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检索条件"任意字段=Conference on Image Quality and System Performance IX"

共 7128 条记录，以下是31-40 订阅

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New Optical Imaging system for Determining Wear Criteria for Cutting Inserts 9

New Optical Imaging System for Determining Wear Criteria for...

引用

conference on Optical Metrology and Inspection for Industrial Applications ix Part of SPIE/COS Photonics Asia conference

作者： Pivkin, Petr M. Grechishnikov, Vladimir A. Ershov, Artem A. Nadykto, Alexey B. Moscow State Univ Technol STANKIN Dept Cutting Tools & Machining Technol Moscow 127055 Russia Lab Micromachining Technol STANKIN Moscow 127055 Russia Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow 127055 Russia Moscow State Univ Technol STANKIN Dept High Efficiency Proc Technol Moscow 127055 Russia

ISBN: (纸本)9781510657052;9781510657045

In mechanical engineering, cutting tool wear is a major factor in the functional and parametric failure of a cutting tool. The loss of tool performance causes failures in the production of suitable products, as well as machine downtime. In this connection, accurate measurement of wear of the cutting wedge at various stages of the technological process is extremely important. The article proposes a method for recognizing wear zones on the back surfaces of cutting inserts by image processing. Indicators have been formed for assigning a failure criterion for various quality factors. For the formation of a measurement system, a three-stage control method with the rationale for operational recommendations is proposed. The correlation of wear by image processing has been compared with the optical method currently applied in manual mode, which greatly reduces the efficiency of production quality control.

关键词： laser ablation machine computer-aided design control system performance

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Explaining Face Recognition Through SHAP-Based Pixel-Level Face image quality Assessment

Explaining Face Recognition Through SHAP-Based Pixel-Level F...

引用

IEEE International Joint conference on Biometrics (IJCB)

作者： Biagi, Clara Rethfeld, Louis Kuijper, Arjan Terhoerst, Philipp Univ Paderborn Paderborn Germany Tech Univ Darmstadt Darmstadt Germany Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany

ISBN: (纸本)9798350337266

Biometric face recognition models are widely used in many different real-world applications. The output of these models can be used to make decisions that may strongly impact people. However, an explanation of how and why such outputs are derived is usually not given to humans. The lack of explainability of face recognition models leads to distrust in their decisions and does not encourage their use. The performance of face recognition models is influenced by the quality of the input image. In case the quality of a face image is too low, the face recognition system will reject it to avoid compromising its performance. The quality is evaluated by Face image quality (FIQ) approaches, which assigned quality scores to the input images. Pixel-level face image quality (PLFIQ) increases the explainability of quality scores by explaining face image quality at the pixel level. This allows the users of face recognition systems to spot low-quality areas and allows them to make guided corrections. Previous works introduced the concept of PLFIQ and proposed evaluation procedures. This work proposes a new way of computing PLFIQ values depending on given FIQ methods using Shapley Values. They score the contribution of each pixel to the overall image quality evaluation. Therefore, Integrating Shapley Values increases the explainability of the FIQ models. Results show that using these methods leads to significantly better and more robust PLFIQ values estimates and thus provide better explainability.

关键词： image quality

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COMPARATIVE STUDY OF SALIENCY- AND SCANPATH-BASED APPROACHES FOR PATCH SELECTION IN image quality ASSESSMENT 30

COMPARATIVE STUDY OF SALIENCY- AND SCANPATH-BASED APPROACHES...

引用

30th IEEE International conference on image Processing (ICIP)

作者： Chetouani, Aladine Univ Orleans Lab PRISME Orleans France

ISBN: (纸本)9781728198354

Over the past few years, the development of deep learning-based methods has revolutionised the field of image quality assessment. These methods have shown remarkable success in estimating the quality of 2D images. However, most of these approaches are trained using small patches of the image, with the subjective score of the entire image serving as the target. assuming that all patches have an equal perceptual impact on the image. This assumption is not entirely consistent with our Human Visual system (HVS), which processes different regions of the image to form an overall perception. In this study, we focus on the use of saliency information to estimate image quality. In this context, we explore the use of saliency information to estimate image quality by selecting only the most perceptually relevant patches. Specifically, we evaluate the accuracy of saliency- or scanpath-based patch selection methods for predicting 2D image quality. Our goal is to determine which approach provides the most accurate estimation of image quality and whether the use of saliency information can improve the performance of deep learning-based methods for image quality assessment.

关键词： Visual Attention scanpath prediction adversarial training inter-observer consistency

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Transformer-based image Compression with Variable image quality Objectives

Transformer-based Image Compression with Variable Image Qual...

引用

Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and conference (APSIPA ASC)

作者： Kao, Chia-Hao Chen, Yi-Hsin Chien, Cheng Chiu, Wei-Chen Peng, Wen-Hsiao Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9798350300673

This paper presents a Transformer-based image compression system that allows for a variable image quality objective according to the user's preference. Optimizing a learned codec for different quality objectives leads to reconstructed images with varying visual characteristics. Our method provides the user with the flexibility to choose a trade-off between two image quality objectives using a single, shared model. Motivated by the success of prompt-tuning techniques, we introduce prompt tokens to condition our Transformer-based autoencoder. These prompt tokens are generated adaptively based on the user's preference and input image through learning a prompt generation network. Extensive experiments on commonly used quality metrics demonstrate the effectiveness of our method in adapting the encoding and/or decoding processes to a variable quality objective. While offering the additional flexibility, our proposed method performs comparably to the single-objective methods in terms of rate-distortion performance.

关键词： image compression

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From Raindrops To Pixels: A Novel Model to Predict ADAS Camera image Degradation in Rain

From Raindrops To Pixels: A Novel Model to Predict ADAS Came...

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IEEE International conference on Smart Mobility (SM)

作者： Li, Long Pao, Wing Yi Agelin-Chaab, Martin Komar, John Ontario Tech Univ Fac Engn & Appl Sci Oshawa ON Canada Ontario Tech Univ ACE Climat Aerodynam Wind Tunnel Oshawa ON Canada

ISBN: (纸本)9798350390148;9798350390131

The development of Advanced Driver Assistance systems (ADAS) has led to safer roads through the implementation of automated features such as blind spot monitoring, lane departure assist, and collision mitigation braking. In ADAS, cameras are one of the sensor types required to capture environmental information. However, camera performance degrades in adverse weather, especially in rain, due to droplet impacts, which cause changes to the light trajectory, thus decreasing object recognition and detection quality. While it is commonly understood that camera image quality directly correlates to rain intensity, understanding is limited in the exact relationship between incoming rain and image degradation in a dynamic environment due to the complexity of the multiphase interactions. In this study, a novel wind-driven rain system is deployed in conjunction with a spray-nozzle system at the ACE Climatic Aerodynamic Wind Tunnel. Using the controlled and repeatable testing methodology, objective camera image quality metrics are correlated with the incoming rain conditions through theoretical modeling. The model shows consistent trends across a variety of test conditions with different surface materials. The results prove the validity of the proposed model, which provides the impetus for further development to include additional physical interactions, such as droplet dynamics and vehicle aerodynamics.

关键词： camera sensor ADAS autonomous vehicle image quality rain weather wind tunnel soiling MSE PSNR multiphase

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Region of Interest Scalable image Compression Using Semantic Communications

Region of Interest Scalable Image Compression Using Semantic...

引用

2024 IEEE International conference on Consumer Electronics, ICCE 2024

作者： Samarathunga, Prabhath Gowrisetty, Vishnu Fernando, Thanuj Ganearachchi, Yasith Fernando, Anil University of Strathclyde Department of Computer and Information Sciences Glasgow United Kingdom

ISBN: (纸本)9798350324136

Growing consumer demand for media content across a wide range of devices has made scalable image compression vital in the current media landscape. image compression is conventionally achieved by means of statistical signal processing, but since recently deep learning techniques have been widely adopted as well. Capabilities of such systems also enable accurate identification of regions of interest in images, leading to optimized performance in most applications. This paper proposes a region-of-interest scalable image compression system using semantic communications, where an autoencoder-based semantic encoder performs the base-level compression, while a Semantic Mask Extracting Transformer (SeMExT) enables identification of regions of interest to create enhancement layers with different quality levels using a scalable JPEG encoder. When benchmarked against scalable JPEG across a variety of images, the proposed system demonstrates significantly improved compressive performance. The base layer achieved 61.4 times more compression on average, along with better rate-distortion performance at any given quality level. © 2024 IEEE.

关键词： Deep Neural Networks image Compression Region of Interest Scalable image Compression Semantic Communications

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LGCRS: LLM-Guided Representation-Enhancing for Conversational Recommender system 33rd

LGCRS: LLM-Guided Representation-Enhancing for Conversationa...

引用

33rd International conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Wang, Ruobing He, Xin Gu, Hengrui Wang, Xin Jilin Univ Sch Artificial Intelligence Changchun 130012 Peoples R China

ISBN: (纸本)9783031723551;9783031723568

Conversational recommender systems primarily focus on acquiring user preferences through real-time interactions. However, their effectiveness in modeling user preferences is constrained by the amount of conversational information. Existing conversational recommendation methods typically leverage external knowledge graph to enhance recommendation performance, potentially overlooking valuable textual information related to users and items. With significant natural language understanding capabilities, large language models (LLMs) have the potential to improve the performance of conversational recommendation task by improving the quality of text representations. However, the incorporation of raw textual information may inadvertently introduce noise to recommendations. Additionally, the semantic gap between the textual information and the collaborative signals could potentially degrade the performance of the conversational recommender system. To tackle the aforementioned challenges, we propose a LLM-guided representation-enhancing method for conversational recommender system, which fuses collaborative signals and semantic information to improve recommendation performance and generate high-quality responses. Specifically, we leverage LLMs to refine item profiles while reducing noise in text information, construct fused item representations across multiple aspects, and align the LLMs-enhanced semantic representation with the CF-side rational representation through regularization terms in the training stage. Experiments on two publicly available conversational recommendation datasets show that our method exhibits better performance in both recommendation and conversation tasks.

关键词： Conversational Recommender system Large Language Models Representation Learning

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A quality Evaluation Method for images in Adverse Weather Conditions 2

A Quality Evaluation Method for Images in Adverse Weather Co...

引用

2nd IEEE International conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Shao, Zhengxi Chen, Jifeng Lin, Wenxin School of Information and Electromechanical Engineering Hunan International Economics University Changsha China

ISBN: (纸本)9798350373646

By adopting an adaptive image quality evaluation method under adverse weather conditions, an advanced image defogging system has been developed. This system aims to predict the optimal algorithm for achieving the best image defogging results. The system integrates nine efficient defogging algorithms. To comprehensively evaluate the defogging performance of these algorithms, a set of evaluation metrics including PSNR, SSIM, LPIPS, ENL, MSE, and RMSE is employed to establish a comprehensive evaluation method. By calculating the values of these evaluation metrics, the optimal algorithm suitable for the image is predicted, and the defogged image produced by the optimal algorithm is processed to output the best-defogged image finally. All predicted data is stored in a database. When defogging an image, if there is matching data in the database, the corresponding optimal algorithm and image processing are directly selected, thereby enhancing the overall defogging efficiency. © 2024 IEEE.

关键词： Algorithm Prediction Deep Learning Evaluation Method image Defogging system image Processing

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image Noise Compensation in Industrial Implementation of the SFF Method Using Controllable Optics 25

Image Noise Compensation in Industrial Implementation of the...

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25th International Carpathian Control conference (ICCC)

作者： Lenty, Bartosz AGH Univ Krakow Fac Mech Engn & Robot Krakow Poland

ISBN: (纸本)9798350350708;9798350350715

To keep up with increasing production efficiency and rising consumer demands, the industry is increasingly implementing quality control systems. Vision systems are starting to play an increasingly important role in quality control. Such systems allow the assessment of the geometry and surface of a part in an autonomous and non-contact manner. 3D vision systems are a particular case of vision systems. 3D vision systems allow the measurement of a scene, including depth information. As a result, a broader analysis of dimensions and defects is possible which for 2D systems may remain neglected. Only a few 3D imaging methods are applicable in industrial conditions. Scanners based on laser triangulation, stereovision and time of flight technologies are available. This article discusses the industrial implementation of another 3D image acquisition method - Shape From Focus. Shape from focus estimates the scene depth by comparing local sharpness in the image during the movement of the image plane. Usually image plane position is changed by the movement of the vision system. The novel approach is the use of electrically controlled optics to replace the movement of the system. It recognizably improves 3D scene acquisition performance. Electrically controlled optics also introduces the undesirable implications of changing the focusing power and magnification during acquisition. This results in noise in the 3D image. The article proposes a compensation algorithm to reduce the impact of the mentioned factors on the final 3D image. The paper discusses the structure of the stand used in the research. Factors affecting the quality of the 3D image obtained are presented and compensation algorithm is proposed. The study focuses on images of actual industrial materials.

关键词： Shape-from-focus quality control 3D imaging noise industrial

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Design and Implementation of Adaptive Gaussian Denoising Algorithm Based on FPGA 7

Design and Implementation of Adaptive Gaussian Denoising Alg...

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7th International conference on Automation Electronics and Electrical Engineering

作者： Chen, Xinghao Beihang Univ Sch Elect Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350377040;9798350377033

With the continuous progress of image processing and machine vision technology, the demand for efficient and real-time processing is becoming more and more prominent, especially in the field of high-noise image processing. In this study, an adaptive Gaussian filtering algorithm is proposed, which is implemented based on FPGA and aims to improve the computational efficiency and real-time performance of the image processing system. Compared with the traditional fixed-weight filter, this algorithm is able to dynamically adjust the filtering parameters according to different noise environments, effectively balancing noise suppression and image detail retention. We coded the algorithm using Verilog hardware description language and verified it on PYNQ-Z2 FPGA platform. The experimental results show that the adaptive algorithm outperforms the fixed-weight filtering method in terms of performance, especially in terms of noise suppression and detail preservation. Meanwhile, the FPGA hardware implements the reduction of filtering delay and optimization of resource consumption, making it well suited for real-time applications. This study demonstrates the promise of FPGA adaptive filtering for applications in medical imaging, remote sensing, and intelligent surveillance, which have stringent requirements for high-performance and high-efficiency processing. This research provides new hardware solutions for real-time, high-quality image processing in constrained environments.

关键词： FPGA image denoising gaussian filtering adaptive algorithm

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