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

共 12927 条记录，以下是151-160 订阅

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Guided View Approach for Lane Detection and Caution Analysis using Computer Vision and Deep Learning Techniques

Guided View Approach for Lane Detection and Caution Analysis...

引用

Artificial Intelligence and Data Engineering (AIDE), International conference on

作者： Manjula Gururaj Rao Vinyas S Gururaj Rao H Priyanka Vishal Aisiri NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karkala Sasken Technologies Limited Bangaluru Computer Science & Engineering PESU Bangaluru

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

In recent years, advancements in computer vision technology have enabled innovative applications in various fields, including transportation safety. One such application is a Lane Detection and Caution Measurement Analyzer system aimed at enhancing road safety. This system uses computer vision techniques to provide real-time analysis of road conditions, helping drivers navigate more safely. The Lane Detection module employs image processing algorithms such as edge detection, Hough transform, and perspective transformation to identify lane boundaries accurately. Additionally, the Caution Measurement Analyzer module utilizes object detection and tracking algorithms to identify potential hazards, such as potholes, and measures the caution required for safe driving. The proposed model integrates CNN, OpenCV, and YOLO, achieving 87% accuracy in object detection and caution measurement, and 94% accuracy in lane boundary detection.

关键词： YOLO Computer vision Accuracy Lane detection image edge detection Transforms Road safety Real-time systems Hazards Vehicles

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Deepfake image and Video Detection Using Deep Learning algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks Artificial intelligence Research and development

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AI-Driven Personalized Skincare Recommendations

AI-Driven Personalized Skincare Recommendations

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

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

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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 ...

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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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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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Adaptive KOA: Leaf disease classification using hyperspectral images for Internet of Things (IoT)-based sustainable agriculture

Adaptive KOA: Leaf disease classification using hyperspectra...

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Emerging systems and Intelligent Computing (ESIC), International conference on

作者： Pradeep Kumar Mallick Annapareddy V N Reddy Manas Ranjan Mohanty Biswajit Sahoo Alok Kumar Jagadev School of Computer Engineering Kalinga Institute of Industrial Technology(KIIT) DU Bhubaneswar Odisha Department of Information Technology Lakireddy Bali Reddy College of Engg. Mylavaram Andhra Pradesh

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

In recent times, the crops as well as the agriculture management is one of the important field to watch out, therefore the image processing business provide more benefit to the crops along with support precaution needs. In this research, an adaptive Kookaburra Optimization Algorithm (Adaptive KOA) is implemented for leaf disease classification using hyperspectral images for Sustainable Agriculture in *** first, the IoT modelis replicated and the routing is conductedto transmit the data from source to the Base Station (BS). Moreover, in BS the following steps are conducted. The input hyperspectral image (HSI)is subjected to a band selection module. The band selection is done using the implemented Adaptive Kookaburra Optimization Algorithm (Adaptive KOA). The developed Adaptive KOA is devised by incorporating Adaptive concept with Kookaburra Optimization Algorithm (KOA). The Selected bands are fed into the leaf segmentation module, where the Deep Embedded Clustering (DEC) technique is utilized for segmentation. Fusion of segmented image from different bands is carried out by the majority voting method. Finally, Leaf disease classification is carried out based on the 3D-Convolutional Neural Network (3DCNN) into normal and abnormal cases. The developed Adaptive KOA-3DCNN method has the maximum value for accuracy as 0.917%, highest value for True negative rate(TNR)as 0.927%, highest value for True positive rate (TPR) as 0.907%.

关键词： image segmentation Plant diseases Adaptation models Clustering algorithms Routing Agriculture Classification algorithms Internet of Things Optimization Hyperspectral imaging

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