consume a large number of human resources with sufficient knowledge reserves. To improve the efficiency and accuracy of Thangka classification, researchers began to focus on computer-based machine learning and deep learning technology to complete the Thangka image classification task. Due to the complexity of the Thangka image and the lack of training samples, the existing classification methods cannot well complete the Thangka classification task. To solve the above problems, this paper proposes a fine-grained classification method Tk-SENet for Thangka images based on SENet. This method introduces the dual mechanism of spatial attention and channel attention and uses different sizes of convolution kernels to perform convolution operations in images and gives different weights according to the importance of channels and regions, which improves the classification efficiency. The pooling method in the squeeze operation and the activation function in the excitation operation are optimized to improve the classification accuracy. In the training process, the unique training method of the Thangka image is used to prepare for the excellent completion of the fine-grained classification task of the Thangka image. The experimental results show that the improved network model improves the classification accuracy by 3.6658% compared with SENet. Compared with AlexNet, ZFNet, VGG16, VGG19, ResNet-152 , DenseNet and SKNet, the accuracy increases by 6.7747%, 6.2804%, 6.6528%, 5.2586%, 6.0404% , 5.6962% and 2.9241% respectively. At the same time, compared with the existing classification methods of Thangka images, the proposed method can not only accurately identify the categories of Thangka statues, but also identify the production process types of Thangka images. Therefore, this method is more suitable for the fine-grained classification of Thangka images and provides strong technical support for the digital protection of intangible cultural heritage.

Abstract In this article, we propose a novel 3D face modeling method which constructs a new 3D face model from a low‐dimensional feature space consisted of a large set of blend shapes based on the discrete shape space theory. The details of original face features are completely retained during the modeling process and a large number of new natural faces are constructed by several face samples. The optimization process of our method is independently decoupled for different facial attributes (identity, expression, and head pose), which improves the application flexibility and reduces the probability of it falling into a local optimal situation. The new facial data with new attributes are constructed based on the geodesic path search in discrete shape space with sufficient freedom and accuracy. In experiments and applications based on public databases (Helen, LFW, and CUFS), the modeling results show our method can provide high‐quality 3D face model, with enough freedom for face expression editing and natural facial expression animation from a small facial sample set.

The 3D skull is a well preserved bone under the effect of fire, humidity, temperature changes, and it is a important biological characteristic in the fields of archaeology, forensic science and anthropology. In particular, measuring the 3D skull similarity is a challenging and meaningful task. 3D skulls are geometric models with multiple holes and complex topologies. It is difficult to correctly calculate the similarity between 3D skulls because the general 3D shape similarity measurement is sensitive to boundaries. In this paper, we provide an effective pipeline for measuring the 3D skull similarity by calculating the cosine distance between the harmonic wave kernel signature (HWKS) values of 3D skulls. Based on the wave kernel signature, the HWKS is a shape descriptor which is involved the Laplace-Beltrami operator that can effectively extract geometrical and topological information from the 3D skulls. And the HWKS simultaneously describes the local and global properties of a skull compared to the wave kernel signature. In addition, our method is more flexible, and can be generalized to other 3D shapes. Several experiments show our method achieves good results and can correctly calculate the similarity between 3D skulls.

Facial expression editing in face sketch is an important and challenging problem in computer vision community as facial animation and modeling. For criminal investigation and portrait drawing, automatic expression editing tools for face sketch improve work efficiency obviously and reduce professional requirements for users. In this paper, we propose a novel method for facial expression editing in face sketch using shape space theory. The new facial expressions in the sketch images can be regenerated automatically. The method includes two components: 1) face sketch modeling; 2) expression editing. The face sketch modeling constructs 3D face sketch data from 3D facial database to match the 2D face sketch. Using facial landmarks, the "shape" of the face sketch is represented in shape space. The shape space is a manifold space which removes the rigid transform group. In shape space, the accurate 3D face sketch model is obtained which is consistent to the original 2D face sketch. For expression editing, we change the parameters of 3D face sketch model in the shape space to obtain new expressions. The expression transfer in 3D face sketch model can be mapped into the 2D face sketch. The advantages of our method are: full-automatic in modeling process; no requirements of drawing skills to user and friendly interaction; robustness to head poses and different scales. In experiments, we use the 3D facial database, FaceWareHouse, to construct the 3D face sketch model and use face sketch images from database: CUHK Face sketch Database (CUFS) to show the performance of expression editing. Experimental results demonstrate that our method can effectively edit facial expressions in face sketch with high consistency and fidelity.