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7th International Conference on machine vision and applications (ICMVA)

作者： Liu, Shuang Hanson, Steen G. Takeda, Mitsuo Wang, Wei Xian Technol Univ Sch Optoelect Engn Int Ctr Optic Res & Educ iCORE Xian 710032 Shaanxi Peoples R China Tech Univ Denmark DTU Foton Dept Photon Engn DK-4000 Roskilde Denmark Utsunomiya Univ Ctr Opt Res & Educ CORE 7-1-2 Yoto Utsunomiya Tochigi 3218585 Japan Heriot Watt Univ Sch Engn & Phys Sci Inst Photon & Quantum Sci Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9798400716553

Speckle field is one of the most information-rich light fields related to plentiful physical characteristics at present that can be used to provide high-resolution surface topography information or applied to image reconstruction, image enhancement and other fields. However, the most study of speckle image recovery for monochromatic wavelength ignores a large amount of real object information. In this paper, the amplitude information of colored speckle recovery in optical imaging is studied, as might be seen if red, green, and blue lasers illuminate a rough surface with different reflectivity at these three wavelengths. We derived the expression for color speckle distribution and designed an imaging system with a pupil stop, normal plane-wave incidence on the diffuser, and a camera to observe the colored speckled image. In order to analyze the simulation experiment, two aspects are studied: phase shift and average speckle size. The results show that more characteristic information is recovered from the colored speckle image.

关键词： Colored speckle image recovery image processing

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Robot Automatic Wire Welding Based on machine vision 5

Robot Automatic Wire Welding Based on Machine Vision

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Liu, Xinsheng Li, Lidan Xing, Hongmei Wang, Shaohua Zhang, Chuan Diao, Xiyao Zhuo, Zhang China North Vehicle Res Inst Beijing Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Currently, the welding process between electrical connectors and multi-core wires mainly relies on manual operation. This traditional method not only consumes a lot of time and manpower, but also long-term operation may cause certain physical burden and health hazards to the operator. Therefore, researching and implementing automated welding between electrical connectors and multi-core wires has become an urgent problem to be solved. On the basis of summarizing the current research status at home and abroad, the software and hardware parts of the system were designed to meet the requirements of identifying and positioning welding circular electrical connectors. By introducing image processing and machine vision technology, adopting a dual machine collaboration approach and based on machine vision methods, automatic wire welding of electrical connectors has been achieved, improving welding efficiency and reducing the labor intensity of operators. In addition, it is also conducive to promoting the development of industrial automation.

关键词： Industrial robot Automatic welding Visual image Aviation plug Wires

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Pure vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT image Denoising

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024年第5期37卷 2669-2687页

作者： Marcos, Luella Babyn, Paul Alirezaie, Javad Toronto Metropolitan Univ Dept Elect Biomed & Comp Engn 350 Victoria St Toronto ON M5B 2K3 Canada Univ Saskatchewan Dept Med Imaging 105 Adm Pl Saskatoon SK S7N 0W8 Canada

Convolutional neural networks (CNN) have been used for a wide variety of deep learning applications, especially in computer vision. For medical image processing, researchers have identified certain challenges associated with CNNs. These challenges encompass the generation of less informative features, limitations in capturing both high and low-frequency information within feature maps, and the computational cost incurred when enhancing receptive fields by deepening the network. Transformers have emerged as an approach aiming to address and overcome these specific limitations of CNNs in the context of medical image analysis. Preservation of all spatial details of medical images is necessary to ensure accurate patient diagnosis. Hence, this research introduced the use of a pure vision Transformer (ViT) for a denoising artificial neural network for medical image processing specifically for low-dose computed tomography (LDCT) image denoising. The proposed model follows a U-Net framework that contains ViT modules with the integration of Noise2Neighbor (N2N) interpolation operation. Five different datasets containing LDCT and normal-dose CT (NDCT) image pairs were used to carry out this experiment. To test the efficacy of the proposed model, this experiment includes comparisons between the quantitative and visual results among CNN-based (BM3D, RED-CNN, DRL-E-MP), hybrid CNN-ViT-based (TED-Net), and the proposed pure ViT-based denoising model. The findings of this study showed that there is about 15-20% increase in SSIM and PSNR when using self-attention transformers than using the typical pure CNN. Visual results also showed improvements especially when it comes to showing fine structural details of CT images.

关键词： CT denoising Convolutional neural networks Deep learning Medical image processing machine learning vision transformers

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A survey on deep learning in UAV imagery for precision agriculture and wild flora monitoring: Datasets, models and challenges

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SMART AGRICULTURAL TECHNOLOGY 2024年 9卷

作者： Epifani, Lorenzo Caruso, Antonio Palazzo Fiorini Dept Math & Phys Ennio Giorgi Campus Ecotekne I-73100 Lecce Italy

machine learning is the state of the art for many recurring tasks in several heterogeneous domains. In the last decade, it has been also widely used in Precision Agriculture (PA) and Wild Flora Monitoring (WFM) to address a set of problems with a big impact on economy, society and academia, heralding a paradigm shift across the industry and academia. Many applications in those fields involve image processing and computer vision stages. Remote sensing devices are very popular choice for image acquisition in this context, and in particular, Unmanned Aerial Vehicles (UAVs) offer a good tradeoff between cost and area coverage. For these reasons, research literature is rich of works that face problems in Precision Agriculture and Wild Flora Monitoring domains with machine learning/computer vision methods applied to UAV imagery. In this work, we review this literature, with a special focus on algorithms, model sizing, dataset characteristics and innovative technical solutions presented in many domain-specific models, providing the reader with an overview of the research trend in recent years.

关键词： machine learning Deep neural networks image analysis Unmanned aerial vehicles Agritech

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LEGIT: TEXT LEGIBILITY FOR USER-GENERATED MEDIA 31

LEGIT: TEXT LEGIBILITY FOR USER-GENERATED MEDIA

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2024 International Conference on image processing

作者： Mandal, Maniratnam Birkbeck, Neil Adsumilli, Balu Bovik, Alan C. Univ Texas Austin Austin TX 78712 USA YouTube Mountain View CA USA Google Inc Mountain View CA USA

ISBN: (纸本)9798350349405;9798350349399

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 Assessment Text Quality Text Legibility User-generated Content

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The generative adversarial networks and its application in machine vision

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ENTERPRISE INFORMATION SYSTEMS 2022年第2期16卷 326-346页

作者： Zhang, Dongbo Huang Dongru Kang, Lanlan Zhang, Wei Guangdong Inst Intelligent Mfg Guangdong Key Lab Modern Control Technol Guangzhou Guangdong Peoples R China Guangdong Univ Finance & Econ Publ Procurement Res Ctr Guangzhou Guangdong Peoples R China JiangXi Univ Sci & Technol Dept Comp Engn 156 KeJia Ave Ganzhou Jiangxi Peoples R China Natl Radio & Televis Adm Acad Broadcasting Sci Beijing Peoples R China

In recent years, the model of improved GAN has been widely applied in the field of machine vision. It not only covers the traditional image processing, but also includes image conversion, image synthesis and so on.. Firstly, this paper describes the basic principles and existing problems of GAN, then introduces several improved GAN models, including Info-GAN, DC-GAN, f-GAN, Cat-GAN and others. Secondly, several improved GAN models for different applications in the field of machine vision are described. Finally, the future trend and development of GAN are prospected.

关键词： Deep learning machine vision generative network discriminative network generative adversarial network

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A Data Augmentation Method for Data-Driven Component Segmentation of Engineering Drawings

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2024年第1期24卷 011001页

作者： Zhang, Wentai Joseph, Joe Chen, Quan Koz, Can Xie, Liuyue Regmi, Amit Yamakawa, Soji Furuhata, Tomotake Shimada, Kenji Kara, Levent Burak Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.

关键词： computational synthesis computer aided design data-driven engineering machine learning for engineering applications

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Exploring optimizer efficiency for facial expression recognition with convolutional neural networks

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JOURNAL OF ENGINEERING-JOE 2025年第1期2025卷

作者： Madni, Syed Hamid Hussain Pathmanatan, Lokessh A. L. Faheem, Muhammad Shahzad, Hafiz Muhammad Faisal Shah, Sajid Univ Southampton Malaysia Sch Elect & Comp Sci Johor Baharu Malaysia Univ Teknol Malaysia Fac Comp Skudai Johor Malaysia Univ Vaasa Sch Technol & Innovat Vaasa Finland VTT Tech Res Ctr Finland Ltd Espoo Finland Univ Sargodha Dept Comp Sci Sargodha Pakistan

It's widely accepted that human expressions, considering for roughly sixty percent of all daily interactions, are among the most authentic forms of communication. Numerous studies are being conducted to explore the importance of facial expressions and the development of machine-assisted recognition techniques. Significant progress is being made in facial and expression recognition, largely due to the rapid growth of machine learning and computer vision. A variety of algorithmic approaches and methods exist for detecting and recognizing facial expressions and features. This study investigates various optimization algorithms used with convolutional neural networks for facial expression recognition. The primary focus is on Adam, RMSProp, stochastic gradient descent and AdaMax optimizers. A comprehensive comparison is being made, examining the key aspects of each optimizer, including its advantages and disadvantages. Furthermore, the study also incorporates findings from recent studies that used these optimizers in various applications, highlighting their performance in terms of training time and precision. The aim is to illuminate the process of selecting a suitable optimizer for specific applications, analysing the trade-offs between training speed and higher accuracy levels. Moreover, this study provides a deeper analysis of the role optimizers play in machine learning-based facial expression recognition models. The discussion of the technical challenges posed by these optimizers and future improvements for achieving much more optimal results concludes the study.

关键词： image classification image processing image recognition optimisation convolution neural network machine leanring

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Research on Badminton Movement machine Learning Model Based on Computer vision Technology 2

Research on Badminton Movement Machine Learning Model Based ...

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2nd IEEE International Conference on image processing and Computer applications, ICIPCA 2024

作者： Zong, Cheng Hohhot Vocational College Hohhot China

ISBN: (纸本)9798350360240

This paper aims to explore an innovative method combining computer vision and machine learning to accurately identify and analyze various movements in badminton. This paper first summarizes the application prospect of computer vision in the field of sports analysis, and introduces its specific application scenarios in badminton in detail. By constructing a complete technical framework of image preprocessing module, feature extraction algorithm and deep learning model, the complex movements of badminton players such as swing, stroke and moving pace are captured and analyzed. In the research process, we used multi-view image fusion and key point detection technology to accurately extract action features in badminton, combined with convolutional neural network (CNN), recurrent neural network (RNN), long term memory network (LSTM) and other deep learning models to efficiently learn and model these features. Thus, the automatic classification and recognition of badminton movement can be realized. The experimental results show that the model has significant accuracy in badminton action recognition, good generalization ability and practicability, and can be effectively applied in the badminton teaching and training process of athlete performance evaluation, competition data analysis and other aspects. This research result not only expands the practical application of computer vision technology in the field of badminton, but also provides new ideas and tools for further promoting the development of sports intelligence and digitalization. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving image classification of one-dimensional convolutional neural networks using Hilbert space-filling curves

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APPLIED INTELLIGENCE 2023年第22期53卷 26655-26671页

作者： Verbruggen, Bert Ginis, Vincent Vrije Univ Brussel Data Analyt Lab Pl Laan 2 B-1050 Brussels Belgium Harvard Univ Sch Engn & Appl Sci 9 Oxford St Cambridge MA 02138 USA

Convolutional neural networks (CNNs) have significantly contributed to recent advances in machine learning and computer vision. Although initially designed for image classification, the application of CNNs has stretched far beyond the context of images alone. Some exciting applications, e.g., in natural language processing and image segmentation, implement one-dimensional CNNs, often after a pre-processing step that transforms higher-dimensional input into a suitable data format for the networks. However, local correlations within data can diminish or vanish when one converts higher-dimensional data into a one-dimensional string. The Hilbert space-filling curve can minimize this loss of locality. Here, we study this claim rigorously by comparing an analytical model that quantifies locality preservation with the performance of several neural networks trained with and without Hilbert mappings. We find that Hilbert mappings offer a consistent advantage over the traditional flatten transformation in test accuracy and training speed. The results also depend on the chosen kernel size, agreeing with our analytical model. Our findings quantify the importance of locality preservation when transforming data before training a one-dimensional CNN and show that the Hilbert space-filling curve is a preferential transformation to achieve this goal.

关键词： image classification image transformation machine learning Convolutional neural network Supervised learning Data preprocessing

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