Virtual Reality Intelligent Hardware最新文献

Background

Petrochemical products are flammable, explosive, and toxic, petrochemical accidents are generally extremely destructive. Therefore, disaster analysis and prediction and real-time simulation have become important means to control and reduce accident hazards.

Methods

In this study, a complete real-time simulation solution of gas diffusion with coordinate data and concentration data is proposed, which is mainly aimed at the simulation of the types of harmful gas leakage and diffusion accidents in the petrochemical industry. The rendering effect is more continuous and accurate through grid homogenization and trilinear interpolation. A data processing and rendering parallelization process is presented to improve simulation efficiency. Combines gas concentration and fragment transparency to synthesize transparent pixels in a scene. To ensure the approximate accuracy of the rendering effect, improve the efficiency of real-time rendering, and meet the requirement of intuitive perception using concentration data, a weighted blended order-independent transparency with enhanced alpha weight is presented, which can provide a more intuitive perception of hierarchical information of concentration data while preserving depth information. In this study, three order-independent transparency algorithms, depth peeling algorithm, weighted blended order-independent transparency and weighted blended order-independent transparency with enhanced alpha weight, are compared and analyzed for rendering image quality, rendering time, required memory, hierarchical information and so on.

Results

Using weighted blended order-independent transparency with enhanced alpha weight technique, the rendering time is shortened by 53.2% compared with the depth peeling algorithm, and the texture memory required is much smaller than the depth peeling algorithm. The rendering results of weighted blended order-independent transparency with enhanced alpha weight are approximately accurate compared with the depth peeling algorithm as ground truth, and there is no popping when surfaces pass through one another. At the same time, compared with weighted blended order-independent transparency, weighted blended OIT with enhanced alpha weight achieves an intuitive perception of hierarchical information of concentration data.

Background

There are many regular-shape objects in the artificial environment. It is difficult to distinguish the poses of these objects, when only geometric information is utilized. With the development of sensor technologies, we can utilize other information to solve this problem.

Methods

We propose an algorithm to register point clouds by integrating color information. The key idea of the algorithm is that we jointly optimize dense term and edge term. The dense term is built similarly to iterative closest point algorithm. In order to build the edge term, we extract the edges of the images obtained by projecting the point clouds. The edge term prevents the point clouds from sliding in registration. We utilize this loosely coupled method to fuse geometric and color information.

Results

The experiments demonstrate that edge image approach improves the precision and the algorithm is robust.

With more and more vehicles becoming autonomous, intelligent, and connected, paying attention to the future usage of car human-machine interface (HMI) with these vehicles should also get more relevant. While car HMI has been addressed in several scientific studies, little attention is being paid to designing and implementing interactive glazing into everyday (autonomous) driving contexts. Through reflecting on what was found before in theory and practice, we describe an engineering psychology practice and the design of six novel future user scenarios, which envision the application of a specific set of augmented reality (AR) support user interactions. We also present evaluations conducted with the scenarios and experiential prototypes and found that these AR scenarios support our target user groups in experiencing a new type of interactions. The overall evaluation was positive, with some valuable assessment results and suggestions. We envision that this paper will interest applied psychology educators who aspire to teach how to operationalize AR in a human-centered design (HCD) process to students with little preexisting expertise or little scientific knowledge about engineering psychology.

Background

Image matching is crucial in numerous computer vision tasks such as 3D reconstruction and simultaneous visual localization and mapping. The accuracy of the matching significantly impacted subsequent studies. Because of their local similarity, when image pairs contain comparable patterns but feature pairs are positioned differently, incorrect recognition can occur as global motion consistency is disregarded.

Methods

This study proposes an image-matching filtering algorithm based on global motion consistency. It can be used as a subsequent matching filter for the initial matching results generated by other matching algorithms based on the principle of motion smoothness. A particular matching algorithm can first be used to perform the initial matching; then, the rotation and movement information of the global feature vectors are combined to effectively identify outlier matches. The principle is that if the matching result is accurate, the feature vectors formed by any matched point should have similar rotation angles and moving distances. Thus, global motion direction and global motion distance consistencies were used to reject outliers caused by similar patterns in different locations.

Results

Four datasets were used to test the effectiveness of the proposed method. Three datasets with similar patterns in different locations were used to test the results for similar images that could easily be incorrectly matched by other algorithms, and one commonly used dataset was used to test the results for the general image-matching problem. The experimental results suggest that the proposed method is more accurate than other state-of-the-art algorithms in identifying mismatches in the initial matching set.

Conclusions

The proposed outlier rejection matching method can significantly improve the matching accuracy for similar images with locally similar feature pairs in different locations and can provide more accurate matching results for subsequent computer vision tasks.

Background

The use of micro-expression recognition to recognize human emotions is one of the most critical challenges in human-computer interaction applications. In recent years, cross-database micro-expression recognition (CDMER) has emerged as a significant challenge in micro-expression recognition and analysis. Because the training and testing data in CDMER come from different micro-expression databases, CDMER is more challenging than conventional micro-expression recognition.

Methods

In this paper, an adaptive spatio-temporal attention neural network (ASTANN) using an attention mechanism is presented to address this challenge. To this end, the micro-expression databases SMIC and CASME II are first preprocessed using an optical flow approach, which extracts motion information among video frames that represent discriminative features of micro-expression. After preprocessing, a novel adaptive framework with a spatiotemporal attention module was designed to assign spatial and temporal weights to enhance the most discriminative features. The deep neural network then extracts the cross-domain feature, in which the second-order statistics of the sample features in the source domain are aligned with those in the target domain by minimizing the correlation alignment (CORAL) loss such that the source and target databases share similar distributions.

Results

To evaluate the performance of ASTANN, experiments were conducted based on the SMIC and CASME II databases under the standard experimental evaluation protocol of CDMER. The experimental results demonstrate that ASTANN outperformed other methods in relevant crossdatabase tasks.

Conclusions

Extensive experiments were conducted on benchmark tasks, and the results show that ASTANN has superior performance compared with other approaches. This demonstrates the superiority of our method in solving the CDMER problem.

Mixed Reality (MR) video fusion system fuses video imagery with 3D scenes. It makes the scene much more realistic and helps the users understand the video contents and temporalspatial correlation between them, thus reducing the user’s cognitive load. Nowadays, MR video fusion has been used in various applications. However, video fusion systems require powerful client machines because video streaming delivery, stitching, and rendering are computation-intensive. Moreover, huge bandwidth usage is also another critical factor that affects the scalability of video fusion systems. The framework proposed in this paper overcomes this client limitation by utilizing remote rendering. Furthermore, the framework we built is based on browsers. Therefore, the user could try the MR video fusion system with a laptop or even pad, no extra plug-ins or application programs need to be installed. Several experiments on diverse metrics demonstrate the effectiveness of the proposed framework.

Background

Adequate-data collection could enhance the realism of surface texture haptic online-rendering or offline-playback. A parallel challenge is how to reduce communication delays and improve storage space utilization.

Methods

Based on the similarity of the short-term amplitude spectrumtrend, this paper proposes a frequency-domain compression method. A compression framework is designed, firstly to map the amplitude spectrum into a trend similarity grayscale image, compress it with the stillpicture-compression method, and then to adaptively encode the maximum amplitude and part of the initial phase of each time-window, achieving the final compression.

Results

The comparison between the original signal and the recovered signal shows that when the time-frequency similarity is 90%, the average compression ratio of our method is 9.85% in the case of a single interact point. The subjective score for the similarity reached an excellent level, with an average score of 87.85.

Conclusions

Our method can be used for offline compression of vibrotactile data. For the case of multi-interact points in space, the trend similarity grayscale image can be reused, and the compression ratio is further reduced.

Background

Using remote photoplethysmography (rPPG) to estimate blood volume pulse in a non-contact way is an active research topic in recent years. Existing methods are mainly based on the single-scale region of interest (ROI). However, some noise signals that are not easily separated in single-scale space can be easily separated in multi-scale space. In addition, existing spatiotemporal networks mainly focus on local spatiotemporal information and lack emphasis on temporal information which is crucial in pulse extraction problems, resulting in insufficient spatiotemporal feature modeling.

Methods

This paper proposes a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution and dimension separable attention. First, in order to solve the problem of single-scale ROI, we construct a multi-scale feature space for initial signal separation. Secondly, separable spatiotemporal convolution and dimension separable attention are designed for efficient spatiotemporal correlation modeling, which increases the information interaction between long-span time and space dimensions and puts more emphasis on temporal features.

Results

The signal-to-noise ratio (SNR) of the proposed network reaches 9.58 dB on the PURE dataset and 6.77 dB on the UBFC-rPPG dataset, which outperforms state-of-the-art algorithms.

Conclusions

Results show that fusing multi-scale signals generally obtains better results than methods based on the only single-scale signal. The proposed separable spatiotemporal convolution and dimension separable attention mechanism contributes to more accurate pulse signal extraction.

This work aims to build a comprehensive and effective fire emergency management system based on the Internet of Things (IoT) and achieve an actual intelligent fire rescue. A smart fire protection information system was designed based on the IoT. A detailed analysis was conducted on the problem of rescue vehicle scheduling and the evacuation of trapped persons in the process of fire rescue. The intelligent fire visualization platform based on the three-dimensional (3D) Geographic Information Science (GIS) covers project overview, equipment status, equipment classification, equipment alarm information, alarm classification, alarm statistics, equipment account information, and other modules. The live video accessed through the visual interface can clearly identify the stage of the fire, which facilitates the arrangement of rescue equipment and personnel. The vehicle scheduling model in the system primarily used two objective functions to solve the Pareto Non-Dominated Solution Set Optimization: emergency rescue time and the number of vehicles. In addition, an evacuation path optimization method based on the Improved Ant Colony (IAC) algorithm was designed to realize the dynamic optimization of building fire evacuation paths. The experimental results indicate that all the values of detection signals were significantly larger in the smoldering fire scene at t = 17s than the initial value. In addition, the probability of smoldering fire and the probability of open fire were relatively large according to the probability function of the corresponding fire situation, demonstrating that this model could detect fire. The IAC algorithm reported here avoided the passages near the fire and spreading areas as much as possible and took the safety of the trapped persons as the premise when planning the evacuation route. Therefore, the IoT-based fire information system has important value for ensuring fire safety and carrying out emergency rescue and is worthy of popularization and application.