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A GPU-Accelerated 3D Visual Simulation Platform of X-Ray Digital Imaging Based on Unity

A GPU-Accelerated 3D Visual Simulation Platform of X-Ray Dig...

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international conference on computer and Automation Engineering, ICCAE

作者： Zhiyu Gao Jian Fu Junyi Lu Xianliang Gao Dept. of Mechanical Engineering and Automation Beihang University Beijing China Jiangxi Research Institute Nanchang China Ningbo Institute of Technology Ningbo China Shanghai Spaceflight Precision Machinery Institute Shanghai China

X-ray digital imaging is an advanced and widely used non-destructive testing technology, X-ray digital imaging system faces some problems in actual inspection such as high costs and radiation effects. computer simulation technology, which comprehensively applies mathematics, computer graphics and image processing technology, can solve these problems well and has its own advantages. At present, most of the research on X-ray digital imaging simulation has slow imaging speed, poor visibility and poor interaction. In this paper, we make a mathematical model for X-ray digital imaging and build a 3D visual simulation platform in Unity. By using GPU acceleration technology, the imaging speed is reduced to only a fraction of a second which can satisfy real-time interaction. This simulation platform has good demonstration and comfortable user experience based on the 3D visualization technology. This work will promote the applications of computer simulation technology in X-ray digital imaging technology.

关键词： Visualization Solid modeling Three-dimensional displays Digital images Computational modeling Graphics processing units Imaging

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A Comparative Analysis of Satellite image to Map image Translation Using GANs

A Comparative Analysis of Satellite Image to Map Image Trans...

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international conference on image Information processing

作者： V Uma Maheshwari Sakilam Abhiman Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

image-to-image translation is a challenging task that aims to convert an input image from one domain to another while preserving its semantic content. In this comparative study, we examine four state-of-the-art Generative Adversarial Network (GAN) architectures: Pix2Pix, CycleGAN, and DualGAN, for the specific task of translating satellite images to map types and examine how they are used in the classification of land cover, urban planning, disaster management, and environmental monitoring. Using several measures such as Inception score, Fréchet Inception Distance, Pixel Accuracy, and Structural Similarity, we assess how models are producing maps and compare their respective performances. This thorough comparative study aims to provide insights into the strengths and weaknesses of Pix2Pix, CycleGAN, and DualGAN in the context of satellite image-to-map translation. Our findings can aid researchers and practitioners in selecting the most appropriate GAN architecture for this task, ultimately advancing the field of image-to-image translation for satellite imagery applications.

关键词： Training Visualization Technological innovation Urban planning Land surface Disaster management Generative adversarial networks

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Enhancing image Clarity in Real Time: An Automated Gamma Correction Approach for Dehazing

Enhancing Image Clarity in Real Time: An Automated Gamma Cor...

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international conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Uma Biswas Aurgha Karmakar Avra Ghosh Sheli Sinha Chaudhuri ECE JGEC Jalpaiguri India ETCE Jadavpur University Kolkata India

Haze is a natural occurrence resulting from the scattering of unwanted particles in the atmosphere, leading to issues such as color distortion, decreased contrast, and diminished visual appeal in outdoor images. However, computer vision applications necessitate clear images for effective processing. This study proposes a simple and fast image dehazing technique that reduces time complexity by employing gamma correction with a dark channel prior-based technique. This reduction in time complexity enables real-time application. The proposed method enhances image contrast through automatically adjusts brightness levels using gamma correction method where the gamma value is calculated based on the average pixel value of the gray scale image and contrast modification technique to enhance the image quality to suit the image requirements. Experimental results demonstrate that the proposed method produces higher-quality images compared to existing technologies. The combination of gamma correction and contrast limited Adaptive Histogram Equalization proves to be an effective approach for image enhancement and dehazing tasks, resulting in improved contrast and finer details across various image types. Comparative analysis based on parameters such as SSIM, MSE, PSNR, and Correlation confirms the competitive performance of the proposed method.

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Stock Price Prediction using LSTM and TLBO

Stock Price Prediction using LSTM and TLBO

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Integrated Circuits and Communication Systems (ICICACS), ieee international conference on

作者： Kishore Kumar M Veena R C Ila Chandana Kumari. P Gandla Shivakanth Karunakara Rai B Department of CSE CMR Technical Campus Hyderabad India Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of Computer Science of Engineering Hyderabad Institute of Technology and Management Telangana India Dept of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The stock market's inherent complexity and volatility make accurate predictions difficult. Complex patterns in unstructured data may be simulated and analyzed with the help of Deep Learning. It is possible to employ deep learning models for a wide variety of purposes, including but not limited to image recognition, audio recognition, and natural language processing (NLP). As a result, everyone with a stake in the market is always trying to make educated guesses about what the stock price will do next. We used many deep learning techniques, including Long Short-Term Memory, to model our problem (LSTM). To successfully predict the closing prices of three distinct markets, an Extreme Learning Machine (ELM) model is combined with a variety of metaheuristic optimization techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Teaching Learning Based Optimization (TLBO), and Symbiotic Organism Search (SOS). These algorithms are helpful in creating ELM network weights and biases in the most effective way possible. The Teaching and Learning Based Optimization (TLBO) paradigm underpins most of the models and methodologies now in use for feature selection. To that end, this study sets out to identify the most effective machine learning model for making such predictions. The primary objective is to develop a smart technology that can predict the movement of stock market values based on financial time series and news headlines. Investors would be able to make more informed decisions based on the binary results of the proposed method. processing time, error rate, and accuracy in forecasting the direction of stock price movement for five companies across sectors are used to evaluate the performance of the proposed LSTM-TLBO model.

关键词： Deep learning Symbiosis image recognition Education Time series analysis Predictive models Prediction algorithms

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Disentangling Spatial and Temporal Learning for Efficient image-to-Video Transfer Learning

Disentangling Spatial and Temporal Learning for Efficient Im...

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international conference on computer Vision (ICCV)

作者： Zhiwu Qing Shiwei Zhang Ziyuan Huang Yingya Zhang Changxin Gao Deli Zhao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group ARC National University of Singapore

Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to video recognition still suffers from unsatisfactory temporal modeling capabilities. Existing methods insert tunable structures into or in parallel with the pre-trained model, which either requires back-propagation through the whole pre-trained model and is thus resource-demanding, or is limited by the temporal reasoning capability of the pre-trained structure. In this work, we present DiST, which disentangles the learning of spatial and temporal aspects of videos. Specifically, DiST uses a dual-encoder structure, where a pre-trained foundation model acts as the spatial encoder, and a lightweight network is introduced as the temporal encoder. An integration branch is inserted between the encoders to fuse spatio-temporal information. The disentangled spatial and temporal learning in DiST is highly efficient because it avoids the back-propagation of massive pre-trained parameters. Meanwhile, we empirically show that disentangled learning with an extra network for integration benefits both spatial and temporal understanding. Extensive experiments on five benchmarks show that DiST delivers better performance than existing state-of-the-art methods by convincing gaps. When pre-training on the large-scale Kinetics-710, we achieve 89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability of DiST. Codes and models can be found in https://***/alibaba-mmai-research/DiST.

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Research on Lightweight Improvement of YOLOv8-based Fire Detection Model for Edge Computing

Research on Lightweight Improvement of YOLOv8-based Fire Det...

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computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), ieee international conference on

作者： Huixiang Zhou Desheng Zhang Gaojun Hu Xin Gu School of Software. East China Jiaotong University Nanchang China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

In the field of fire detection, computational constraints and limited memory resources pose challenges for robots and edge-embedded devices. Furthermore, some lightweight models struggle with effectively detecting complex targets. To address these issues, this study proposes a lightweight enhancement for the YOLOv8 model. The approach involves replacing the original backbone network with EfficientNetV2 for feature extraction, incorporating weighted feature fusion, and introducing the Convolutional Block Attention Module (CBAM). The improved model undergoes experimentation using a self-constructed dataset for fire detection. Results indicate a significant reduction in network parameters and model inference time compared to the original YOLOv8 model, accompanied by an increase in the processing frame rate. Moreover, the enhanced model demonstrates a 1.9% improvement in the map@50 metric and a 1.2 % enhancement in map@50-95. This affirms that the improved model not only ensures superior detection performance but is also well-suited for applications in embedded devices and edge computing scenarios.

关键词： Performance evaluation YOLO Computational modeling image edge detection Memory management Feature extraction Task analysis

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GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-image Pre-training

GrowCLIP: Data-aware Automatic Model Growing for Large-scale...

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international conference on computer Vision (ICCV)

作者： Xinchi Deng Han Shi Runhui Huang Changlin Li Hang Xu Jianhua Han James Kwok Shen Zhao Wei Zhang Xiaodan Liang Sun Yat-sen University Huawei Noah’s Ark Lab University of Technology Sydney The Hong Kong University of Science and Technology

Cross-modal pre-training has shown impressive performance on a wide range of downstream tasks, benefiting from massive image-text pairs collected from the Internet. In practice, online data are growing constantly, highlighting the importance of the ability of pre-trained model to learn from data that is continuously growing. Existing works on cross-modal pre-training mainly focus on training a network with fixed architecture. However, it is impractical to limit the model capacity when considering the continuously growing nature of pre-training data in real-world applications. On the other hand, it is important to utilize the knowledge in the current model to obtain efficient training and better performance. To address the above issues, in this paper, we propose GrowCLIP, a data-driven automatic model growing algorithm for contrastive language-image pre-training with continuous image-text pairs as input. Specially, we adopt a dynamic growth space and seek out the optimal architecture at each growth step to adapt to online learning scenarios. And the shared encoder is proposed in our growth space to enhance the degree of cross-modal fusion. Besides, we explore the effect of growth in different dimensions, which could provide future references for the design of cross-modal model architecture. Finally, we employ parameter inheriting with momentum (PIM) to maintain the previous knowledge and address the issue of the local minimum dilemma. Compared with the existing methods, GrowCLIP improves 2.3% average top-1 accuracy on zero-shot image classification of 9 downstream tasks. As for zero-shot image retrieval, GrowCLIP can improve 1.2% for top-1 image-to-text recall on Flickr30K dataset.

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Research on Database processing System of Electromagnetic Environment Imaging Equipment of Intelligent Nuclear Power Plant

Research on Database Processing System of Electromagnetic En...

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international conference on Information Systems and computer Aided Education (ICISCAE)

作者： Tiecheng Yang Yu Wang Chun Hu Xincai Chang Maolin Huang Daya Bay Nuclear Power Operations and Management Co. Ltd. Guangdong China Shanghai Engineering Science & Technology Co. Ltd. CGNPC Beijing China

This paper proposes a scheme of space electromagnetic environment imaging monitoring system for nuclear power plant based on shared database technology. Firstly, the spectral data of field programmable gate array is collected and processed. In this method, the color query list of multi-wave bands is established to realize the color display of images with different wave bands. Real-time feedback of spectral data of magnetic field environment is realized by using shared database technology. The terminal display control system displays the electromagnetic spectrum distribution in space of the measured nuclear power plant in real time and intuitively in the form of spectral diagram and radiation heat map. The monitoring of electromagnetic spectrum extends from surface to stereo. The simulation results show that the simulation image can not only show the spatial distribution of each frequency band in the electromagnetic environment, but also reflect the common influence of each frequency band. This allows users to better understand their electromagnetic environment.

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Paste-and-Cut: Collective image Localization and Classification for Real-Time Multi-Camera Object Detection

Paste-and-Cut: Collective Image Localization and Classificat...

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international conference on Information and Communication Technology Convergence (ICTC)

作者： Young Eun Kang Woosung Kang Taehun Lee Hoon Sung Chwa Electronical Engineering and Computer Science DGIST Daegu Republic of Korea

In recent years, object detection has emerged as a crucial task in various real-world applications, including security surveillance, autonomous vehicles, and robotics. However, traditional object detection models face numerous challenges, such as inefficient image processing, inadequate resource utilization, and a failure to consider the different criticality of input images, making it difficult to apply these models for timely inferences in practical applications. To overcome these challenges, this paper proposes a novel object detection framework, called Paste-and-Cut, that utilizes two techniques, image merging (paste) and RoI patching (cut), to optimize resource utilization and improve object detection performance. Additionally, our approach incorporates a dynamic merge size and canvas size decision mechanism to adapt to varying object detection environments. Experimental results obtained from experiments conducted with the MOT dataset demonstrate the effectiveness of our approach in achieving real-time object detection with improved detection accuracy and without generating any deadline miss. As such, Paste-and-Cut provides a promising solution for efficient and accurate real-time object detection in multi-camera scenarios.

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Clustering Based Segmentation with 1D-CNN Model for Grape Fruit Disease Detection

Clustering Based Segmentation with 1D-CNN Model for Grape Fr...

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Integrated Circuits and Communication Systems (ICICACS), ieee international conference on

作者： Piyush Kumar Pareek Ramya I M Jagadeesh B N LeenaShruthi H M Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

There has been a lot of focus on agricultural image dispensation research in the previous years. computers employ image processing procedures to do picture analysis. Recent technological advances in the fields of picture imprisonment and data dispensation have greatly facilitated the solution of several agricultural issues. Identification and categorization of crop diseases are essential to the agricultural sector's technical and economic health. The first step in agricultural image processing is acquiring a digital colour photograph of a sick leaf. Grapes have the highest global per capita fruit consumption. It is unacceptable to let the spread of grape leaf contamination in the grapefruit crop to go unchecked, since this would be detrimental to the industry's growth. Farmers in rural regions sometimes lack the skills to spot these illnesses early on, which leads to poor crop yields. In order to detect diseases in grapefruit leaves, the suggested system employs a deep learning-based categorization method. K-means clustering is first used to segregate the backgrounds of the input datasets. The Convolutional Neural Network (CNN) is then fed the segmented regions to get a disease classification. The suggested work utilises the hyperparameter tuning operation with the assistance of Firefly with cyclic randomization to enhance the classification accuracy (FCR). The accuracy, f-measure, recall, and precision of the proposed model are evaluated in light of the experimental data. Finally, compared to the current models, which obtained accuracies of 88% to 90% for grape fruit disease detection, the suggested CNN model achieved 95% accuracy and 92% precision.

关键词： computers image segmentation Computational modeling Pipelines Crops Real-time systems Convolutional neural networks

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