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共 11503 条记录，以下是411-420 订阅

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A Comprehensive Analysis of image processing Methods for Agricultural Product Quality Control

A Comprehensive Analysis of Image Processing Methods for Agr...

引用

2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nandhini, E. Vadivu, G. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

In the post-harvest stages of agricultural products, labor shortages and poor-quality control lead to significant market losses. The automated industries for agricultural products that use machine learning are evolving, with a recent focus on deep learning (DL). Computer vision (CV) serves as an essential part in the automation method for fruits and vegetables, where image processing (IP) ensures proper product categorization, addressing the issue of product loss. This paper focuses on IP techniques in sequential procedures, which can improve efficiency and precision in agricultural product sorting and grading. Deep neural networks and algorithms are effective because they achieve high classification accuracy with a low loss percentage. © 2024 IEEE.

关键词： Industries Accuracy Reviews image processing Scalability Quality control Loss measurement IP networks Reliability Sorting

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Texture Feature Extraction of image Based on 2D Hilbert-Huang Transform and Multifractal Analysis 2

Texture Feature Extraction of Image Based on 2D Hilbert-Huan...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Yang, Lei Lu, Feng Zhang, Tiegang Chen, Jing Shanghai University School of Mechatronic Engineering and Automation Shanghai China Sichuan University College of Electrical Engineering Chengdu China

ISBN: (纸本)9798350331417

Analysis and classification of texture images are significant topics in the field of computer vision. The extraction of texture features from images is mainly applied in areas of object recognition, image segmentation, and image fusion and so on. As images are considered signals, signal processing techniques can commonly be used in image analysis and processing. Traditional signals analysis methods include Fourier transform, short-time Fourier transform, wavelet transform, and others. These methods can effectively analyze stationary signals but cannot effectively analyze image signals. For non-stationary signals, the Hilbert-Huang Transform (HHT) was proposed by Norden E. Huang et al. This method enables accurate analysis of non-linear and non-stationary signals. The HHT can analyze non-stationary and nonlinear signals while interpreting their instantaneous frequency characteristics. This paper extends the theory of HHT to the two-dimensional domain and applies it to image texture analysis. 2D empirical mode decomposition (EMD) algorithm and theory of HHT are introduced first. Then 2D EMD is used to analyze image texture. Thirdly, multifractal spectrum is adopted to describe the image texture, and we give a group experiment on simple classification of natural marble texture. Experimental results show that the proposed method combines the theory of multifractal method and HHT theory to extract features from images, which provide a new way for non-stationary signal fields such as texture image processing. © 2023 IEEE.

关键词： Multifractal Analysis Texture classification Texture image Two Dimensional Hilbert-Huang Transform

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Research on Detection of Moldy Corn Kernels Based on machine vision 7

Research on Detection of Moldy Corn Kernels Based on Machine...

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7th International conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023

作者： Chen, Yuanqi Zhang, Ning Liu, Ziyang Ye, Xinwei Xijing University Xi'an China

ISBN: (纸本)9798350382877

Corn plays an important role in many fields, but the level of intelligent detection for moldy corn is low. This article proposes a method for identifying moldy corn kernels based on machine vision. First, the image is processed, and then the MSR method with color restoration in the Retinex image enhancement algorithm is used to enhance the image. The threshold is set in the HSV space, and the moldy corn kernels are segmented through morphological processing. The two processing results are combined through logical AND operation to obtain the result. The final detection result shows that the probability of effectively recognizing moldy corn kernels can reach around 95%. This proves that the algorithm proposed in the article can efficiently and quickly detect moldy individuals in corn kernels, providing technical support and theoretical basis for the future development of intelligent detection of moldy individuals in corn. © 2023 IEEE.

关键词： image segmentation

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Wild Animal Detection System on a Farm Using image processing

Wild Animal Detection System on a Farm Using Image Processin...

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International conference on Advances and Applications of Artificial Intelligence and machine Learning, ICAAAIML 2023

作者： Barhate, Madhuri Gulhane, Nandini Gunjal, Tanvi Gudi, Swaroop Gulhane, Vedant Wagade, Prasad Gude, Ayush Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819795062

Human civilization is based on agriculture, and one of the biggest threats to agricultural productivity is the existence of wild animals on farmlands. Animals are taking over some farmlands, causing significant crop losses, property damage, and livestock risks. As a result, suitable solutions must be developed. A system to support farmers is necessary because the conventional method of constant guard monitoring is impractical. Modern technologies like machine learning, computer vision, and image processing are used by this wild animal detection system to effectively manage conflicts between humans and wildlife. machine learning and computer vision rely on the integration of particular algorithms to guarantee the accuracy and effectiveness of the system. With an emphasis on sustainability, the technology protects property and crops while also aiding in the conservation of wildlife. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Livestock

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Research on Visual Detection Methods and Development Trends of Surface Defects of Urban Tunnels

Research on Visual Detection Methods and Development Trends ...

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IEEE International conference on Prognostics and Health Management (ICPHM)

作者： Xu, Geng Gao, Mingxin Liu, Feng Liu, Yang JiNan Urban Construct Grp Co Ltd Jinan 250000 Peoples R China Harbin Inst Technol Sch Transportat Sci & Engn Harbin 150090 Peoples R China Shandong HuichengMingzhi Tech Dev Ltd Co Jinan 250000 Peoples R China

ISBN: (纸本)9798350346251

The detection of surface defects in urban tunnels is a key focus of safety operations and maintenance. Structural surface defect detection has gone through three key phases: manual visual inspection phase, manual instrumental inspection phase, and image visual perception phase, with most current studies focusing on the third phase. This paper analyses the current situation and problems of existing surface defects detection technologies at two levels: traditional image processing and intelligent machine vision perception. Correspondingly, future trends in sur-face defect detection techniques for urban tunnels are discussed, which provide solutions for the development of intelligent perception of the structural safety status of urban tunnels.

关键词： Defect detection image processing Computer vision Deep Learning Convolutional Neural Networks

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Adaptive analysis of rapid prediction of coal quality information based on image analysis

Adaptive analysis of rapid prediction of coal quality inform...

引用

2022 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2022

作者： Liu, Ming Zhang, Hua CHN Energy Xinjiang Chemical Co. Ltd. Xinjiang Urumqi831404 China

ISBN: (纸本)9781510663350

machine vision technology has shown great potential for development and application in the coal heat utilization and coal chemical production process It is important to carry out image analysis-based stability adaptation analysis of coal heat utilization and coal chemical equipment. To further expand its application, this work reviewed the characteristics of different vision sensor systems and image processing algorithms in the process of image acquisition and processing, conducted applicability analysis for rapid prediction of coal quality, presented the current problems and future development trends of image analysis technology in the coal heat utilization and coal chemical production process. It provided theoretical guidance and reference for promoting automation, intelligence, and mechanization in the coal industry. © 2023 SPIE.

关键词： Forecasting

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Accurate extraction of handwritten text line in complex document images

Accurate extraction of handwritten text line in complex docu...

引用

2024 IEEE International conference on Computer vision and machine Intelligence, CVMI 2024

作者： Wu, Ruiqing Zeng, Hailin Xie, Jun Hao, Hang Gu, Qingshui Chen, Wei University of Electronic Science and Techonlogy of China School of Information and Communcition Enginnering Chengdu China

ISBN: (纸本)9798350376876

As for handwritten text recognition, accurately detecting and recognizing text lines in complex document images remains challenging due to the lack of datasets with complex document images and high-quality annotations. In this paper, we introduce a novel approach that employs fully convolutional network for iterative refinement of coarse labels, resulting in enhanced and refined ground truth labels. These labels correct manual annotation errors and upgrade the quality of annotation. Meanwhile, we present an improve model Handwritten Text Extraction Network (HTENet) designed for the extraction of handwritten text lines in complex scenarios, which demonstrates substantial improvements in detection accuracy when trained with our refined labels. Our approach outperforms existing methods like Progressive Scale Expansion Network (PSENet) and DBNet, offering a promising direction for future research in complex document image processing. Main contributions include a label refinement method that minimizes manual annotation effort, the development of a more accurate text line detection model, and the creation of two complex document image datasets. © 2024 IEEE.

关键词： image annotation

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Breaking Through Color Casts: Enhancing image Fidelity with machine Learning-Based Correction

Breaking Through Color Casts: Enhancing Image Fidelity with ...

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2024 International conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Reddy, Choppa Jeevan Sai Akshay, Bavani Bhawane, Bange Reddy, Busireddy Parvathammagari Srikanth Satish, Addanki Sree Vidyanikethan Engineering College Tirupati India School of Engineering Mohan Babu University Tirupati Dept. of Ece India

ISBN: (纸本)9798350375237

L Color cast, an aberration common in digital images, poses challenges in various image processing applications, affecting image quality and visual perception. This research investigates diverse methodologies for color cast correction, ranging from traditional algorithms to modern machine learning-based approaches. Leveraging a comprehensive dataset of original and corrected images, the present study evaluates the efficacy of each method using quantitative metrics, including ACMO, BREN, GRAS, LAPM, LAPV, LAPD, and WAVV. Results indicate that while traditional techniques like Gray World Algorithm and White Patch Retinex Algorithm demonstrate moderate effectiveness, the implemented machine learning-based algorithm showcases superior performance across multiple color cast levels. By employing linear regression on RGB values, the method efficiently corrects color cast aberrations, yielding visually appealing and perceptually accurate results. Furthermore, the research highlights the significance of robust color constancy algorithms and their role in mitigating color cast distortions in digital images. This study contributes valuable insights into the field of color cast correction, offering practitioners in image processing and computer vision a comprehensive understanding of effective correction strategies. Future research directions may explore advanced machine learning models and integration with color constancy mechanisms to further enhance color cast correction techniques. © 2024 IEEE.

关键词： image enhancement

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Degraded Polygons Raise Fundamental Questions of Neural Network Perception 37

Degraded Polygons Raise Fundamental Questions of Neural Netw...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Tang, Leonard Ley, Dan Harvard Univ Dept Math Cambridge MA 02138 USA Harvard Univ Sch Engn & Appl Sci Cambridge MA 02134 USA

ISBN: (纸本)9781713899921

It is well-known that modern computer vision systems often exhibit behaviors misaligned with those of humans: from adversarial attacks to image corruptions, deep learning vision models suffer in a variety of settings that humans capably handle. In light of these phenomena, here we introduce another, orthogonal perspective studying the human-machine vision gap. We revisit the task of recovering images under degradation, first introduced over 30 years ago in the Recognition-by-Components theory of human vision. Specifically, we study the performance and behavior of neural networks on the seemingly simple task of classifying regular polygons at varying orders of degradation along their perimeters. To this end, we implement the Automated Shape Recoverability Test(1) for rapidly generating large-scale datasets of perimeter-degraded regular polygons, modernizing the historically manual creation of image recoverability experiments. We then investigate the capacity of neural networks to recognize and recover such degraded shapes when initialized with different priors. Ultimately, we find that neural networks' behavior on this simple task conflicts with human behavior, raising a fundamental question of the robustness and learning capabilities of modern computer vision models.

关键词： Computer vision

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Enhancing Endometrial Tumor Detection: Early Diagnosis with Advanced vision Transformer Architecture 3rd

Enhancing Endometrial Tumor Detection: Early Diagnosis with ...

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3rd International conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Tejavath, Abhinaya Swarnkar, Bhawna Khare, Nilay Maulana Azad Natl Inst Technol MANIT Ctr Artificial Intelligence Bhopal India Maulana Azad Natl Inst Technol MANIT Dept Comp Sci & Engn Bhopal India

ISBN: (纸本)9783031686160;9783031686177

Introduction: Early detection and treatment are key to improving the prognosis of endometrial cancer. However, conventional machine learning approaches have limited capacity to simulate the complex links between histopathological images and their interpretations, making it challenging to achieve accurate results. A vision transformer-based image classification model has been proposed to assist medical professionals in detecting endometrial cancer and improving patient outcomes. Objective: This study aims to develop and evaluate a vision transformer-based model for accurately detecting histopathology images of endometrium, and compare its performance against existing fine-tuning methods such as MobilenetV2, Xception, and VGG16. Methods: A publicly accessible histopathology imaging dataset of endometrium was used to train and validate the proposed model. The performance of the model was evaluated against state-of-the-art approaches in the field. Results: The validation results showed that the proposed model attained an accuracy of 99.36%, surpassing the performance of existing fine-tuning methods and achieving the state-of-the-art performance in the widely used endometrial cancer benchmark dataset. These findings highlight the potential of vision transformer-based models in accurately detecting histopathology images of endometrium, which could lead to better patient outcomes. Conclusions: The proposed vision transformer-based model provides a highly accurate and efficient approach to detecting endometrial cancer. This study underscores the potential of this model as a valuable tool for medical professionals in the early detection and treatment of endometrial cancer, ultimately improving patient outcomes.

关键词： Endometrial Cancer Hematoxylin and Eosin (H&E) image vision Transformer Deep Learning Medical image Classification

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