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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16349 条记录，以下是211-220 订阅

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AI-Driven Personalized Skincare Recommendations

AI-Driven Personalized Skincare Recommendations

引用

Electronics and Renewable systems (ICEARS), International conference on

作者： Iyyanar Perumal Kalaivani P Naveenkumar A Saran V Sriram K Suruthi N Department of Information Technology Sona College of Technology Salem India Department of CSE Kongu Engineering College Perundurai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study investigates the utilization of artificial intelligence (AI) to transform skincare product recommendations and the formulation of individualized routines through the analysis of user inputs and characteristics. The system utilizes data including skin type, acne count, oiliness level, and UV exposure, using KNearest Neighbors (KNN) for personalized product suggestions. Natural Language processing (NLP) approaches are utilized to develop tailored 7-day skincare regimen recommendations by examining product and user problems, converting unstructured data into actionable insights. Moreover, deep learning algorithms are employed for real-time image-based assessments of skin conditions, facilitating precise acne diagnosis and oiliness evaluation. The system integrates visual data with textual analysis, continually adapting through user feedback to improve accuracy and relevance. This article highlights the capacity of AI-driven methods to automate routine generation and provide tailored product recommendations, fostering innovation in the cosmetics sector through a thorough, data-oriented strategy.

关键词： Deep learning Visualization Accuracy Transforms Nearest neighbor methods Skin Natural language processing Real-time systems Artificial intelligence Wearable devices

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Research on Fitting and Interpolation Methods for Surface Deformation Data in Tilt Sensor Integrated System

Research on Fitting and Interpolation Methods for Surface De...

引用

Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Lei Lei Liang Wang Kai Wang Chaoyu Feng Qingyun Chen Duozhi Kang State Grid (Xi'an) Environmental Protection Technology Center Co. Ltd. Xi'an Shaanxi China Lintong District Sub-Company of Xi'an Power Supply Company State Grid Xi'an Shaanxi China Extra High Voltage (EHV) Company of State Grid Shaanxi Electric Power Co. Ltd. Xi'an Shaanxi China Electric Power Research Institute State Grid Shaanxi Electric Power Company Limited Xi'an Shaanxi China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Surface deformation monitoring plays an important role in geological hazard prevention and engineering stability analysis, especially in high-risk areas such as landslides and subsidence. Accurate measurement of surface displacement is the key to scientifically evaluating geological safety. Tilt sensors reflect surface deformation by detecting tilt angles and have been widely used in geological hazard monitoring systems in recent years. However, due to factors such as environment and equipment accuracy, the collected data has discreteness and errors, and it is necessary to process the data through fitting and interpolation methods to obtain more accurate surface deformation information. This study used a combination of experimental design and data simulation to compare and analyze various fitting and interpolation algorithms. Based on actual collected surface deformation data, experimental verification was conducted on methods such as multiple linear regression, Lagrange interpolation, and spline interpolation to evaluate their applicability under different deformation amplitudes and sampling densities. Then, by constructing a dataset and simulating different surface deformation scenarios, the performance of each method in noise suppression, error correction, and other aspects can be quantified. The experimental results show that spline interpolation can effectively improve data continuity in scenarios with smaller deformation amplitudes, while multiple linear regression has higher stability and accuracy in scenarios with larger deformations. Research has shown that the combination of multiple linear regression and spline interpolation can significantly improve the data fitting effect, providing a more reliable technical means for surface deformation monitoring.

关键词： Interpolation Accuracy Deformation Linear regression Fitting Surface fitting Stability analysis Splines (mathematics) Surface treatment Monitoring

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Design and Implementation of an Autonomous Line-Following Minidrone Using image processing and PID Control

Design and Implementation of an Autonomous Line-Following Mi...

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： Rekha P Maaz Hussain Misrah Naveed Dept. of ECE BNM Institute of Technology Bangalore India 7th Sem ECE BNM Institute of Technology Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This paper presents a comprehensive approach to controlling a Parrot Mambo minidrone in a simulation environment using MATLAB. The primary objective is to guide the drone along a predefined path while maintaining stability and precision. Optical flow technique and Inertial Measurement Unit sensor data are used for real-time position and velocity estimation. Path planning algorithms, coupled with control strategies such as Kalman filtering and dynamic error correction, ensure the drone follows the optimal path to reach its target. Simulation results demonstrate the effectiveness of the proposed system in successfully navigating the drone through complex maneuvers with minimal drift.

关键词： Filtering Simulation Heuristic algorithms Real-time systems Error correction Kalman filters MATLAB Optical flow Tuning Drones

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Perceptual Learning Technology Based on Computer Vision and Machine Learning algorithms

Perceptual Learning Technology Based on Computer Vision and ...

引用

International conference on Communication systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision image segmentation Machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness Real-time systems

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GMT-TabNet based type classification method for forest cover 24

GMT-TabNet based type classification method for forest cover

引用

Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Bao Liu Guanrong Wang Zaiying Wang School of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an Shaanxi China

ISBN: (纸本)9798400713880

Aiming at the problems of loss of global feature information, insufficient utilization of inter-feature information and redundancy of feature selection in the TabNet network for forest cover type classification, this paper proposes a forest cover classification method based on a global feature extraction module, a multi-scale feature converter and a T-attention converter. Firstly, we propose a global feature extraction module, which utilizes both global and local feature information during classification and provides global information for subsequent feature extraction; secondly, a multi-scale feature converter is proposed, which extracts inter-feature information using multi-Scale one-dimensional convolution to further enhance the utilization of feature information; lastly, this paper proposes a T-attention converter, which, in comparison with Sparsemax. SparseTmax will be sparser and more flexible for feature screening. This paper uses the Covtype dataset for comparison experiments, and the results show that the classification model proposed in this paper has higher classification accuracy and F1 score than the original TabNet, CatBoost, XGBoost, LightGBM and TabTransformer, and it can better satisfy the needs of the forest cover classification task.

关键词： Classification algorithms

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Optimization of Micro-Object Identification with Factorization and Correction Mechanisms for Distorted image Points

Optimization of Micro-Object Identification with Factorizati...

引用

International Russian Smart Industry conference (SmartIndustryCon)

作者： Isroil I. Jumanov Rustam A. Safarov Olimjon I. Djumanov Intelligent systems and computer technologies Samarkand state university Samarkand Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The scientific and methodological foundations for the optimal identification of micro-objects with mechanisms for using information structural components, features, statistical and dynamic characteristics of images have been developed. Mechanisms for tracking, detecting and correcting distorted points, factorizing the structural components of the image, filtering, segmentation, tracking distorted points under conditions of a priori insufficiency, parametric uncertainty, and low reliability of information processing are proposed. Methods for converting the initial brightness of a point into homogeneous objects as a function of two variables have been studied and nonlinear optimization functionals have been used. Mechanisms for factorization of the structural components of the image in the form of an indicator of linear connectivity, “neighborhood relationship” of three separate algorithms have been implemented. An alternative image factorization algorithm has been developed. A comparative analysis of effectiveness was carried out. The effectiveness of the mechanisms for detecting and correcting distorted points was studied using the criterion of the probability of undetected errors, the root-mean-square error, according to Shannon's estimates, the average level of intensity of the points, and the total compactness parameter. Rank estimates of integral indicators of image factorization were obtained for 30 images with 100 tests. A software package for visualization, recognition, and classification of images of microobjects has been developed, the implementations of which have been tested using real medical diagnostic data.

关键词： image segmentation Uncertainty Software packages Nonlinear distortion Brightness Noise Information processing Reliability Object recognition Optimization

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Algorithm Optimization and Immersion Enhancement in Virtual Reality Film

Algorithm Optimization and Immersion Enhancement in Virtual ...

引用

Intelligent systems and Computational Networks (ICISCN), International conference on

作者： Chulei Zhang Hongxing Qian Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

With the widespread application of virtual reality (VR) technology in the fields of entertainment and education, the immersion experienced by users in VR movies and TV shows has become a key factor affecting their acceptance and satisfaction. However, there are still significant shortcomings in terms of image rendering speed in current VR movies and TV shows. This article introduces an algorithm optimization framework based on Convolutional Neural Networks (CNN). Firstly, this article uses CNN algorithm to optimize the image rendering process. Next, use Dynamic Resolution Adjustment (DRS) technology to improve rendering speed and maintain frame rate stability; Then, combining high dynamic range (HDR) rendering and temporal anti aliasing (TAA) technology to enhance the color expression and visual smoothness of the image; Finally, optimize the sensor data processing flow and data transmission protocol. Through experimental evaluation in VR scenes of different complexities, the results showed that the optimized system improved rendering speed by an average of about 58.3%, reduced interaction latency by about 33.3%, and improved image quality indicators PSNR and SSIM by about 26.2% and 9%, respectively. In the above data conclusions, CNN based optimization methods have shown significant advantages in improving VR film and television performance and user experience, providing strong support for the future development of VR film and television technology.

关键词： image quality Visualization TV Heuristic algorithms Virtual reality Motion pictures Rendering (computer graphics) User experience Convolutional neural networks Optimization

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Implementation of a Comparative Study of Convolutional Neural Network Architectures for image Blind Noise Elimination 4th

Implementation of a Comparative Study of Convolutional Neura...

引用

4th International conference on Intelligent systems and Pattern Recognition, ISPR 2024

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Department of Mathematics and Computer Science ENS M2PA Laboratory University of Sidi Mohamed Ben Abdellah Fez30030 Morocco

ISBN: (纸本)9783031821523

The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of image processing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. These methods have demonstrated remarkable performance in image processing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. These findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Rectifying Irregular Boundaries Stereoscopic images Using Deep Learning and Evolutionary algorithms

Rectifying Irregular Boundaries Stereoscopic Images Using De...

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IEEE SSD International Multi-conference on systems, Signals and Devices

作者： Muntasser A. Wahsh Zainab M. Hussain Department of Computer Science Informatics Institute for Postgraduate Studies (IIPS) Baghdad Iraq Department of Political Science Al-Iraqia University Baghdad Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Application of stereoscopic technology has increased significantly in several industries, including the media and entertainment sectors, medical imaging, and virtual reality. However, the overall viewing experience and the feeling of depth can be negatively impacted by uneven boundaries in stereoscopic images. To have a realistic and immersive stereoscopic experience, fixing these wavy edges is an essential process. This work present a distinctive approach for optimizing the rectangular bounds of stereoscopic images using a hybrid of deep learning and evolutionary algorithms. The suggested method uses a genetic algorithm to optimize the parameters of a convolutional neural network (CNN) to extract useful features for residual regression. Due to the lack of compatible datasets, a set of stereoscopic images was constructed from monocular images by calculating the depth of the objects using MiDaS pretrained depth estimation algorithm. Several metrics, including as mean squared error, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID), were used to evaluate the performance of the suggested technique. The experimental findings show that the proposed method effectively optimizes rectangular borders with excellent accuracy, even for images with complicated shape and orientations. Various parameter settings were also tested, revealing that increasing the number of generations and population size contributes to improved performance. The combination of the deep learning algorithm, specifically the CNN, and the optimization algorithm, specifically the genetic algorithm (GA), leads to an increase in the accuracy of rectified images. Notably, the average PSNR reached 24.42, SSIM measured at 0.7793, and FID scored 18.26. The proposed method holds promise for a wide range of applications, including image cropping, object detection, and image segmentation, among others. By effectively optimizing rectangular boundaries in ster

关键词： Deep learning Visualization Accuracy PSNR Stereo image processing Depth measurement Evolutionary computation Feature extraction Convolutional neural networks Genetic algorithms

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Research on Production Decision Making Based on Greedy Algorithm and Shortest Path Model

Research on Production Decision Making Based on Greedy Algor...

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Electronics, Energy systems and Power Engineering (EESPE), IEEE International conference on

作者： Shuying Zhou Zheng Duan Rocket Force University of Engineering Xi'an China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The aim of this study is to explore production decision optimisation methods based on greedy algorithms and shortest path models, with a special focus on the impact of defective rates on firms and related decision considerations. By analysing the processing strategies such as testing and quality control of production materials, this paper establishes a binomial distribution model, a dynamic programming model and a shortest path model to optimise the enterprise structure. In the study, the binomial distribution model is firstly constructed to assess the defective rate of spare parts by applying the confidence test method in statistics, and the normal approximation is combined with the central limit theorem in order to facilitate the decision-making of sampling and testing. For multi-stage decision-making that deals with more complex production materials, dynamic planning methods are used to clarify the decision variables in each stage, and the shortest path model is combined to identify the lowest-cost path, and the optimal decision combination is finally filtered out. In addition, for the complexity of multiple processes, this paper is based on the idea of greedy algorithm to decompose the overall problem into multiple sub-problems, solving and integrating the optimal solutions of each sub-strategy one by one. The research results show that the proposed method effectively reduces the production decision-making cost and provides a feasible solution for enterprises to achieve cost minimisation and revenue maximisation.

关键词： Greedy algorithms Analytical models Costs Biological system modeling Decision making Quality control Production materials Minimization Dynamic programming Testing

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