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Multi-viewpoint Rendering Optimization of Indoor Scene Based on Binocular vision 1

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3rd EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2021

作者： Jing, He School of Road Bridge and Architecture Chongqing Vocational College of Transportation Chongqing402247 China

ISBN: (数字)9783030825652

ISBN: (纸本)9783030825645

The traditional multi-viewpoint rendering method for indoor scenes has poor lighting and shadow effects, resulting in too dark or bright indoor scene multi-viewpoint rendering. Therefore, an optimization method for indoor scene multi-viewpoint rendering based on binocular vision is proposed. This research plans light and shadow effects based on the visual relationship between point light sources and indoor scenes;sets rendering points based on binocular vision;and renders indoor scenes with multiple viewpoint angles. The simulation experiment results show that compared with the traditional rendering method, the light and shadow effect of the studied method is excellent, and the rendered indoor scene is suitable for light and dark. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Binocular vision

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Determining on-shelf availability based on RGB and ToF depth cameras 23

Determining on-shelf availability based on RGB and ToF depth...

引用

23rd international conference on control Systems and computer Science Technologies, CSCS 2021

作者： Craciunescu, Mihai Baicu, Diana Mocanu, Stefan Dobre, Cristian Faculty of Automatic Control and Computer Science Department of Automatic Control and Applied Informatics Bucharest Romania Rinftech Bucharest Romania

ISBN: (纸本)9781665439398

In this paper, a method of calculating the occupancy of a shelf will be presented. A vision pillar composed of two RGB cameras and two ToF depth cameras will be used to scan a shelf and determine the percentage of emptiness for the products. Since the cameras will scan a small section of the shelf at the time, a stitching method will be applied on both depth and RGB stream. In order to identify categories of products, along with their spatial delimitation within a shelf, a segmentation neural network is used. By combining the segmentation output with the depth information, the distance from the pillar to the product can be determined. Since this type of information does not help a human operator or a supervisor in understanding the state of the shelf or in making an appropriate decision, such as restocking, a method for computing the shelf occupancy is proposed. Optimization must be done to the processing algorithms to enable them to run on an embedded platform, which will allow for the implementation of the proposed architecture on a platform deployed in a production environment. All the computation is done on board of the mobile robot without transmitting the data to an external server. © 2021 IEEE.

关键词： Cameras

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Deep Transfer Learning Approach for Facial and Verbal Disease Detection 3

Deep Transfer Learning Approach for Facial and Verbal Diseas...

引用

3rd international conference on Advancements in Computing, ICAC 2021

作者： Manage, Dilki Madara Alahakoon, A. M. Ishan Shavinda Weerathunga, Kavindi Weeratunga, Thenuka Lunugalage, Dilani De Silva, Hansi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Millions of people have been subjected to different kind of acute diseases, some of them are eye diseases, facial skin diseases, tongue diseases and voice abnormalities. Most of eye diseases cause fully or partial blindness. Skin and tongue complications can be signs of cancers. Voice abnormalities can be cured at initial stages. Well-practiced medical practitioners have the ability of diagnose these diseases, but due to the pandemic situations and high consultation costs people do not tend to consult doctors. This research is predominantly focused on development of an application for automatic detection of eye, skin, tongue and verbal diseases using transfer learning (TL) based deep learning (DL) approach. Deep learning is a part of machine learning (ML) which has been used in most computer vision approaches. Transfer learning has been used to rebuild the existing convolutional neural network (CNN) models and used in disease detection. DenseNet121, MobileNetV2, RestNet152V2, models have been used to detect eye, skin and tongue diseases respectively and a new model has been used to detect voice abnormalities. CNN models are capable of automatically extracting features from the given images and voice data. All the trained models have been given accuracy rate of 80%-95%. © 2021 IEEE.

关键词： Convolutional neural networks

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Scene Text Detection Based on Bi-directional Feature Pyramid Network

Scene Text Detection Based on Bi-directional Feature Pyramid...

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international conference on Artificial Intelligence and computer Information Technology (AICIT)

作者： Changshi Qi Qiong Li School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

A scene text detection algorithm based on bidirectional feature pyramid network is extracted to address the problem of inaccurate text region detection caused by text of different sizes and shapes in natural scenes. Firstly, the ordinary convolution is replaced by variability convolution in the backbone network, which can better locate the region of interest, and then BIFPN is introduced to remove the unnecessary branches in it as a feature fusion network to fuse the feature information on different scales, and finally differentiable binarisation is used to generate the text region. The proposed method is experimented on the publicly available datasets ICDAR2015, MSRA-TD500, and Total-Text dataset, and its F-value reaches 87.4%, 86.5%, and 85.6%, respectively. The experimental results show that the method has good detection performance on natural scene text.

关键词： Shape Fuses Convolution Computational modeling Semantics Text detection Bidirectional control Feature extraction Information technology Artificial intelligence

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The Bayesian CNN-LSTM Mixed Hybrid Algorithm Model of the Photovoltaic Short-term Output Forecasting

The Bayesian CNN-LSTM Mixed Hybrid Algorithm Model of the Ph...

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New Energy and Power Engineering (ICNEPE), international conference on

作者： Enchong Dong Yuxi Guo School of Control and Computer Engineering North China Electric Power University Beijing China Shenzhen College of International Education Shenzhen China

Due to the volatility, randomness, and intermittency of photovoltaic power generation, it is difficult to accurately forecast its output. This paper proposes a Bayesian-optimized CNN-LSTM mixed neural model for a short-term photovoltaic output prediction model. First of all, the Xinjiang photovoltaic power station dataset is selected and preprocessed in this study. Secondly, the CNN method is applied for data feature extraction. And then the Bayesian optimization algorithm is used to analyze the LSTM super parameter optimization. Finally, the data, after undergoing feature extraction, are input into the Bayes theorem-LSTM model for forecast generation. The results show that the proposed algorithm has realized the accurate prediction of photovoltaic power, getting better than most other single benchmark models. These findings not only highlight the improved efficacy of the proposed algorithm but also affirm its potential applicability and value for practical implementation.

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Multi-Path Attention Block-Loss Function for Non-Uniform Blind Image Deblurring

Multi-Path Attention Block-Loss Function for Non-Uniform Bli...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Nagendar Yamsani Pradeep Kumar S Kassem AL-Attabi Zainab Alassedi Abbas Hameed Abdul Hussein School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic university Najaf Iraq National University of Science and Technology Dhi Qar Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

Blind image deblurring is a significant and difficult task in the computer vision field. It is mainly focused on decreasing the blur caused by motion or camera shake. However, image deblurring has made great progress, but there is a need for enhancement in the visual effect and image information to enhance the overall image quality. In this research, the Multi-Path Attention Block-Loss Function (MPAB-LF) is proposed for non-uniform blind image deblurring to enhance image quality. The MPAB approach is employed to provide the foundation for blind image deblurring. To combine numerous MPABs instead of employing associated feature aggregation, the Improved One-Shot Aggregation (IOSA) is utilized. Finally, the loss functions are employed to enhance the generator and discriminator of the framework. Existing approaches like Generative Adversarial Network (GAN), MPAB, Two Convolutional Neural Networks (CNN), and Dense Dilated Block and Improved Attention Module (DDBIAM) are used to compare with the proposed MPAB-FL approach. The proposed MPAB-FL achieves a better Peak-to-Signal Noise Ratio (PSNR) of 35.65 dB and Structural Similarity Index (SSIM) of 0.9957 compared to the existing approaches like GNN, MPAB, Two-stage CNN, and DDBIAM respectively.

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An Evaluation of User Experience in a Virtual Semiconductor Laboratory Utilizing HMDs during Covid-19 Pandemic

An Evaluation of User Experience in a Virtual Semiconductor ...

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Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Ahmed Jamah Ahmed Alnagrat Abdalslam S Imhmed Mohamed Abubaker Almintisir Abubaker Akeel Rizalafande Che Ismail Ababaker Mohamed Ibrahim Noureldeen Syed Zulkarnain Syed Idrus Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Wadi al-Shati Libya Faculty of Information Technology Aljufra University Aljufra Libya Department of Mathematics and Computer Faculty of Education Wadi Alshatti University Wadi Alshatti Libya Faculty of Electronic Engineering Technology (FTKEN) Universiti Malaysia Perlis (UniMAP) Arau Malaysia Department of Communication Faculty of Business & Communication (FPK) Universiti Malaysia Perlis (UniMAP) Arau Malaysia

Virtual reality (VR) technology has been increasingly adopted for creating engaging educational environments, such as virtual laboratories. This study evaluated the user experience of a virtual semiconductor laboratory environment, utilizing Reverb G2 HMD VR headsets, through a user experience questionnaire (UEQ-S) and a psychometric analysis of its 26 items. Results showed that most participants found the virtual laboratories enjoyable, understandable, creative, easy to learn, and valuable. The psychometric analysis revealed relatively consistent and reliable construct related to user experience. The Kruskal-Wallis H test demonstrated that participants perceived the VR headsets to be easier to use when they found the virtual laboratories more enjoyable. However, there were no significant differences in user experience scores based on preferred VR headset, and user experience did not predict headset preference. These findings suggest that VR technology presents a promising solution for addressing practical challenges in education and fostering student engagement, although further research is needed to optimize the user experience and enhance student motivation.

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Deep Fusion: A CNN-LSTM Image Caption Generator for Enhanced Visual Understanding

Deep Fusion: A CNN-LSTM Image Caption Generator for Enhanced...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Chandradeep Bhatt Sumit Rai Rahul Chauhan Deepika Dua Mukesh Kumar Sanjay Sharma CSE Department Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Hill University Dehradun India Department of School of Computing Uttranchal University Dehradun India Computer Application Department SGRRITS Dehradun India

Photo caption generators had gained huge recognition recently from the areas of NLP and computer vision. These generators produce captions that explain an image's content using deep learning algorithms. A sizable collection of image-caption pairs is used to train the algorithm. The photos are fed into a pre-trained CNN after the dataset has undergone preprocessing to extract its visual properties. Along with the picture features, the necessary captions are tokenized and supplied to the LSTM decoder. In order to accelerate the caption generation process, the LSTM decoder is trained to utilize beam search and maximum likelihood estimation methods. To assess the effectiveness of the suggested model, several metrics such as BLEU and METEOR are employed. The suggested CNN-LSTM image caption generator has a lot of promise for use in a variety of contexts, such as picture comprehension, information retrieval, and assistive technology for the blind. Combining CNN and LSTM models takes advantage of each architecture's capabilities, allowing the model to produce insightful and contextually appropriate captions for a variety of images. We also explore present difficulties and potential directions for additional investigation in this area. This research study concludes by presenting an innovative method for creating image captions that combines the strength of CNN and LSTM. The experimental outcomes demonstrate the efficacy and reliability of the suggested model, highlighting its potential to advance the field of image captioning and contribute to the creation of intelligent systems that can comprehend visual content and produce precise and insightful textual descriptions.

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Design and Implementation of Renewable Energy Applications Based Bi-Directional Buck-Boost Converter

Design and Implementation of Renewable Energy Applications B...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： K. Venkata Ramana S. Arulkumar Asmita Marathe Kedir Beshir V Jaiganesh K. Tamilselvi M. Sudhakar Department of Computer Scinece And Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India Department of Electrical And Computer Engineering Wachemo University Centre For Energy Studies Department of Technology Savitribai Phule Pune University Pune India Department of Electrical And Computer Engineering Wachemo University Ethiopia Department of Mechanical Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Computer science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Sri Sai Ram Engineering College Chennai India

Our main focus was on creating a Two directional power converter for a standalone photo-voltaic energy-generating technology as well as technology energy control when a lead-acid energy storage device is used to control the energy supply. We utilized a light bulb as the load to evaluate the effectiveness of the directional power converter developed as part of our project as well as the functionality of energy control technologies. The suggested technology was composed of an energy control technology, a lead-acid battery, a bulb, a two-directional buck-boost converter, an utmost power point tracking controller, and these components. An energy control technique was developed to grow the rate at which the Photo-voltaic power producing technology consumed energy. The circuits are intended to charge the battery between upper and lower voltage limits, as well as to continuously check the battery's state of charge and add or release current as necessary. The bidirectional buck-boost converter's ability to function as a charge controller on its own, along with the use of particular BUCK and BOOST converter properties to optimize the home application, is the primary distinction between the method used in the proposed technology and other techniques used in the past. The battery is charged and discharged using a bidirectional dc-dc converter. Measurement data are employed to support the viability of a photovoltaic air conditioning technology.

关键词： Lead acid batteries Photovoltaic systems Air conditioning Renewable energy sources Switches DC-DC power converters Bidirectional control

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Research on the Scoring Algorithm of Throwing Effect Based on Visual Motion Sensing Technology

Research on the Scoring Algorithm of Throwing Effect Based o...

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Sensor Technology and control (ISSTC), international Symposium on

作者： Fuyong Liu Ding Wang Minrui Hou Xinjiang college of science & technology School of Information Science and Engineering Korla the Xinjiang Uygur Autonomous Region China Liyuan Town people’s government Tangshan Hebei Province China

ISBN: (数字)9798331541637

ISBN: (纸本)9798331541644

vision-based human-computer interaction technology, namely visual motion sensing technology, has made many application achievements. In terms of entertainment, there are many pioneering applications. Aiming at the combination of visual motion sensing technology and throwing game, this paper studies the throwing scoring algorithm of the motion sensing throwing target system for *** RGB-D target detection, get the detection frame coordinates of the face and hand at the end of the throwing, extract the depth information of the center point of the detection box, calculate the movement speed of the center point of the hand at the end of the throwing, compared with the lowest throwing speed, determine whether the throwing missed the target, and the distance between the center point is calculated, and the accuracy of the throwing is judged according to the distance of the two. A difficulty coefficient is designed according to the distance of the throw, comparing the difficulty coefficient to the number of cycles in last throw are multiplied to give the final score. The effectiveness of the algorithm is verified by comparing experiments with real throwing situations.

关键词： Hands Human computer interaction Visualization Accuracy Entertainment industry Object detection Games Sensors Data mining Faces

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