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Study on Optimization of Computer image Structure in Wushu Performance Training Model

International Journal of High Speed Electronics and systems 2025年第2期34卷

作者： Li, Li PE Department of Shaanxi Xueqian Normal University Shaanxi Xueqian Normal University Shanxi Xi’an710100 China

With the vigorous development of computer technology, its application in various fields is increasingly deepening, especially in the application of martial arts performance training models. This paper focuses on the practical application and far-reaching impact of computer image structure optimization in martial arts performance training models. Computer image structure optimization is a multidisciplinary technique that involves image processing, computer vision, and machine learning, aiming to optimize the visual effects and quality of images through algorithms and models. In the training model of martial arts performance, computer image structure optimization technology plays a crucial role. By utilizing advanced technologies such as color management, light and shadow processing, motion estimation and compensation, the clarity, smoothness, and coherence of martial arts performance images can be effectively improved, thereby enhancing the artistic effect and viewing value of martial arts performances. The study delved into the application effect of computer image structure optimization in martial arts performance training models. Through empirical research, we have found that optimized images can not only more accurately display the details and techniques of martial arts movements, but also create a more stunning performance atmosphere. Meanwhile, computer image structure optimization technology can also provide more accurate and efficient feedback mechanisms for martial arts performance training, helping athletes better adjust and improve their movements. © World Scientific Publishing Company.

关键词： Motion estimation

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Hyperspectral image processing with Machine Learning and Mathematical Models 7th

Hyperspectral Image Processing with Machine Learning and Ma...

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7th International conference on Soft Computing and Signal processing, ICSCSP 2024

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

ISBN: (纸本)9789819799251

Hyperspectral imaging is a fast-growing imaging technique in many fields, like remote sensing, fruit analysis, clinical images, etc. The spectral image consists of two parts: spectral data and spatial data. The processing of spectral images includes the processing of the spatial part as well as the spectral part. This paper analyzes various methods and algorithms that are used for spectral image processing in existing systems and proposes a diagram for hyperspectral image processing. There are five important steps for processing the spectral image and analyzing the region selection and dimensionality reduction methods in detail. Comparing the performance of region selection and dimensionality reduction techniques, the Rembg algorithm and Principal Component Analysis (PCA), respectively, show very good results. A well processed image has better performance in the classification and regression steps, and it reduces the computational time and cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Principal component analysis

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image Deblurring Based on Normalized-weighted Total Variation 8

Image Deblurring Based on Normalized-weighted Total Variatio...

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8th Annual International conference on Network and Information systems for Computers, ICNISC 2022

作者： Zhou, Errui Yan, Ming Liu, Lu Li, Gang Guo, Mingan Yang, Shaohua Li, Binkang Northwest Institute of Nuclear Technology State Key Laboratory of Intense Pulsed Radiation Simulation and Effect Xi'an China

ISBN: (纸本)9781665453516

image deblurring is an important and challenging problem in imaging processing. It aims to restore clear images from degenerated ones caused by camera shake or target motion. The total variation (TV) regularization has been widely used in image deblurring, which needs to be carefully modified because of over-smoothing issue and solution bias caused by the homogeneous penalization. In this paper, a non-blind image deblurring method based on normalized-weighted TV (NWTV) is proposed. The method utilizes a weight vector to indicate the importance of image gradients, and the weight vector is normalized in a range between 0 and 1. The NWTV method can perform well in images with sparse or dense gradients. The performance of NWTV has been compared with several state-of-the-art image deblurring methods including MPTV, TV-ADMM, LO-IG and INSR. Experimental results demonstrate that the proposed method achieves comparative or better performance. © 2022 IEEE.

关键词： image enhancement

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LW-YOLOv8: An Lightweight Object Detection Algorithm for UAV Aerial imagery

LW-YOLOv8: An Lightweight Object Detection Algorithm for UAV...

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Natural Language processing (ICNLP), International conference on

作者： Hu Chen Dianwei Wang Jie Fang Yuanqing Li Sidi Xu Zhijie Xu School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Computing and Engineering University of Huddersfield Huddersfield UK

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Deep learning is vital for improving drone intelligence through UAV aerial object detection. However, current algorithms struggle to balance detection speed and accuracy. To address this issue, in this paper we propose a lightweight UAV aerial image object detection algorithm based on YOLOv8 named LW-YOLOv8. Firstly, the lightweight VanillaNet architecture is used in the backbone network to effectively reduce the number of YOLOv8 network parameters. Then, a feature pyramid network AFPNs based on shallow network fusion is proposed to improve the detection performance of small targets in UAV aerial images. Experimental results on the VisDrone2019 dataset show that compared with the YOLOv8 algorithm, the proposed algorithm reduces the number of parameters and improves the detection accuracy simultaneously.

关键词： Deep learning Accuracy Object detection Lightweight structures Network architecture Autonomous aerial vehicles Feature extraction Natural language processing Drones

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CHARACTERIZATION OF WETTING PROPERTIES OF MAGNETOACTIVE ELASTOMER SURFACES 16

CHARACTERIZATION OF WETTING PROPERTIES OF MAGNETOACTIVE ELAS...

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ASME 16th Annual conference of the Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

作者： Kriegl, Raphael Kravanja, Gaia Hribar, Luka Jezersek, Matija Drevensek-Olenik, Irena Shamonin, Mikhail Ostbayer Tech Hsch Regensburg Regensburg Germany Univ Ljubljana Fac Mech Engn Ljubljana Slovenia Univ Ljubljana Fac Math & Phys Ljubljana Slovenia J Stefan Inst Ljubljana Slovenia

ISBN: (纸本)9780791887523

Commercially available contact angle (CA) measuring devices usually do not allow for the application of magnetic fields to the sample under test. A setup for measuring the CA of liquids on magnetosensitive surfaces has been developed specifically for investigating the surfaces of magnetoactive elastomers (MAEs). The addition of a programmable linear stage, which moves a permanent magnet, allows for fine control of the magnetic field applied to the MAE without the need for large and power-consuming electromagnets. Paired with a custom control and evaluation software, this measurement setup operates semiautomatically, limiting operator error and increasing precision, speed, as well as repeatability of static and dynamic CA measurements for different magnetoactive materials. The software is equipped with robust droplet fitting algorithms to avoid experimental challenges arising with soft magnetoactive materials, such as the curling of sample edges or diffuse non-reflective surfaces. Several application examples on MAE surfaces, both processed and unprocessed, are presented.

关键词： magnetoactive elastomers image processing droplet contour fitting contact angle

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Adversarial Encoder-Driven Filter for Targeted image Tagging: A Novel Approach to Visual Content Manipulation

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

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International image processing, Applications and systems conference (IPAS)

作者： Cole Mckee Dominic Flowers Jesse Wood Ethan Shafer Department of Electrical Engineering and Computer Science United States Military Academy West Point New York

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. This paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon’s Rekognition image tagging algorithm using a black box approach.

关键词： Training Resistance Computer vision Visualization Law enforcement Military computing Computational modeling Autoencoders image annotation Closed box

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Super-Resolution Algorithm Based on Transformer+CNN

Super-Resolution Algorithm Based on Transformer+CNN

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Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), International conference on

作者： Jinyu Han Mingli Jing School of Electronic Engineering Xi'an Shiyou University Xi'an China

CNN and Transformer have their own excellent performance in image super-resolution, but these methods are difficult to be applied to the field of image SR alone due to the challenges of balancing model performance and complexity. To address these issues, we propose a Transformer+CNN-based algorithmic model for image super-resolution. First, a channel attention module is added to the model framework to model the correlation between channels of the feature maps; second, different training strategies are used to fine-tune the parameters at the end of the network base training; finally, a new loss function is introduced to optimize the parameters in the fine-tuning phase. The experimental results show that the method is more effective than the excellent algorithms in recent years.

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Research on Point Cloud Reconstruction Method for Film Cooling Hole

Research on Point Cloud Reconstruction Method for Film Cooli...

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China Instrument and Control Society (ACCIS), Academic conference of

作者： Weiyu Xu Andi Li Lei Luan Min Zhao Zhengxi Lu xiang Wei Beijing Precision Engineering Institute for Aircraft Industry Beijing China Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350350326

ISBN: (纸本)9798350350333

In order to measure the inner diameter of the film cooling hole in turbine blades, the 3D point cloud reconstruction method was studied in this paper. A five-axis motion platform and microscopic vision probe were used to obtain the focusing image sequence of the film cooling hole. image processing techniques such as image enhancement and image noise reduction are used to improve the quality of the focusing image sequence. Then, edge detection and threshold selection are used to extract clear pixel points representing the inner wall information of the film cooling hole in the focusing image sequence. Based on the grating ruler readings of the five axis motion platform during image sequence collection, different depth information of the measured film cooling hole is transformed. Combined with the clear pixel points, a three-dimensional point cloud reflecting the internal morphology information of the film cooling hole is reconstructed. Finally, the three-dimensional point cloud is processed to obtain parameter information such as the inner diameter of the measured film cooling hole.

关键词： Point cloud compression Three-dimensional displays Cooling image edge detection Focusing Reconstruction algorithms image sequences image reconstruction image enhancement Turbines

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VisNow-Medical—A Visual Analysis Platform for Medical Data processing 10th

VisNow-Medical—A Visual Analysis Platform for Medical Data ...

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10th KES International conference on Innovation in Medicine and Healthcare, KES-InMed 2022

作者： Regulski, Piotr Szopinski, Kazimierz Department of Dental and Maxillofacial Radiology Faculty of Medicine and Dentistry Medical University of Warsaw Binieckiego 6 St Warsaw Poland

ISBN: (纸本)9789811934391

The VisNow Medical platform is a set of integrated algorithms for visual analysis of medical data and is an extension of the VisNow platform used for image processing and visualization. VisNow Medical platform emphasizes on the ease of development of case-specific, multi-platform medical applications, with the possibility of processing one-, two-, and three-dimensional data in terms of filtering, denoising, segmentation, mapping, classification, quantitative and qualitative visual processing. VisNow Medical facilitates the research and development process in medical field. This research presents several applications of the platform for magnetic resonance, computed tomography and image processing applications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical applications

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Compression and Denoising of Medical images Using Autoencoders 1

Compression and Denoising of Medical Images Using Autoencode...

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1st International conference on Recent Trends in Microelectronics, Automation, Computing and Communications systems, ICMACC 2022

作者： Senapati, Ranjan K. Badri, Rohit Kota, Anudeep Merugu, Nithin Sadhul, Suraj Vnr Vjiet Bachupally Dept.of Ece Nizampet Hyderabad India

ISBN: (纸本)9781665496049

In medical image analysis image compression and denoising is an important processing steps for remote analytics. A number of algorithms are proposed in the literature with varying degrees of denoising performances. In this paper, a 3-layer autoencoder model to compress and denoise grayscale medical images is proposed. Gaussian noise with a noise factor of 0.5 is added and passes through 3 convolution and 3 max pool layers. The reverse process is made for image denoising. The proposed model learns to denoise data with a high PSNR and a high compression ratio after training. Most of the denoised images show over 30 dB PSNR with a 4:1 compression ratio. images can be combined to increase sample size and overall denoising performance. © 2022 IEEE.

关键词： image denoising

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