Stabilization and visual analysis of video-recorded sailing sessions

One common way to aid coaching and seek to improve athletes’performance is by recording training sessions for posterior analysis.In the case of sailing,coaches record videos from another boat,but usually rely on handheld devices,which may lead to issues with the footage and missing important moments.On the other hand,by autonomously recording the entire session with a fixed camera,the analysis becomes challenging owing to the length of the video and possible stabilization issues.In this work,we aim to facilitate the analysis of such full-session videos by automatically extracting maneuvers and providing a visualization framework to readily locate interesting moments.Moreover,we address issues related to image stability.Finally,an evaluation of the framework points to the benefits of video stabilization in this scenario and an appropriate accuracy of the maneuver detection method.

A New Mode of Enterprise Management -Value Enterprise Management

In the development of modern enterprises,the management mode is the main factor that determines the height of its development,so it is highly valued.The traditional management mode has many disadvantages,which can not comply with the development trend of the current market economy,which leads to the slow improvement of enterprise management level,which is not conducive to the operation and development of enterprises.The proposal of the concept of value-oriented enterprise management has become a new mode of enterprise management,which is helpful to speed up the reform process of enterprises and enhance their development vitality.Therefore,we should clarify the key points of the construction of the model and gradually improve the level of management.This paper analyzes the concept of value enterprise management,puts forward the application status and classification of value enterprise management model,and explores the construction strategy of value enterprise management model.

JMNet: A joint matting network for automatic human matting

We propose a novel end-to-end deep learning framework, the Joint Matting Network(JMNet), to automatically generate alpha mattes for human images.We utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module,which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques;the shared encoder leads to better performance and lower memory costs.Our model can process real images downloaded from the Internet for use in composition applications.

3D floor plan recovery from overlapping spherical images

We present a novel approach to automatically recover, from a small set of partially overlapping spherical images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. We introduce several improvements over previous approaches based on color and spatial reasoning exploiting Manhattan world priors. In particular, we introduce a new method for geometric context extraction based on a 3D facet representation,which combines color distribution analysis of individual images with sparse multi-view clues. We also introduce an efficient method to combine the facets from different viewpoints in a single consistent model, taking into the reliability of the facet information. The resulting capture and reconstruction pipeline automatically generates 3D multi-room environments in cases where most previous approaches fail, e.g., in the presence of hidden corners and large clutter, without the need for additional dense 3D data or tools. We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes. Our test data is available to allow further studies and comparisons.

Vox Link—Combining sparse volumetric data and geometry for efficient rendering

Processing and visualizing large scale volumetric and geometric datasets is mission critical in an increasing number of applications in academic research as well as in commercial enterprise. Often the datasets are, or can be processed to become, sparse.In this paper, we present Vox Link, a novel approach to render sparse volume data in a memory-efficient manner enabling interactive rendering on common, offthe-shelf graphics hardware. Our approach utilizes current GPU architectures for voxelizing, storing, and visualizing such datasets. It is based on the idea of perpixel linked lists(pp LL), an A-buffer implementation for order-independent transparency rendering. The method supports voxelization and rendering of dense semi-transparent geometry, sparse volume data, and implicit surface representations with a unified data structure. The proposed data structure also enables efficient simulation of global lighting effects such as reflection, refraction, and shadow ray evaluation.

A novel parallel algorithm for computing the mooring line based on lumped-mass method

Dynamic modeling and simulation of the mooring system are the key technologies in anchor handling simulator(AHS).Built up the mooring line’s dynamics model based on lumped-mass method(LMM),and fourth-order Runge–Kutta method was used to solve the model;because of the huge amounts of calculation in the model’s solving,the very time-consuming process brings great impact on the real-time,fidelity and immersed feeling in the anchor handling scene simulation,seriously hindered its application in AHS.A novel parallel algorithm was proposed to speed-up the model’s solving process by taking the advantages of graphic processing units(GPU’s)massive parallel computing and float point computing capability.The model’s solving process was implemented on vertex shader based on the transform feedback(TF)mechanism in modern GPU.Experimental results show that,the new algorithm reduced the calculating time largely without losing accuracy,and can finally realize the real-time solving and simulation.