检索结果-内蒙古大学图书馆

2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Bhamare, Archana Upadhyay, Vivek Bansal, Payal Poornima University Dept of Electronics & Communication Engineering Jaipur India

ISBN: (纸本)9798350384659

This research work aims to develop an AI-based plant growth monitoring system using computer vision. By leveraging computer vision algorithms and artificial intelligence techniques, the system will enable real-time and automated assessment of plant growth and health. Traditional methods for plant growth monitoring are time-consuming and labor-intensive. The proposed system will address these limitations by analyzing images of plants using image processing algorithms and extracting relevant features such as leaf area, plant height and width. machine learning algorithms will be employed to train models capable of recognizing patterns and predicting plant growth based on the extracted features. The system will be designed to accommodate different plant types and environmental conditions. By providing accurate and timely insights into plant health and growth, this AI-based plant growth monitoring system will benefit farmers, researchers, and plant enthusiasts, enabling informed decision-making for plant care and optimizing growth conditions. This research work aims to revolutionize plant growth monitoring by offering a cost-effective, scalable, and efficient solution with wide applications in agriculture, horticulture and environmental research. © 2023 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2022年第12期44卷 8874-8895页

作者： Wang, Lin Yoon, Kuk-Jin Korea Adv Inst Sci & Technol Dept Mech Engn Visual Intelligence Lab Daejeon 34141 South Korea

High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions.

关键词： Imaging image reconstruction Loss measurement Cameras Deep learning Visualization Dynamic range High-dynamic-range (HDR) imaging deep learning (DL) convolutional neural networks (CNNs)

来源：评论

学校读者我要写书评

暂无评论

Drone-Based applications for Tailings Dam Monitoring

Drone-Based Applications for Tailings Dam Monitoring

引用

APCOM 2023 Conference: Intelligent Mining: Innovation, vision, and Value

作者： Gomez, Jose A. Sattarvand, Javad Department of Mining & Metallurgical Engineering Mackay School of Earth Sciences & Engineering University of Nevada RenoNV United States

ISBN: (纸本)9780873355216

Failures of tailings dams have been happening lately. Due to the lack of laws on particular design criteria and stability requirements related monitoring during construction and maintenance, they are thought to be more fragile than hydraulic dams. Monitoring the dam is therefore necessary to understand its current condition and guarantee its safety. The physical condition of the dam could be evaluated with the early identification of seepage. Additionally, due to their adaptability and capacity for high-resolution data collecting, UAVs are an excellent choice for efficiently covering the tailings dam site. UAVs may capture high-quality photos when equipped with a high-resolution RGB camera, thermal sensors, or multispectral sensors. When these sensors are paired with image processing and machine learning algorithms, the result is a reliable estimate of the dam condition. © 2023 Society for Mining, Metallurgy & Exploration Inc. All rights reserved.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Normalized Determinant Pooling Layer in CNNs for Multi-Label Classification 7

Normalized Determinant Pooling Layer in CNNs for Multi-Label...

引用

Computational Imaging VII 2023

作者： Giuliano, Alessandro Hilal, Waleed Alsadi, Naseem Yawney, John Gadsden, S. Andrew Lab HamiltonONL8S 4L8 Canada Adastra Corporation TorontoONM5J 2J2 Canada

ISBN: (数字)9781510661615

ISBN: (纸本)9781510661608

Convolutional neural networks (CNNs) are a widely researched neural network architecture that has demonstrated exemplary performance in image processing tasks and applications compared to other popular deep learning and machine learning methods resulting in state-of-the-art performance in many image processing tasks such as image classification and segmentation. CNNs operate on the principle of automated learning of filters or kernels in contrast with hand-crafted digital filters to extrapolate features from images effectively. This paper aims to investigate whether a matrix's determinant can be used to preserve information in CNN convolutional layers. Geometrically the absolute value of the determinant is defined as a scaling factor of the linear transformation resulting from matrix multiplication. When an image's size is reduced into a feature space through a convolutional layer of a CNN, some information is lost. The intuition is that the scaling factor that results from the determinant of the pooling layer matrix can enhance the feature space introducing scaling as a piece of information in the feature space as well as lost relations between adjacent pixels. © 2023 SPIE. All rights reserved.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Swarm optimisation-based bag of visual words model for content-based X-ray scan retrieval

引用

INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2022年第2期40卷 168-183页

作者： Karthik, K. Kamath, S. Sowmya Natl Inst Technol Dept Informat Technol Healthcare Analyt & Language Engn HALE Lab Mangaluru Karnataka India

Classification and retrieval of medical images (MedIR) are emerging applications of computer vision for enabling intelligent medical diagnostics. Medical images are multi-dimensional and require specialised processing for the extraction of features from their manifold underlying content. Existing models often fail to consider the inherent characteristics of data and have thus often fallen short when applied to medical images. In this paper, we present a MedIR approach based on the bag of visual words (BoVW) model for content-based medical image retrieval. When it comes to any medical approach models, an imbalance in the dataset is one of the issues. Hence the perspective is also considering a balanced set of categories from an imbalanced dataset. The proposed work on BoVW model extracts features from each image are used to train supervised machine learning classifier for X-ray medical image classification and retrieval. During the experimental validation, the proposed model performed well with the classification accuracy of 89.73% and a good retrieval result using our filter-based approach.

关键词： content-based medical image retrieval image classification visual space modelling

来源：评论

学校读者我要写书评

暂无评论

Contextual Evaluation of Segmentation Models using Spatial Reasoning 26

Contextual Evaluation of Segmentation Models using Spatial R...

引用

26th Irish machine vision and image processing Conference, IMVIP 2024

作者： Porter, Victoria Styles, Iain Curtis, Tim M. Taggart, Michael J. Albargothy, Mona J. Gault, Richard Centre for Intelligent Sustainable Computing School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Wellcome-Wolfson Institute for Experimental Medicine Queen's University Belfast United Kingdom Biosciences Institute Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781837242672

image segmentation models are often evaluated using measures of overlap and boundary deviation between a ground truth and a prediction. These measures do not indicate whether a prediction is an overestimation or underestimation of the ground truth. This contextual information is critical in medical imaging applications such as tumor detection where a model's tendency to overestimate a prediction would be preferred to avoid overlooking malignant cells. Spatial reasoning provides context on a model's segmentation performance in terms of its tendency to over- or underestimate a region of interest. Such context can highlight a model's decision-making trends and can be applied to inform targeted improvements. In this work, we provide a Python module1,2 that implements a model-agnostic spatial reasoning pipeline for the contextual evaluation of segmentation methods. We apply this pipeline to the output of the Segment Anything model on 3 electron microscopy (EM) datasets and demonstrate the meaningful inferences that can be made. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Surface quality and orientation estimation using speckle imaging of ground specimens

引用

Procedia Computer Science 2025年 253卷 2096-2105页

作者： Deep Singh N. Arunachalam Department of Mechanical Engineering Indian Institute of Technology Madras Chennai Tamil Nadu India PIN - 600036

In the current era, machine vision systems are being implemented widely in varied fields due to its key features, such as rapid processing, non-contact-based technology and in-situ measurements. This technology also possesses wide applications in the manufacturing sector. The surface texture properties of any machined component vary based on the manufacturing process, machining parameters, tool and machine conditions etc. As the surface texture of the machined components greatly influences the functional performance, it is vital to examine the surface characteristics. The surface texture of the machine component can be assessed by implementing a series of image processing techniques on its speckle images. Speckle image refers to the randomly distributed granular pattern which is obtained when a rough or textured surface is illuminated using a laser beam. This paper focuses on estimating the orientation of the workpiece and examining the surface characteristics based on the post-processing of the speckle images. The hardened steel workpieces used in this investigation were ground by varying the process parameters and speckle images were obtained at 0°, 30°, 60° and 90° orientations. The shifted power spectral density of the ground sample images contains high-energy coefficients which mimic a line and its orientation varies based on the sample orientation. The Hough transform technique was applied to the binary image of shifted PSD to efficiently determine the orientation. Furthermore, correlations have been established between several surface texture characteristics and GLCM parameters with the surface roughness of ground samples.

关键词： Speckle image Grinding image processing Power spectral density (PSD) Hough transform Gray level co-occurrence matrix

来源：评论

学校读者我要写书评

暂无评论

A concise review on food quality assessment using digital image processing

引用

TRENDS IN FOOD SCIENCE & TECHNOLOGY 2021年第PartA期118卷 106-124页

作者： Meenu, Maninder Kurade, Chinmay Neelapu, Bala Chakravarthy Kalra, Sahil Ramaswamy, Hosahalli S. Yu, Yong Zhejiang Univ Coll Biosyst Engn & Food Sci 866 Yuhangtang Rd Hangzhou 310058 Peoples R China Indian Inst Technol Dept Mech Engn Jammu 181221 J&K India Natl Inst Technol Biotechnol & Med Engn Rourkela 769008 Odisha India McGill Univ Dept Food Sci 21111 Lakeshore Rd Ste Anne De Bellevue PQ H9X 3V9 Canada Minist Agr Key Lab Equipment & Informatizat Environm Control 866 Yuhangtang Rd Hangzhou 310058 Peoples R China

Background: Recent advances in signal processing technology and computational power have increased the attention towards computer vision-based techniques in diverse applications such as agriculture, food processing, biomedical, and military. Especially in agricultural and food processing, computer vision can replace most of the manual methods for screening of seed, grain and food quality. Scope and approach: The objective of present study is to review the recent advancements in computer vision techniques for predicting quality of various raw materials and food products. This review paper is focused on the quality determination of grains, vegetables, fruits, beverages, meat, sea food and edible oils using Digital image processing (DIP). Several studies have reported the successful applications of DIP techniques for feature extraction, classification and quality prediction of foods. DIP algorithms are used to extract the significant features from images which are further used as input for machine learning (ML) algorithms to classify them based on different criteria. These feature extraction methods have been improved by Deep Learning (DL) algorithms. Features can be automatically extracted by DL algorithms resulting in higher accuracy. DL algorithms require huge data management and computational resources which can be a major limitation. Key findings and conclusion: A significant literature is available for quality estimation of food products by using computer vision algorithms, but they lack commercial exploitation. Android based applications have not yet been developed for this specific purpose. User friendly, low cost and portable devices equipped for quality estimation would be helpful for rapid quality measurement of food products in real time.

关键词： Food quality Classification Prediction Deep learning Artificial intelligence machine learning Computer vision Linear regression DIP

来源：评论

学校读者我要写书评

暂无评论

Realtime Object Distance Measurement Using Stereo vision image processing 2nd

Realtime Object Distance Measurement Using Stereo Vision Ima...

引用

2nd International Conference on Big Data, machine Learning, and applications, BigDML 2021

作者： Arunakumari, B.N. Shashidhar, R. Naziya Farheen, H.S. Roopa, M. Department of Computer Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru560064 India Department of Electronics and Communication Engineering JSS Science and Technology University Karnataka Mysuru570006 India Department of Electronics and Communication Engineering Navkis College of Engineering Hassan Karnataka Hassan573217 India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru560078 India

ISBN: (纸本)9789819934805

In recent years, great progress has been made on 2D and 3D image understanding tasks, such as object detection and instance segmentation. The recent trends in technology driverless cars are making a difference in daily life. The basic principle in these driverless cars is object detection and localization using multiple video cameras and LIDAR and it is one of the current trends in research and development, so attempts to achieve the same on small scale using the available resources. In the proposed method, firstly the stereo images are captured in a dual-lens camera, and secondly, converting the RGB image into a grayscale image. The third step is to apply a global threshold to separate the background, to get the same size of the image using morphological operation. Blob detection is used to detect the points and regions in the image. The fourth step is to detect the object distance and size measurement using the pinhole camera formula. Further, in the proposed work, an effort is made to determine the linear space between the camera and the object from the pictures taken from the camera. Typically, stereo images are used for computation. Binocular stereopsis, or stereo vision, is the capability to derive information about how far left the objects are, grounded uniquely on the comparative places of the object in the two eyes. It depends on both sensory and motor capabilities, using the similar principle the human brain employs, taking two images of the same object taken from two different linearly separated distances. The frame rate of the system can go a maximum of up to 15 frames per second. 15 frames per second can be considered as acceptable for most autonomous systems, and it will work in realtime. Effective convolutional matching technique between embeddings are used for localization that leads LIDAR to increase centimeter level accuracy by about 97%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pinhole cameras

来源：评论

学校读者我要写书评

暂无评论

A machine vision Measurement Method for Large Plates Based on Reference Point Assistance 5

A Machine Vision Measurement Method for Large Plates Based o...

引用

5th International Conference on Electronics and Electrical Engineering Technology (EEET)

作者： Ji, Tianyi Zhao, Zhiwei Zhao, Ning Southeast Univ Coll Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798350320428

In this work, a coordinate positioning method based on machine vision with reference point assistance is proposed to apply to the size measurement for large rectangular plate parts. This method first obtains the coordinate information of the reference point through circular detection, and uses the line detection and mean-shift clustering algorithm to fit the plate vertex information. Then the pixel coordinate relationship between the reference point and the plate vertex is used to solve the world coordinate of the plate vertex. Finally, the world coordinate of the plate vertex is used to calculate the length and width dimensions of the plate. Instead of other methods which unify coordinate system by moving cameras, this method uses reference point assistance system on measuring plate to unify the coordinate system, simplifying the difficulty of the unification of coordinate system and ensuring accuracy. Effectiveness of such method is verified by measuring rectangular plates with scales from centimeters to meters. The results showed that the measurement error could be controlled within 5 millimeters. In practical applications, the measurement accuracy has been much higher than the processing accuracy during the plate manufacturing, meeting the needs of size measurement.

关键词： reference points size measurement machine vision image processing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：