Film Effect Optimization by Deep Learning and Virtual Reality Technology in New Media Environment
TL;DR: The quality and diversity of the specific types of images generated by the proposed GAN are improved compared with the current mainstream GAN method with supervision, which is in line with the subjective evaluation results of human beings.
Abstract: Today, new media technology has widely penetrated art forms such as film and television, which has changed the way of visual expression in the new media environment. To better solve the problems of weak immersion, poor interaction, and low degree of simulation, the present work uses deep learning technology and virtual reality (VR) technology to optimize the film playing effect. Firstly, the optimized extremum median filter algorithm is used to optimize the “burr” phenomenon and a low compression ratio of the single video image. Secondly, the Generative Adversarial Network (GAN) in deep learning technology is used to enhance the data of the single video image. Finally, the decision tree algorithm and hierarchical clustering algorithm are used for the color enhancement of VR images. The experimental results show that the contrast of a single-frame image optimized by this system is 4.21, the entropy is 8.66, and the noise ratio is 145.1, which shows that this method can effectively adjust the contrast parameters to prevent the loss of details and reduce the dazzling intensity. The quality and diversity of the specific types of images generated by the proposed GAN are improved compared with the current mainstream GAN method with supervision, which is in line with the subjective evaluation results of human beings. The Frechet Inception Distance value is also significantly improved compared with Self-Attention Generative Adversarial Network. It shows that the sample generated by the proposed method has precise details and rich texture features. The proposed scheme provides a reference for optimizing the interactivity, immersion, and simulation of VR film.
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14 Dec 2022
TL;DR: In this paper , an analysis of the topic "Adaptive 3D and VFX Films Virtual Learning" has been provided, where an adaptive learning environment is an environment that can dynamically adapt to the learner and the activities that can be performed by that specific learner.
Abstract: In this study, the analysis of the topic “Adaptive 3D and VFX Films Virtual Learning” has been provided. As virtual learning and 3D technologies use are increasing, the interest in their learning in academic discussion is increasing daily. However, there are various drawbacks to the use of3D for learning environments. To solve this drawback, the use of adaptive learning environments is increasing more, such as an environment that can dynamically adapt to the learner and the activities that can be performed by that specific learner. As the new ways of learning have been increasing over the past years (in the times of the COVID-19 Pandemic) through the use of computers in the educational sector. The learning environment has been widely adopted by the educational sectors in the case of obtaining promising outcomes. In recent years, these environments have evolved into more advanced environments with the implication of3D technology. With the help of 3D, these adaptive environments are helping learners according to their preferences.
TL;DR: This article put forward some solutions to the problems of college students' mental health management under the new media environment, including strengthening mental health education, guiding the rational use of new media and improving social skills.
Abstract: This paper puts forward some solutions to the problems of college students' mental health management under the new media environment. Firstly, through questionnaire survey and literature analysis, this paper discusses the problems existing in the management of college students' mental health, including increased psychological pressure, addiction to new media, social loneliness and so on. Secondly, the corresponding solutions are put forward, including strengthening mental health education, guiding the rational use of new media and improving social skills. Finally, the conclusion of this paper is summarized and some prospects for future research are put forward.
14 Dec 2022
TL;DR: In this article , an analysis of the topic "Adaptive 3D and VFX Films Virtual Learning" has been provided, where an adaptive learning environment is an environment that can dynamically adapt to the learner and the activities that can be performed by that specific learner.
Abstract: In this study, the analysis of the topic “Adaptive 3D and VFX Films Virtual Learning” has been provided. As virtual learning and 3D technologies use are increasing, the interest in their learning in academic discussion is increasing daily. However, there are various drawbacks to the use of3D for learning environments. To solve this drawback, the use of adaptive learning environments is increasing more, such as an environment that can dynamically adapt to the learner and the activities that can be performed by that specific learner. As the new ways of learning have been increasing over the past years (in the times of the COVID-19 Pandemic) through the use of computers in the educational sector. The learning environment has been widely adopted by the educational sectors in the case of obtaining promising outcomes. In recent years, these environments have evolved into more advanced environments with the implication of3D technology. With the help of 3D, these adaptive environments are helping learners according to their preferences.
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TL;DR: In this paper, the authors explored the performance of fuzzy system-based medical image processing for brain disease prediction, and designed a brain image processing and brain disease diagnosis prediction model based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation).
Abstract: The present work aims to explore the performance of fuzzy system-based medical image processing for brain disease prediction. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. While ensuring the model safety performance, a brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation). Brain MRI images collected from the Department of Brain Oncology, XX Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrated that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 seconds on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under the DSC coefficient, reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. To sum up, the proposed algorithm can provide higher accuracy while ensuring energy consumption, a more apparent denoising effect, and the best segmentation and recognition effect than other models, which can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.
179 citations
TL;DR: In this paper, a comprehensive bibliometric analysis of the field of smart learning environments has been conducted, focusing on the research trends, scholar's productivity, and thematic focus of scientific publications.
Abstract: This study examines the research landscape of smart learning environments by conducting a comprehensive bibliometric analysis of the field over the years. The study focused on the research trends, scholar’s productivity, and thematic focus of scientific publications in the field of smart learning environments. A total of 1081 data consisting of peer-reviewed articles were retrieved from the Scopus database. A bibliometric approach was applied to analyse the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of smart learning environments. The result from this bibliometric analysis indicates that the first paper on smart learning environments was published in 2002; implying the beginning of the field. Among other sources, “Computers & Education,” “Smart Learning Environments,” and “Computers in Human Behaviour” are the most relevant outlets publishing articles associated with smart learning environments. The work of Kinshuk et al., published in 2016, stands out as the most cited work among the analysed documents. The United States has the highest number of scientific productions and remained the most relevant country in the smart learning environment field. Besides, the results also showed names of prolific scholars and most relevant institutions in the field. Keywords such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning” remain the trending keywords. Furthermore, thematic analysis shows that “digital storytelling” and its associated components such as “virtual reality,” “critical thinking,” and “serious games” are the emerging themes of the smart learning environments but need to be further developed to establish more ties with “smart learning”. The study provides useful contribution to the field by clearly presenting a comprehensive overview and research hotspots, thematic focus, and future direction of the field. These findings can guide scholars, especially the young ones in field of smart learning environments in defining their research focus and what aspect of smart leaning can be explored.
53 citations
TL;DR: A system that accurately estimates a hand pose in 3D space using depth images for VR applications and an object-manipulating loss function, which considers knowledge of the hand-object interaction, to enhance performance are developed.
Abstract: Hand Pose Estimation aims to predict the position of joints on a hand from an image, and it has become popular because of the emergence of VR/AR/MR technology. Nevertheless, an issue surfaces when trying to achieve this goal, since a hand tends to cause self-occlusion or external occlusion easily as it interacts with external objects. As a result, there have been many projects dedicated to this field for a better solution of this problem. This paper develops a system that accurately estimates a hand pose in 3D space using depth images for VR applications. We propose a data-driven approach of training a deep learning model for hand pose estimation with object interaction. In the convolutional neural network (CNN) training procedure, we design a skeleton-difference loss function, which effectively can learn the physical constraints of a hand. Also, we propose an object-manipulating loss function, which considers knowledge of the hand-object interaction, to enhance performance. In the experiments we have conducted for hand pose estimation under different conditions, the results validate the robustness and the performance of our system and show that our method is able to predict the joints more accurately in challenging environmental settings. Such appealing results may be attributed to the consideration of the physical joint relationship as well as object information, which in turn can be applied to future VR/AR/MR systems for more natural experience.
23 citations
TL;DR: In this paper, the authors describe industrial co-creation projects as complex and ambiguous, involving high levels of interpretive uncertainty over processes and outcomes, and propose a method to boost the effectiveness of such projects.
Abstract: Industrial co-creation projects are often complex and ambiguous, involving high levels of interpretive uncertainty over processes and outcomes. To boost the effectiveness of such projects, firms ha ...
21 citations
TL;DR: The improved probabilistic Hough transform algorithm and the adaptive region growth algorithm based on Gaussian model are applied to detect lane lines and drivable regions, respectively and demonstrate the robust adaptation and real-time effectiveness of the approach under UVCs.
Abstract: The detection of lane lines and drivable regions is the basis for the development of advanced driving assistance systems. Aiming at the problem of the poor robustness of highway detection under unfavorable visual conditions (UVCs), a new road detection method based on the dynamic image enhancement algorithm is proposed. The classification of images under different UVCs is obtained using gray feature and definition feature, and the classification result is employed to select an appropriate enhancement algorithm. The definition parameters of the images, which are used to dynamically adjust the parameters of the image enhancement algorithm, are acquired based on the definition evaluation model. On this basis, the improved probabilistic Hough transform algorithm and the adaptive region growth algorithm based on Gaussian model are applied to detect lane lines and drivable regions, respectively. The experimental results demonstrate the robust adaptation and real-time effectiveness of the approach under UVCs.
16 citations