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

共 13605 条记录，以下是101-110 订阅

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Comprehensive image Classification and Object Detection Using Convolutional Neural Networks

Comprehensive Image Classification and Object Detection Usin...

引用

Disruptive Technologies (ICDT), International conference on

作者： Archana Kumari Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In the modern era, Deep Learning (DL) methodology is used extensively in multiple areas and Convolutional Neural Network (CNN) is majorly used for addressing the problems in real-time for computer vision activities due to its capability of enabling acquisitions with higher accuracy. The concept at the back of this DL technique was inspired from architecture of biotic visual systems. In the current time, it is applied extensively in multiple areas and industries like automotive, medical and healthcare, space, aviation, agriculture etc. Moving forward, numerous algorithms on CNN were proposed to address the requirement of multiple areas, with Natural Language processing (NLP) and computer vision as the major fields for application. The activities in computer vision with major application of CNN includes image categorization, object identification, recognition of face, estimation of human pose, recognition of action, scene labelling along with document analysis. This paper delves into modern techniques of CNN for classification of images and detection of objects.

关键词： Deep learning Computer vision image recognition Face recognition Medical services Real-time systems Natural language processing Agriculture Convolutional neural networks Biomedical imaging

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Research on image brightness enhancement algorithm based on deep learning 24

Research on image brightness enhancement algorithm based on ...

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

作者： Siqi He Jianping Xing Yu Bai Shuzhen Huang Kaiping Sun Peng Sun Shandong University Jinan Shandong Province China Shandong Vocational and Technical University of Engineering Jinan Shandong Province China

ISBN: (纸本)9798400713880

In view of traditional image improvement technology's limitations in high real-time application environments, a new low-light video image improvement algorithm is proposed in this article. In consideration of traditional improvement algorithms' faults, and under the precondition of studying low-light improvement techniques in image signal processing (ISP) systems, deep learning frameworks are introduced to break through complex structure and long processing pipeline faults in traditional ISP systems. In this article, a lightweight image brightness improvement algorithm with a basis in convolutional neural network (CNN) is proposed. In this algorithm, feature extraction of RGB images is performed through CNN to generate a brightness coefficient matrix, simplifying RGB to RGB direct mapping and reducing model numbers, model parameters, and computational complexity. This technology pays attention to generating coefficients of brightness, can effectively extract changing parts of brightness, maintain detail information, and conduct efficient and natural brightness improvement, and can apply to high real-time application environments such as autonomous driving.

关键词： Convolutional Neural Network

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Development of a Model for Texture Analysis of Machined Parts on CNC Machines

Development of a Model for Texture Analysis of Machined Part...

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International Russian Smart Industry conference (SmartIndustryCon)

作者： Ilya Kovalev David Malykh Alexander Kiselev department of Computer Control Systems MSUT “STANKIN” Moscow Russia department of Robotics and Mechatronics MSUT “STANKIN” Moscow Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This work is devoted to the development of a software module for analyzing the textures of machined parts on CNC machines using computer vision methods. The main goal of the research is to create a tool capable of analyzing geometric parameters of products based on texture properties of materials, which is especially relevant in the conditions of high-volume production. The paper considers modern methods of texture analysis, as well as their robustness to various disturbances such as illumination changes, noise, blurring, rotation and image scaling. An analysis system that combines binocular and texture methods of image processing is proposed, which makes it possible to improve the accuracy of measuring distances to objects on the basis of binocular algorithm, by means of additional analysis of texture properties of surfaces of the processed part.

关键词： Computer vision image analysis Software algorithms Noise User interfaces Software Robustness Classification algorithms Surface texture Surface treatment

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Identifying Adversarial Attacks in Crowdsourcing via Dense Subgraph Detection

Identifying Adversarial Attacks in Crowdsourcing via Dense S...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Abdullah Karaaslanli Panagiotis A. Traganitis Aritra Konar Dept. of Electrical & Computer Engineering Michigan State University East Lansing MI USA Dept. of Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Crowdsourcing is becoming increasingly important for contemporary applications in machine learning and artificial intelligence. However, crowdsourcing systems may be susceptible to adversarial attacks where a subset of annotators deliberately provide erroneous responses. This paper introduces a novel algorithm for identifying adversarial attacks in crowdsourcing systems by recasting the problem as dense subgraph detection in bipartite graphs. In particular, we represent crowdsourced data as a weighted bipartite graph between workers and data points which are connected with edges whose weights are computed from annotators’ responses. The constructed bipartite graph is then analyzed with a sequential peeling algorithm to detect the dense subgraph that includes adversarial attacks. Compared to previous methods where only adversaries are detected, our proposed method can simultaneously identify adversarial annotators as well as affected data points. Preliminary results on real datasets showcase the potential of this novel approach.

关键词： Crowdsourcing Machine learning algorithms image edge detection Signal processing algorithms Machine learning Signal processing Acoustics Bipartite graph Speech processing

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Real-Time Dynamic image Stitching Using GNN-Based Feature Matching

Real-Time Dynamic Image Stitching Using GNN-Based Feature Ma...

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Mechatronics and Robotics Engineering (ICMRE), International conference on

作者： Young Hoon Suh Eun Ho Kim Yeong Gwang Choi JaeWook Jeon Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties, such as managing shifting objects, changing surroundings, and processing limitations. An image stitching look at that works well in dynamic real-time situations is presented in this paper. The method makes use of GNN-based feature matching, dynamic object removal, and real-time synthesis techniques. Comparative studies suggest that, in comparison to traditional methods like SIFT and ORB, the suggested approach meets real-time processing requirements while achieving greater registration precision, lower homography RMSE, and consistent panoramic outputs. The findings imply that the algorithm might be used in fields where precision and efficiency are required in dynamic circumstances, like parking management systems, sports broadcasting, and autonomous driving.

关键词： Accuracy Smoothing methods Heuristic algorithms Surveillance Real-time systems Robustness image stitching Robots Optimization Sports

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Unsupervised domain adaptation in human detection for various human pose

Unsupervised domain adaptation in human detection for variou...

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International image processing, Applications and systems conference (IPAS)

作者： Benoit Lagadec Ekaterina Kostrykina Esifrance Le Cannet France Universite Cote d’Azur Nice Cedex 2 France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In human detection algorithms, some recurrent issues are still challenging. Indeed, in a real application we have a lack of various human poses in our data. For example, in video surveillance it could be a significant issue to forget detecting a human in uncommon pose. We propose a compensation to these less frequent cases. We introduce a detection in rotated view to compensate limited various poses in our dataset. Instead of introducing a generative enhancement for every human body pose, which could be exhausting. Indeed, data annotation has become a difficulty for many applications. Moreover, a simple data augmentation is not efficient to generalize our training. We need a strategy to learn this new poses. So, we will introduce a process to compute pseudo labels in a sequence of rotated views. It avoids to use a clustering algorithm like dbscan [Chen et al.(2021)Chen, Lagadec, and Bremond.] or K-means to compute a pseudo label. A summary of theoretical results is described in a first section to illustrate our baseline. First of all, we will illustrate how we process pseudo label on various views by two mechanisms which have been called relaxation and scanning. We introduce a sequence of rotations of original view, to process a detection in a compensated view. In second step, we will learn this distortion created by the various views and construct an approach to distill original knowledge to a more generalized *** main improvement of this approach is to propose an alternative solution at dbscan [Chen et al.(2021)Chen, Lagadec, and Bremond.] approach which is popular, but which is difficult to use in real applications. We obtain a significant result by improving accuracy in especially various pose datasets.

关键词： Training Accuracy Annotations image processing Clustering algorithms Data collection Video surveillance Distortion Data augmentation Detection algorithms

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Research on Intelligent Data processing Support Model of Government Information System Based on Deep Learning

Research on Intelligent Data Processing Support Model of Gov...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe conference on

作者： Dapeng Lu Haojie Liu School of Tourism and Public Administration Zhuhai College of Science and Technology Guangdong Zhuhai China Computer Application Technology Henan University of Technology Zhengzhou China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This study constructs a data processing model that supports government information system. By designing a deep learning algorithm suitable for government scenarios, intelligent processing functions such as text analysis and image recognition are integrated into the system, aiming to achieve accurate processing of multi-source government data. The model design includes data preprocessing, feature extraction, algorithm optimization and adaptive learning modules to improve the recognition and analysis capabilities of complex government data. Through system simulation, this paper verifies the effectiveness of the proposed model. The simulation data shows that the model is significantly superior to traditional methods in data processing speed, accuracy and resource utilization. Precise experimental results show that the accuracy of the model in text data classification, image data recognition and other aspects reaches more than 96%. At the same time, it can effectively shorten the processing time in large-scale data processing tasks, providing technical support for the practical application of intelligent government information system. This study provides an efficient and scalable solution for intelligent data processing in government information system.

关键词： Deep learning Adaptation models Accuracy image recognition Heuristic algorithms Government Data processing Data models Optimization Information systems

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

ISBN: (纸本)9789819660049

The proceedings contain 29 papers. The special focus in this conference is on Intelligent Information and Database systems. The topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological Data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at Thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute Information;continuous Recognition of Mouth Patterns in Japanese Sign Language for Visual Communication;Towards an UAV Visual Navigation System Based on Map processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset;estimation of Distribution algorithms with Overlapped Subpopulations;energy-Efficient Edge Query processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge Computing Through Co-design for Enhanced Data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient Vietnamese Name Detection Using Highly Discriminative N-Gra

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Analysis of Duplicating the Human Mind Suspicions in AI in Various Divisions

Analysis of Duplicating the Human Mind Suspicions in AI in V...

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

作者： S. P. Santhoshkumar R. Karthika N. Vijayakumar S. Gajalakshmi K. Jayanthi S. Hariharasudhan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Information Technology School of Computing Rathinam Technical Campus Coimbatore India Computer Science and Engineering Pollachi Institute of Engineering and Technology Pollachi Coimbatore India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness of its output or decision-making. Uncertainty can arise in AI systems due to a variety of factors, including the quality of input data, variations in data formats, variations in context or environment, and the complexity of the underlying algorithms. Different AI applications can have specific types of uncertainty associated with them, as we discussed in previous questions about uncertainty in AI image recognition, natural language processing, data extraction, expert systems, planning and optimization, and robotics. To mitigate uncertainty in AI systems, researchers and developers use various methods, such as incorporating more data and diverse inputs, improving the quality of input data, enhancing the algorithms, and using feedback mechanisms to learn from errors and improve the system over time. However, complete elimination of uncertainty is often challenging or impossible, and users should always be aware of the potential for errors or unexpected behaviour in AI systems. In this paper, analysis of artificial intelligence uncertainty in various fields.

关键词： Uncertainty Accuracy image recognition Predictive models Probabilistic logic Natural language processing Planning Artificial intelligence Robots Optimization

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

ISBN: (纸本)9789819660070

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