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

共 12901 条记录，以下是1141-1150 订阅

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Hybrid Machine Learning Techniques for image Denoising Based on Wavelet Transform

Hybrid Machine Learning Techniques for Image Denoising Based...

引用

IEEE International conference on Power, Intelligent Computing and systems (ICPICS)

作者： Qikun Yuan University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Wavelet theory has been widely applied in image processing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. This paper introduces a novel image denoising method. It leverages wavelet transform and machine learning algorithms, presenting a synergistic approach that combines discrete wavelet transform coefficients with Random Forests and CNNs. This method significantly enhanced image clarity without increasing computational demand. This article utilized wavelet transform to extract image features, and then trained the model with wavelet coefficients as training data through machine learning methods, integrating Random Forests and Convolutional Neural Networks to analyze the effectiveness of different wavelet coefficients, thereby optimizing the model's denoising performance. It has been proved effective in denoising performance after rigorous validation through quantitative comparisons of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the high-quality stereo image dataset Flickr1024.

关键词： Machine learning algorithms Computational modeling Noise reduction Transforms Feature extraction Discrete wavelet transforms Convolutional neural networks Random forests image denoising Wavelet coefficients

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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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Parallel image processing for Liquid Level Detection 7th

Parallel Image Processing for Liquid Level Detection

引用

7th International conference on Computing in Engineering and Technology, ICCET 2022

作者： Devare, Manoj Amity University Maharashtra Mumbai India

ISBN: (纸本)9789811927188

The liquid level detection in a transparent bottle is the requirement in the food, Beverage industry, Scene understanding, Chemical Laboratories, and Forensic sciences. The liquid level detection indicates the quantity of the liquid in the bottle to assure customer satisfaction. The liquid level in a transparent container is an edge, a sudden change in intensity. The problem of liquid level detection is an image processing application of the Canny Edge Detector. The image data set is prepared using different sizes, transparency, color, and orientation as straight, landscape, and reverse. The transparent bottle classification accuracy rate is 87.5%, and the overall accuracy rate is 69.56%. The Serial and parallel algorithms are developed to improve the speed of execution. The multiprocessing and concurrent process pool of the multiprocessing approach found 4.6 times better turnaround execution time over synchronous pool map, concurrent thread pool, asynchronous thread pool, and serial execution. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bottles

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An exploration of pre-processing approaches for iris spoof detectors 1

An exploration of pre-processing approaches for iris spoof d...

引用

1st International conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2022

作者： Verma, Palak Selwal, Arvind Sharma, Deepika Central University of Jammu Department of Computer Science and Information Technology Jammu and Kashmir181143 India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

Iris recognition has fascinated the attention of several real-life applications to offer secured and reliable human authentication. However, these systems are exposed to a diverse range of security breaches that include direct spoofing of the sensing device or using some indirect means to gain illegal access to the authentication system. To mitigate these spoof attacks on the sensor module, an anti-spoofing sub-module is integrated alongside the sensor that intelligently classifies a presented iris trait as real or fake. With the emergence of machine learning algorithms, the problem of spoof detection has become comparatively easier, where a model learns from training samples to detect the liveliness of a given image. Building a spoof detection model involves three important steps namely: pre-processing, feature extraction, and a classifier. In this study, we propound a comparative analysis of pre-processing techniques, which are widely deployed in data-driven iris spoof detectors (ISDs). The presented image pre-processing techniques are explored based on two categories of ISD mechanism namely;hand-crafted features or deep learning (DL)-based approaches. Our study reveals that pre-processing techniques significantly improves the quality of raw images that leads to better prediction accuracy. © 2022 IEEE.

关键词： Feature extraction

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Simplification and Compression Method Based on Classified Sparse Sampling for LiDAR Point Cloud 23

Simplification and Compression Method Based on Classified Sp...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Jian Lin, YuFang Chen, YuanXiang Zheng, MingKui Zhu, YuYao School of Advanced Manufacturing Fuzhou University Quanzhou China College of Physics and Information Engineering Fuzhou University Fuzhou China Beijing Ctr. Fujian Br. Patent Examin. Cooperation Center of the State Intellectual Property Office Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China

ISBN: (纸本)9781665494571

As a key component of point cloud acquisition technology, LiDAR (Light Detection and Ranging) acts as an eye in autonomous driving. The vehicle-mounted LiDAR point cloud system is the prerequisite foundation for the vehicle to be able to perform environmental perception, path planning, navigation, and other behaviors. In practical applications, LiDAR point clouds have a wide distribution range and a large amount of data, which poses major challenges to existing transmission and storage technologies. Aiming at the point cloud from automotive LiDAR in autonomous driving, we propose a point cloud lossy compression framework based on range image index and classified sparse sampling. This paper takes the original data packet as the compression target and converts it into a 2D range image and a 3D point cloud based on its data characteristics. On this basis, we can segment 3D point clouds into ground point and non-ground point cloud by applying progressive morphological filters. Then, we perform de-redundant sparse processing at different intensities on the two. A sparse 2D range image is generated by referring to the range image index of the downsampling 3D point cloud and is represented as a more compact form of one-dimensional range vector combined with its corresponding occupancy image and Morton sorting. Finally, the image coding method is used to further compress the point cloud. Experimental results show that our proposed method is better than the existing algorithms, thereby effectively reducing the spatial redundancy of the point cloud and achieving higher reconstruction quality. © 2021 IEEE.

关键词： Point cloud compression image coding Laser radar Three-dimensional displays Smart cities Redundancy Prediction algorithms

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Real-Time Scratched image Restoration Using Neural Networks

Real-Time Scratched Image Restoration Using Neural Networks

引用

Advanced Computing & Communication Technologies (ICACCTech), International conference on

作者： Harsh Sharma Rajeshwari Pradhan Harsh Mogha Shivpratap Singh Rupesh Kumar Computer Science and Engineering Chandigarh University Punjab India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Real-time image restoration is a cutting-edge tech-nology that accelerates or rebuilds images as soon as they are captured, processed, or released to correct issues such as noise, blur, and compression. This field is of great importance in applications such as medical imaging, surveillance, autonomous vehicles. and live video streaming where sharpness and image quality are fundamental The main challenge lies in developing algorithms that can quickly and efficiently restore image quality. It balances the need for high-speed processing with the need for precision and adaptability to different conditions. Advanced techniques Including deep learning and convolutional neural networks (CNN) are often used to achieve these goals. Moreover, it is essential to optimize these algorithms for real-time pro-cessing. Real-time performance in constrained hardware such as mobile devices or embedded systems As real-time image recovery technology continues to develop, It thus promises to significantly improve the quality of image data in a variety of domains. They provide sharper, more reliable images for critical applications.

关键词： Performance evaluation image quality Deep learning Surveillance Noise Noise reduction Streaming media Real-time systems image restoration Convolutional neural networks

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Anti-Occlusion Scale-Adaptive Correlation Tracking

Anti-Occlusion Scale-Adaptive Correlation Tracking

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Zhuanyang Chen Linjie Yang Yuan Zhang Luping Wang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Target tracking at high-speed conditions is highly desirable for embedded systems, such as visual navigation. However, existing methods face challenges in achieving real-time processing due to algorithm complexity and computational limitations. To address this issue, we propose an anti-occlusion scale-adaptive correlation tracker (OSCT) based on a Kernelized Correlation Filter (KCF), which considers tracking confidence and multiple features in embedded systems. Specifically, we employ a discriminative correlation filter to estimate the scale of the tracking target. Subsequently, our tracker dynamically adjusts the learning rate by evaluating the confidence of tracking results. Moreover, the OSCT uses a re-detector to re-detect the target in case of tracking failure. Instead of relying on a single feature, our method integrates multiple features to address variation and occlusion in tracking objects. Experimental results demonstrate that: 1) OSCT achieves an average precision of 0.778 at 20 pixels, representing an improvement of over 8% compared to other high-speed trackers. 2) The average running speed of OSCT is 32 FPS, outperforming LCT (26 FPS). Therefore, our approach is well-suited for long-term tracking on embedded platforms.

关键词： Visualization Target tracking Correlation Embedded systems Navigation image processing Machine learning Filtering algorithms Robustness Real-time systems

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Evaluation of NVIDIA Xavier NX Platform for Real-Time image processing for Fusion Diagnostics 28

Evaluation of NVIDIA Xavier NX Platform for Real-Time Image ...

引用

28th International conference on Mixed Design of Integrated Circuits and System (MIXDES)

作者： Jablonski, Bartlomiej Makowski, Dariusz Perek, Piotr Lodz Univ Technol Dept Microelect & Comp Sci Lodz Poland

ISBN: (纸本)9788363578190

Real-time image processing is the core component of image plasma diagnostics. Efficient algorithms enable machine protection, contributing to future steady-slate operation in nuclear fusion devices. The paper evaluates the applicability of the newest low-power NVIDIA Jetson Xavier NX platform for fusion diagnostics. This embedded NVIDIA Tegra System-on-a-Chip (SoC) integrates a Graphics processing Unit (GPU) and Central processing Unit (CPU) on a single chip. General-Purpose computing on Graphics processing Units (GPGPU) provides high parallelism that is advantageous in image-based calculations. The hardware differences in comparison to the previous NVIDIA Jetson TX2 based on Pascal architecture, including innovations introduced in the Volta architecture for NVIDIA Tegra, are signified. The evaluation is performed on the Wendelstein 7-X (W7-X) stellarator experimental data. Implemented algorithms detect and analyse thermal events in real-time utilising the embedded GPU. Investigated thermal events are strike-lines, overload hotspots, reflections and surface layers. Their detection allows the automated real-time risk evaluation incorporated in the feedback plasma control and interlock systems in the W7-X. The speedup resulting from the upgrade to the Xavier NX platform is presented in the paper, along with techniques pertaining to key hardware differences and programming aspects specific to the NVIDIA Tegra facilitating real-time computing on the low-power embedded device.

关键词： Graphics processing Unit General-Purpose Computing on Graphics processing Units image processing Plasma Diagnostics Fusion Diagnostics Embedded systems

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A Statistical Approach for Improving image Quality and Cell Quantification from Imaging Mass Cytometry

A Statistical Approach for Improving Image Quality and Cell ...

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Communication, image and Signal processing (CCISP), International conference on

作者： Xu Xiao Yating Lin Lei Zhang Wenxian Yang Rongshan Yu School of Informatics Xiamen University Xiamen China Aginome Scientific Xiamen China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

Imaging Mass Cytometry (IMC), a multiplexed imaging technology, has become a valuable tool in biomedical research due to its capability to measure over 100 markers theoretically. However, the presence of noise in IMC images can affect cell phenotyping and the precision of clinical analysis. To address this challenge, we propose IMCell, a highly effective and generalizable method that enhances image quality and cell quantification by leveraging biological priors. IMCell begins by generating decoy cells using Monte Carlo sampling and incorporates cell shape information for each protein channel. Subsequently, it estimates background noise from the expression of decoy cells by fitting Gaussian mixture models with the EM algorithm. Additionally, it identifies positive cell expressions by comparing distribution patterns between real cells and decoy cells. Based on the analysis results from IMCell, we further introduce an image quality index for comprehensive image quality assessment by combining noise and positive cell expressions. Experiment results demonstrate the efficacy of our proposed approach in enhancing image quality and facilitating downstream analysis. By improving the quality of IMC images, IMCell holds the potential to enhance our understanding of complex biological systems and disease mechanisms.

关键词： image quality Proteins Multiplexing Protein engineering Monte Carlo methods Shape Noise Signal processing algorithms Clustering algorithms Background noise

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A Review of Intrinsic image Decomposition

A Review of Intrinsic Image Decomposition

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image processing and Media Computing (ICIPMC), International conference on

作者： Siyuan Liu Xiaoyue Jiang Letian Liu Zhaoqiang Xia Sihang Dang Xiaoyi Feng School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

Intrinsic image decomposition (IID) aims to extract intrinsic components from natural images, namely shading and reflectance, which is widely applied in computer vision and image processing tasks for the better understanding of the objects and scenes. However, IID is a severely ill-posed problem, extra constraints should be applied to address the decomposition problem. In this paper, we reviewed recent papers about intrinsic image decomposition. The extra cues from lighting, shape, color and textures are used to constrain the ill-posed problem. These papers can be mainly classified into two categories: physics-based methods and learning-based methods. Physics-based methods mainly introduced extra constraints into an optimization framework to solve the IID problem. While learning-based methods mainly depend on an encoder-decoder network with the explicit constraint from the labeled intrinsic images. Additionally, extra priors are introduced into the network by the form of network structure or specific feature. In order to make the IID problem more practical, researchers may explore how to achieve fast and accurate image decomposition with lower computing resources through optimization algorithms and model design.

关键词： Learning systems Accuracy image color analysis Shape Computational modeling image decomposition Real-time systems

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