Computer Vision-Based Human Body Posture Correction System

With the development of technology and the progress of life,more and more people,regardless of entertainment,learning,or work,cannot do without computer desks and cannot put down their mobile phones.Due to prolonged sitting and often neglecting the importance of posture,incorrect posture can often lead to health problems such as hunchback,lumbar muscle strain,and shoulder and neck pain over time.To address this issue,we designed a computer vision-based human body posture detection system.The system utilizes YOLOv8 technology to accurately locate key points of the human body skeleton,and then analyzes the coordinate positions and depth information of these key points to establish a criterion for distinguishing different postures.With the assistance of an SVM classifier,the system achieves an average recognition rate of 95%.Finally,we successfully deployed the posture detection system on Raspberry Pi hardware and conducted extensive testing.The test results demonstrate that the system can effectively detect various postures and provide real-time reminders to users to correct poor posture,demonstrating good practicality and stability.

A VIDEO SPECTRUM SPLITTING ENCODING SCHEME BASED ON HUMAN VISION AND ITS COMPUTER SIMULATION

In this paper, a 3-D video encoding scheme suitable for digital TV/HDTV (high definition television) is studied through computer simulation. The encoding scheme is designed to provide a good match to human vision. Basically, this involves transmission of low frequency luminance information at full frame rate for good motion rendition and transmission of high frequency luminance signal at reduced frame rate for good detail in static images.

Interpolation of Images Using Discrete Wavelet Transform to Simulate Image Resizing as in Human Vision

This paper presents discrete wavelet transform （DWT） and its inverse （IDWT） with Haar wavelets as tools to compute the variable size interpolated versions of an image at optimum computational load. As a human observer moves closer to or farther from a scene, the retinal image of the scene zooms in or out, respectively. This zooming in or out can be modeled using variable scale interpolation. The paper proposes a novel way of applying DWT and IDWT in a piecewise manner by non-uniform down- or up-sampling of the images to achieve partially sampled versions of the images. The partially sampled versions are then aggregated to achieve the final variable scale interpolated images. The non-uniform down- or up-sampling here is a function of the required scale of interpolation. Appropriate zero padding is used to make the images suitable for the required non-uniform sampling and the subsequent interpolation to the required scale. The concept of zeroeth level DWT is introduced here, which works as the basis for interpolating the images to achieve bigger size than the original one. The main emphasis here is on the computation of variable size images at less computational load, without compromise of quality of images. The interpolated images to different sizes and the reconstructed images are benchmarked using the statistical parameters and visual comparison. It has been found that the proposed approach performs better as compared to bilinear and bicubic interpolation techniques.

An Analysis Model of Sports Human Body Based on Computer Vision Tracking Technology

This paper proposes the analysis model of sports human body based on computer vision tracking technology. Visual target tracking is an important research field of the computer vision, motion trajectory and it can provide not only the goal, to provide the initial data movement analysis, scene understanding, behavior or the event detection in intelligent surveillance, human-computer interaction, robot visual navigation and motion recognition based on field has a broad application prospect. For this reason, it is possible to consider the use of a large number of unlabeled samples to assist the training classifier to improve its performance. This type of machine learning method using both labeled and that unlabeled samples is called the semi-supervised learning. This paper proposes the novel idea of the related research topics to propose the new perspective of the model that will be later give us the novel idea of making it efficient for further development of sport science.

Theoretical and practical exploration of vision building in human influenza pandemic prevention & control

This article introduced the vision building concept about human influenza pandemic prevention and control.Different visions were built by creating different shapes of building blocks which also represented different organizations and physical facilities,respectively.The around-view reflection is required to be developed in the process of building so as to search for the ideal pattern.The correlation of all sectors and systems are established to combine different kinds of things,from one family to another,from communities,towns,counties,cities,rural areas, provinces to the state to handle trivial problems.These training objectives have been successfully accomplished,which has not only enriched the knowledge about prevention and control of influenza pandemic between different departments but also clarified the roles and responsibility. It lays the firm foundation for next cooperation between different departments,and make a bridge for the objective and choice of channel over human influenza pandemic prevention and control.

The Declaration on the Right to Development as a First Step towards a Comprehensive Southern Vision on Human Rights

Having proper sanitation and hygiene, access to affordable health care and enough food on the table are the basic conditions for a dignified life. This link between human dignity and the right to development was made very persuasively already in the White Paper on human rights, issued by the State Council of P.R.C. in 1991. The Declaration on the right to development can be considered the first successful joint action undertaken by Southern states in the area of human rights. The Declaration was based on Southern scholarship, such as the pioneering research conducted by the Senegalese jurist Kéba M’baye. And its adoption was the result of joint stage management performed by diplomats from different continents. Therefore the Declaration serves as a source of inspiration for the work of drafting a Comprehensive Southern Vision on human rights. The Vision document will lay out a common Southern outlook on human rights issues as an elaboration of the Universal Declaration.

Rendering algorithms for aberrated human vision simulation

Vision-simulated imagery―the process of generating images that mimic the human visual system―is a valuable tool with a wide spectrum of possible applications, including visual acuity measurements, personalized planning of corrective lenses and surgeries, vision-correcting displays, vision-related hardware development, and extended reality discomfort reduction. A critical property of human vision is that it is imperfect because of the highly influential wavefront aberrations that vary from person to person. This study provides an overview of the existing computational image generation techniques that properly simulate human vision in the presence of wavefront aberrations. These algorithms typically apply ray tracing with a detailed description of the simulated eye or utilize the point-spread func-tion of the eye to perform convolution on the input image. Based on the description of the vision simulation tech-niques, several of their characteristic features have been evaluated and some potential application areas and research directions have been outlined.

Shadow detection combining characters of human vision

A shadow detection method using pulse couple neural network inspired by the characters of human visual system is proposed.More precisely,lateral inhibition of human vision and coefficient of variation are combined together to improve the pulse couple neural network.Shadow detection is considered to be a shadow region segmentation problem.Experiment shows that the presented method is consistent with human vision compared to shadow detection methods based on HSV and pulse couple neural network(PCNN) by both subjective and objective assessments.

Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

Research on Human Body Behavior Recognition Based on Vision

This paper proposes the research on human body behavior recognition based on vision. Behavior based on high-level human structure can describe behavior more accurately, but it is dif？ cult to extract the behavioral characteristics while often relying on the accuracy of the human pose estimation. Moving object extraction of the moving targets in video analysis as the main content, research based on the image sequence robust, fast moving target extraction, motion estimation and target description algorithm, and the correlation between motion detection is to use frame, frame by comparing the difference between for change and not change area. The model is proposed based on the probability theory, and the future research will be focused on the simulation.

Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.

一种基于HVS的利用零树编码的水印算法

提出了一种利用人类视觉特性的数字图像水印算法 .先采取直接序列扩展方式生成水印序列信号 ,然后嵌入到一组按重要性选出的小波系数中 ,其中嵌入强度由小波域临界可见误差JND(JustNoticedDiffer ence)门限决定 .实验证明此算法在水印的不可见性和鲁棒性之间取得了良好的平衡 ,既保证了人眼视觉的不可察觉性 ,又具有较好的鲁棒性 .

一种基于HVS和自适应小波变换的彩色图像水印算法

数字水印技术是实现数字产品产权保护的一种新方法。本文提出了一种基于HVS和自适应小波变换的彩色图像水印改进算法。首先利用改进的自适应小波变换对原始图像进行处理,然后采用混合置乱算法对水印图像进行处理。同时根据HVS的特点来确定水印的嵌入强度。实验证明这种算法具有较好的隐蔽性,对各种图像处理操作是鲁棒的。

一种基于HVS特性的视频质量评测方法

本文针对视频质量的评测应用,对传统峰值信噪比(PSNR)算法加以改进。通过在视频帧内图像和帧间图像的处理过程中引入人眼视觉系统(HVS)的主要特性,克服传统PSNR算法在序列质量检测应用方面的缺陷。方法在帧内图像处理上利用人眼对边缘轮廓失真具有较强敏感性的特点,设计了基于图像边缘的检测方案以提高对典型空域失真的检测性能;在帧间图像处理上,通过测量帧间时域能量的变化,获得序列在时域轴上的典型特征,并据此对空域检测结果进行修正。通过上述改进,算法能在保持传统PSNR算法简易性的同时,提升其检测结果与主观感受的相关性;同时算法的计算量并不复杂。

基于HVS的抗几何攻击视频水印方案

提出一种基于人类视觉系统(HVS)的抗几何攻击视频水印方案。对原始视频对象进行规范化处理,记录同步信息。在水印嵌入时,根据人类视觉特性,从视频对象中选取高细节区域嵌入水印。在水印检测和提取前,对受攻击的含水印视频对象进行规范化处理,调整一些抖动点的位置,以进一步提高水印检测的性能。实验结果表明,该方案能较好地抵抗高强度几何攻击,协调不可见性、鲁棒性和安全性。

基于HVS的小波变换数字图像水印算法

为实现图像水印在数据认证、访问控制和版权保护等领域发挥作用,提出一种人眼视觉感知特性的小波变换数字图像水印算法。首先对二值图像水印进行Arnold变换,并采用行堆叠的方法,转换成一维序列,然后根据密钥生成的高斯序列中值的正负来确定水印信息嵌入时是采用那种调制方式,最后对原始图像进行3级DWT分解,在小波图像的第二层中频子带中,结合人眼视觉特性将水印正向或负向调制叠加在宿主图像大于JND值的小波系数上。实验结果表明:该算法能抵抗JPEG压缩、加噪、剪切等多种攻击,具有良好的透明性,鲁棒性和安全性。

基于神经网络和HVS的空域数字水印算法

为了弥补空间域水印算法在提取水印时需要原宿主图像、鲁棒性较差等缺点,提出一种基于BP神经网络和人眼视觉特性(HVS)的空域数字水印算法。先选出原宿主图像对比度函数值最大的前块用于二值水印图像的嵌入,利用神经网络能够逼近任意非线性关系的特点和其自适应性,构建水印信号嵌入前后图像块像素值间的映射关系,实现了水印的盲提取,并将该算法与现有算法进行了对比。实验结果表明,对于JPEG压缩、加噪、剪切和旋转等常见的图像处理攻击,算法具有良好的鲁棒性和不可见性。

一种HVS和Contourlet结合的图像水印算法

提出了一种Contourlet域的图像水印算法.首先对图像进行多层分解;然后利用视觉系统的亮度纹理特性,把伪随机序列组成的水印信号自适应地嵌入到能量最大的子带中;最后经过逆变换得到嵌入水印后的图像.该算法解决了直接嵌入边缘扭曲的问题,检测端使用基于相关的盲检测.实验结果表明,该算法能够在满足不可见性的条件下抵抗JPEG压缩、加噪和剪裁等攻击,具有很强的鲁棒性.

基于混沌和HVS的小波域自适应图像水印算法

针对常规的小波域水印算法自适应性差的缺陷,提出了一种新的基于混沌和人类视觉系统的小波域自适应图像水印算法。首先对载体图像进行二级DWT变换;然后对特定的中频子带进行分块,并对每块做SVD分解;最后将Logistic混沌置乱后的二值水印信息嵌入到每块的最大奇异值中,嵌入的强度根据HVS原理自适应地进行调整。仿真实验表明,该算法不仅具有很好的不可见性,且对JPEG压缩、加噪、滤波等常见的图像攻击具有很强的鲁棒性。