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IEEE International Conference on image processing

作者： Maniratnam Mandal Neil Birkbeck Balu Adsumilli Alan C. Bovik University of Texas at Austin YouTube Google Inc

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

User-generated content (UGC) is ubiquitous across the internet as a result of billions of videos and images being uploaded each day. All kinds of UGC media are affected by natural distortions, occurring both during and after capture, which are inherently diverse and commingled. These distortions have different perceptual effects based on the media content. Given recent dramatic increases in the consumption of short-form content, the analysis and control of their perceptual quality has become an important problem. Regardless of the content, many UGC videos have overlaid and embedded texts in them, which are visually salient. Hence text quality has a significant impact on the global perception of video or image quality and needs to be studied. One of the most important factors in perceptual text quality in user-generated media is legibility, which has been studied very little in the context of computer vision. Predicting text legibility can also help in text recognition applications such as image search or document identification. This work aims at modeling text legibility using computer vision techniques and thus studying the relationship between text quality and legibility. We propose a modified dataset variant of COCO-Text [1] and a model for predicting text legibility for both handwritten and machine-generated texts. We also demonstrate how models trained to predict text legibility can help in the prediction of text (perceptual) quality. The dataset and models can be accessed here https://***/research/Quality/***.

关键词： image quality Computer vision Text recognition Computational modeling User-generated content Media Predictive models

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Improved YOLOv8 Orchard Fruit Object Detection Algorithm Based on Enhanced Feature Fusion

Improved YOLOv8 Orchard Fruit Object Detection Algorithm Bas...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Zengming Li Haihui Lu Shuyuan Yang Wenjian Zhang Jianming Mo Zilu Zhou Shufei Huang Shijing Feng Kaijun Luo Xingjun Lin School of Modern Information Industry Guangzhou College of Commerce Huangpu China School of Information Technology & Engineering Guangzhou College of Commerce Huangpu China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Smart agriculture is becoming increasingly popular with the development of computer and artificial intelligence technology. The use of artificial intelligence technology to improve orchard management and enhance harvesting efficiency is an important technology. The current target detection algorithms based on YOLO can preliminarily complete the construction of the system. However, in these detection methods, the feature fusion part did not fully consider the actual contribution differences of different resolution feature maps to the final output, which may become a performance bottleneck when dealing with targets of different sizes and complexities. In response to the above issues, this article proposes an orchard fruit object detection algorithm based on improved YOLOv8. This algorithm improves the feature fusion part in YOLOv8 by introducing the BiFPN (Bidirectional Feature Pyramid Network) module. This method improves the accuracy and efficiency of the model in detecting objects of different sizes through a more refined feature fusion strategy. Through experiments, it has been proven that the proposed algorithm significantly improves the accuracy of object detection while ensuring computational efficiency, especially when dealing with challenging multi-scale objects. The improved algorithm improved accuracy by 16.5 percentage points, recall by 23.3 percentage points, mAP50 by 16.5 percentage points, and mAP50-95 by 20 percentage points on the avocado dataset. This research achievement has promoted the development of YOLOv8 in terms of performance in practical applications from a new perspective, and laid the foundation for future research on object ***.

关键词： YOLO Smart agriculture Accuracy image recognition machine learning algorithms Target recognition Face recognition machine learning Feature extraction Robustness

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A Methodological Review on Prediction of Multi-Stage Hypovigilance Detection Systems Using Multimodal Features

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IEEE ACCESS 2021年 9卷 47530-47564页

作者： Abbas, Qaisar Alsheddy, Abdullah Imam Muhammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Riyadh 11432 Saudi Arabia

Several hypovigilance detection systems (HDx) were developed to avoid road-side accidents due to driver fatigue. They have suffered from several limitations. Notably many of these are focused on center-head position to define an area of interest (often referred to as PERCLOS (percentage eye closure)) without considering the face occlusion problem, light illumination, and suffer poor response time. These HDx systems mostly depend on image processing, vision-based, and multisensor-based features. To address these problems, the author utilized vision, sensors, environmental, and vehicular-based features that integrated together by fusion to predict multistage of HDx. Lately, few studies have utilized the combination of multimodal features and deep learning (DL) architectures. Those multimodal-based features (M-HDx) were feasible to predict stages of driver fatigue (multi-stage). However, there is a need to critically measure the performance of these M-HDx by carrying out a comparative analysis to recognize multi-stage of fatigue in terms of hardware-based benchmarks. Moreover, it is important to evaluate the M-HDx systems using different features-set with respect to traditional and advanced machine learning techniques. Therefore, the primary aim of this work is in algorithm and feature modeling, then compare the advantages and differences with other work. In this paper, a different study is conducted compare to state-of-the-art survey articles by statistically measuring the performance. After experiments on M-HDx systems, this paper concludes that there is still a research gap to real-time development of multistage M-HDx systems. In the end, the paper summarizes the directions, challenges, and applications in the development of HDx systems to assist other researchers for further research.

关键词： Vehicles Fatigue Feature extraction Real-time systems Protocols Monitoring machine learning algorithms Computer vision driver hypovigilance driver distraction deep learning hybrid learning models multimodal features machine learning multisensor fusion transfer learning

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Status of the Deep Learning-Based Shattered Pellet Injection Shard Tracking at ASDEX Upgrade

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JOURNAL OF FUSION ENERGY 2024年第1期43卷 14-14页

作者： Illerhaus, Johannes Treutterer, W. Heinrich, P. Miah, M. Papp, G. Peherstorfer, T. Sieglin, B. Toussaint, U. v. Zohm, H. Jenko, F. Max Planck Inst Plasma Phys Garching Germany Tech Univ Munich Munich Germany Vienna Univ Technol Vienna Austria Graz Univ Technol Graz Austria Ludwig Maximilians Univ Munchen Munich Germany

Plasma disruptions pose an intolerable risk to large tokamaks, such as ITER. If a disruption can no longer be avoided, ITER's last line of defense will be the Shattered Pellet Injection. An experimental test bench was created at ASDEX Upgrade to inform the design decisions for controlling the shattering of the pellets and develop the techniques for the generation of the fragment distributions necessary for optimal disruption mitigation. In an effort to analyze the videos resulting from the more than 1000 tests and determine the impact of different settings on the resulting shard cloud, an analysis pipeline, based on traditional computer vision (CV), was created. This pipeline enabled the analysis of 173 of the videos, but at the same time showed the limits of traditional CV when applied in applications with a highly heterogeneous dataset such as this. We created a machine learning-based (ML) alternative as a drop-in replacement to the original image processing code using a semantic segmentation model to exploit the innate adaptability and robustness of deep learning models. This model is capable of labeling the entire dataset quickly, accurately and reliably. This contribution details the implementation of the ML model and the current state and future plans of the project.

关键词： machine learning Deep learning Computer vision Disruption mitigation Fusion research Shattered Pellet Injection

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Patch-Attention GAN: image Translation using BiFormer and Attention Framework

Patch-Attention GAN: Image Translation using BiFormer and At...

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Computer vision, image and Deep Learning (CVIDL), International Conference on

作者： Dingwen Xiao Huiqing Wu School of Science The Hong Kong University of Science and Technology Hong Kong China Faculty of Science and Technology BNU-HKBU United International College Zhuhai China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

image-to-image translation has long been recognized as a crucial undertaking within the field of computer vision, owing to its broad applications in domain adaption. Existing methods differ in Generative Adversarial Network framework and model structure. Attention-Guided training framework absorbs the input image’s semantic information based on the translation task. However, it ignores the local context for content extraction and lacks mask comparison in the loss function. The attention mechanism is widely regarded as an effective approach for addressing contextual challenges in machine learning models. By augmenting the model’s capacity to capture long-range dependencies and concentrate on the most salient aspects of the input sequence, the attention mechanism significantly enhances its contextual understanding. Bi-level routing attention stands out among other mechanisms for its computation speed and memory cost. In this paper, we propose an image translation algorithm called Patch-Attention Generative Adversarial Network that combines foreground-background separation processing, bi-level routing attention, and content loss. Our proposed method has been extensively evaluated on diverse datasets, yielding compelling experimental results. These findings demonstrate the substantial performance boost achieved by our approach in the domain of image translation. The generated image is more clarified and understandable compared with other existing approaches.

关键词： Training Computer vision Visualization Attention mechanisms Computational modeling Semantics Generative adversarial networks

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A FAST AND COST-EFFECTIVE IMAGING SYSTEM FOR FINE-SCALE TOOL CONDITION MONITORING IN ULTRASONIC METAL WELDING 18

A FAST AND COST-EFFECTIVE IMAGING SYSTEM FOR FINE-SCALE TOOL...

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ASME 18th Int Mfg Sci and Engn Conf held jointly with the 51st N Amer Res Conf / Japan-Soc-of-Mech-Engineers Int Conf on Leading Edge Mfg/Mat and Proc

作者： Dong, Zhiqiao Chen, Qianmeng Lu, Kuan-Chieh Shao, Chenhui Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA

ISBN: (纸本)9780791887240

In industrial-scale applications of ultrasonic metal welding (UMW), tool condition monitoring (TCM) is one of the most important maintenance tasks because tool health degrades quickly, and tool condition impacts the process physics and joint quality significantly. Moreover, tool replacement constitutes a notable portion of maintenance costs. In UMW, tool health degradation occurs mainly in the form of changes in tool surface geometry. Online TCM, which uses in-situ sensing data to indirectly classify tool conditions, has demonstrated to be effective;however, such indirect methods cannot provide a detailed characterization of tool surface profiles. On the other hand, direct measurements of tool surfaces require expensive and time-consuming high-resolution 3D metrology, which substantially increases the cost of quality and delays maintenance decision-making. To overcome these challenges, this paper develops a fast and cost-effective imaging system for fine-scale TCMinUMW. The imaging system mainly consists of a macro lens and a Raspberry Pi (RPI) high quality camera mounted over a linear rail driven by a stepper motor, and the full system is controlled through RPI GPIO (general-purpose input/output). The imaging system is used in conjunction with coaxial illumination, which enhances tool surface features, to capture a photo of a cast reproducing tool surface geometry. Then, image processing techniques are developed to characterize tool surface profiles and features. We demonstrate the effectiveness of the proposed TCMstrategy using tools in three distinct conditions. Results show that the TCM imaging system can effectively reconstruct critical fine-scale geometric features of tools, thus enabling more responsive, interpretable, and reliable TCM for UMW.

关键词： tool condition monitoring surface metrology ultrasonic metal welding machine vision computer vision maintenance quality control

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Preprocessing to improve the accuracy of recognition of radioelement components in automated assembly devices 9

Preprocessing to improve the accuracy of recognition of radi...

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Conference on Optical Metrology and Inspection for Industrial applications IX Part of SPIE/COS Photonics Asia Conference

作者： Semenishchev, Evgeny A. Zelensky, Aleksandr Zdanova, Marina Sizyakin, Roman Ilukhin, Yuriy Voronin, Viacheslav Tula State Univ TulSU Lab Cognit Technol & Simulat Syst 92 Sq Lenina Tula 300012 Tula Region Russia Moscow State Tech Univ STANKIN Ctr Cognit Technol & Machine Vis 1a Vadkovsky Moscow 127055 Russia

ISBN: (纸本)9781510657052;9781510657045

The article presumes a data processing algorithm that improves the accuracy of recognition of radio-electronic components in devices for automated installation. The paper proposes the use of a multicriteria filtering method that allows you to automatically change the smoothing coefficient. Varying the coefficient allows both reducing the noise component and preserving the boundaries of the radio elements without blurring. In order to enhance the contours of objects, a data simplification method is applied using the technique of reducing the range of clusters of color gradient histograms while preserving the shapes of objects. At the stage of detecting the boundaries of the elements and forming the structure of the elements of the radio component base, a modified one-dimensional two-criteria method is used. The combined analytical approach allows detection of the boundaries of radioelements and increases the productivity of the process. As test data used to evaluate the effectiveness, pairs of test images obtained by sensors fixed at various magnifications with a resolution of 1024x768 (8 bit, color image, visible range) are used. images of simple shapes are used as analyzed objects.

关键词： control systems preprocessing machine vision electronics radioelement robotic system

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Role of Thermal images in Various applications of Computer vision

Role of Thermal Images in Various Applications of Computer V...

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Intelligent Engineering and Management (ICIEM), International Conference on

作者： Ravina Gupta Sarika Jain Manoj Kumar AIIT Amity University Noida India EIS University of Wollongong Dubai

Object detection is an advanced area of image processing and computer vision. Its major applications are in surveillance, autonomous driving, face recognition, anomaly detection, traffic management, agriculture etc. This paper focuses on various object detection techniques in thermal images. A thermal imaging sensor is a device that creates an image by analyzing temperature differences between different objects in a scene and detecting radiation from those objects. In recent years, many machine learning and deep learning algorithms have been used to recognize objects in thermal images. This study makes a comparison of YOLO, YOLO DarkNet, Retinex algorithm, CNN-based machine learning model Support Vector machine (SVM), and Gaussian Mixture Model (GMM), Mixer of Gaussian (MoG), Mean Shift Approach, Faster R-CNN and Deep Neural Network along with different datasets.

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CSIM: A COPULA-BASED SIMILARITY INDEX SENSITIVE TO LOCAL CHANGES FOR image QUALITY ASSESSMENT

arXiv

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arXiv 2024年

作者： El Ghazouali, Safouane Michelucci, Umberto El Hillali, Yassin Nouira, Hichem TOELT LLC AI lab Winterthur Switzerland Polytechnique Haut-de-France Valenciennes France LNE-CNAM Paris France

image similarity metrics play an important role in computer vision applications, as they are used in image processing, computer vision and machine learning. Furthermore, those metrics enable tasks such as image retrieval, object recognition and quality assessment, essential in fields like healthcare, astronomy and surveillance. Existing metrics, such as PSNR, MSE, SSIM, ISSM and FSIM, often face limitations in terms of either speed, complexity or sensitivity to small changes in images. To address these challenges, a novel image similarity metric, namely CSIM, that combines real-time while being sensitive to subtle image variations is investigated in this paper. The novel metric uses Gaussian Copula from probability theory to transform an image into vectors of pixel distribution associated to local image patches. These vectors contain, in addition to intensities and pixel positions, information on the dependencies between pixel values, capturing the structural relationships within the image. By leveraging the properties of Copulas, CSIM effectively models the joint distribution of pixel intensities, enabling a more nuanced comparison of image patches making it more sensitive to local changes compared to other metrics. Experimental results demonstrate that CSIM outperforms existing similarity metrics in various image distortion scenarios, including noise, compression artifacts and blur. The metric’s ability to detect subtle differences makes it suitable for applications requiring high precision, such as medical imaging, where the detection of minor anomalies can be of a high importance. The results obtained in this work can be reproduced from this Github repository: https://***/safouaneelg/copulasimilarity. © 2024, CC BY-SA.

关键词： image quality

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Real-Time Drowsiness Detection System using Facial Landmarks and Eye Blink Analysis 5

Real-Time Drowsiness Detection System using Facial Landmarks...

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5th International Conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Manhas, Vishal Singh Sahani, Prince Upase, Shankar Venkatrao Ashtagi, Rashmi Under Graduate Student Department Of Computer Engineering Vishwakarma Institute Of Technology Pune India Department Of Computer Engineering Vishwakarma Institute Of Technology Pune India

ISBN: (纸本)9798400709418

This paper explores the critical issue of driver drowsiness and its significant contribution to road accidents. A real-time drowsiness detection system that harnesses the power of computer vision and facial landmark recognition to enhance safety and prevent accidents in situations where drowsiness poses a significant risk, such as drowsy driving. This system combines various libraries and techniques, including Dlib for face detection and facial landmark identification and OpenCV for image processing. Its core functionality involves capturing video frames from a camera feed, locating faces within each frame, and then focusing on the eyes and eyebrows to assess the individual's level of alertness. This assessment is primarily based on the analysis of eye blinking patterns, with specific attention to the blink ratio, which serves as a key indicator. Depending on the calculated blink ratio, the system classifies the person's state into three distinct categories: "SLEEPING,""Drowsy,"or "Active."These categories are conveyed to the user through real-time text overlays on the video feed, each represented by a specific color. The value of this project lies in its potential to prevent accidents caused by drowsiness, a major factor contributing to road accidents worldwide. By continuously monitoring and evaluating the individual's state of alertness, this system provides immediate feedback and intervention when drowsiness is detected, thus contributing to safer driving and potentially extending its applicability to other domains where alertness is critical, such as operating heavy machinery or monitoring individuals in critical work environments. Furthermore, this project offers a foundation for further development and integration with advanced technologies, potentially evolving into a comprehensive safety system with broader applications in areas where real-time alertness assessment is essential for reducing risks and enhancing overall safety. © 2023 ACM.

关键词： Computer vision

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