Abstract:

Abstract:Since industry 4.0 was introduced in 2013, industries across the globe have been rushing towards the era of intelligent manufacturing. The advances in data sensing technologies have further helped the collection of massive industrial data, providing an excellent opportunity for innovations in industrial informatization. However, it remains a major challenge to analyze industrial data because of its large scale, high dimensionality, heterogeneity and complexity. Constantly changing application scenarios also lead to strict requirements in the flexibility of analyses, which demand placing domain experts in the analysis loop. Therefore, visualization has been widely applied to analyzing industrial data. This review article first summarizes the data types commonly used in the industrial scenarios based on the production stages and properties. Then, based on the data properties, this paper introduces visualization methods for the temporal, spatial and spatio-temporal types. Further, this paper overviews the applications of visual analytics in the industrial scenarios and discusses the integration of automated analysis methods in the visual analytics systems. Finally, this paper prospects the development of industrial data visual analytics and possible research directions for the future.

Abstract:As a common design type and visual art form, collage is widely used in poster design, logo production, and other applications. However, traditional manual process requires a lot of repetitive attempts and it is difficult for designers to achieve the optimal layout. When using computers to solve problems, there are many different ways. This paper summarizes different collage ideas which are roughly concluded as two ideas of top-down and bottom-up, and their algorithm routes are analyzed and compared. Finally, future development trend of the problem is presented.

Abstract:Single image based three dimensional structure reconstruction is a classical and important topic in computer vision domain. This survey focus on image acquisition, such as surface material, surface shape and the information loss of target, and classified the single image based 3D reconstruction methods into three categories, i.e. illumination model, geometric element distribution, and deep learning. By analyzing and concluding the advantage and disadvantage of different methods, potential research direction is also suggested.

Abstract:Traditional interactive modeling of plants can get accurate plants models, but this process is extremely time-consuming and laborious. Automatic modeling methods like L-system-based methods can generate complex models quickly, but it is difficult for non-expert users because of its high learning cost and poor control over plant morphology. As an intuitive and efficient interaction means, the 2D sketch has a strong descriptive ability for plant morphology. In order to improve the plant modeling efficiency, a plant modeling method based on the 2D sketch is investigated. Firstly, the sketch drawn by the creator is preprocessed and analyzed to infer the creator’s intention. Then, based on the botany knowledge, the depth recovery algorithm of flowers, branches and leaves is proposed to recover the missing depth information in the 2D sketch. Finally, the target plant model is constructed according to the depth and plant characteristics. The proposed method is efficient and can support plant models with branch and leaf structures, such as flowers, potted plants, and trees.

Abstract:This paper presents a novel plane extraction algorithm based on 3D line cloud model. The algorithm first maps each line segment in the three-dimensional line cloud model to a point on the Gaussian sphere, and simplifies the plane extraction problem in the 3D space as a fitting problem of the plane through the sphere center. Then, the approximate uniform sampling is applied to the Gaussian sphere, and a plane passing through the sphere center is fitted. Finally, the plane can be extracted by separating parallel planes according to the intercept of the plane equation. The experimental results show that, in terms of the completeness of the plane extraction and the quality of plane extraction, the proposed algorithm has a significant improvement compared with classical plane extraction algorithms.

Abstract:The existing datasets used in video online detection research are mainly concentrated on long videos and the category is relatively simple. At the same time, the detection and evaluation system that meets the needs of online setting is needed to meet the growing demand for short video applications on mobile phones. This paper proposes a new task called online highlight start detection (OHSD) in short video scenarios to assist in guiding the mobile phone to automatically capture highlights or other short video applications during the shooting process. The specific experiment is as below: (1) construct a short video dataset called Highlight45, which is carefully temporally labeled based on mobile phone shooting, to fill the gaps in training and evaluation data for new tasks; (2) set the online evaluation metric——the average precision of the first detection, and the visualization results show that this metric is more suitable for the online start detection task requirements; (3) design a hybrid dual-stream network with sequence contrastive loss function as the baseline method for this task. The experiments show that, compared with the traditional method, the proposed method has achieved 6.98% and 4.11% performance improvements in the existing start detection metric and the average precision of the first detection respectively.

Abstract:The spinal cord connects the brain to the peripheral nervous systemand plays an important role in encoding downstream brain commands and peripheral input signals during somatosensory and motor processes. Resolving the relationship between animal behavior and spinal cord neural activity through optical imaging remains a major challenge in neuroscience research. We have developed a method for in vivo imaging of the spinal cord in freely behaving mice using miniaturized two-photon fluorescence microscopy. The image quality and imaging stability of the method were evaluated by imaging the anterior spinal artery vessels, during spontaneous behavior and strenuous movements of freely behaving animals in an unrestrained environment. This physically solves the key problems in previous studies, such as image bias and loss due to the irregular motion of the spinal cord, and it achieves stable imaging of the spinal cordinfreely behaving mice based on miniaturized two-photon fluorescence microscopy. In addition, this method enables real-time imaging of individual neuronal calcium signals in the superficial neuronal activity of the spinal dorsal horn. This will provide technical support to explore the neuronal activity patterns in the spinal cord during somatosensory and motor processes, it’s important for advancing the study of spinal cordrelated functional neural networks.