检索结果-内蒙古大学图书馆

A Systematic Survey on Biological Cell image Segmentation and Cell Counting Techniques in Microscopic images Using machine Learning

引用

WIRELESS PERSONAL COMMUNICATIONS 2024年第2期137卷 813-851页

作者： Singh, Harjeet Kaur, Harpreet PunjabiUniv Comp Sci & Engn Dept Patiala 147001 Punjab India

The article focuses on the concepts of Cell image Segmentation (CIS) and the gradual introduction of cell counting. Motivated by the rapid development of machine learning (ML) methods, which is carried out in this investigation. ML is evolving from theory to practical applications, with deep neural network models extensively used in academia and business for various applications, including image counting and natural language processing. These advancements can greatly influence medical imaging technologies, data processing, diagnostics, and healthcare in general. Main objectives of the research are to provide an overview of biological cell counting methods in microscopic images and to explore deep learning (DL)-based image segmentation approaches. The study expertly describes current trends, cutting-edge learning technologies, and platforms utilized for DL approaches. Cell counting is one of the most researched and challenging subjects in computer vision systems. Academics are increasingly interested in this area due to its real-time applications in biology, biochemistry, medical diagnostics, computer vision-based cell tracking systems for large populations, and stem cell manufacturing. Counting cells in the biological field is beneficial. For instance, the ratio of white blood cells to cancer cells in the blood can help determine the origin of a disease. Biologists also need to count cells within cell cultures to monitor the time-dependent growth of cells during bacterial experiments. Numerous methods for cell counting have been developed, after addressing the challenges with Cell Counting algorithms;the article explores promising future directions in CIS and cell counting research fields.

关键词： Cell image Segmentation White Blood Cell Biomedical Biology Automatic Cell Counting image Analysis

来源：评论

学校读者我要写书评

暂无评论

A novel opto-tactile sensing approach to enhance the handling of soft fruit

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 235卷

作者： Ameur, Mohamed Adlan Ait El-Sayed, Amr M. Yan, Xiu T. Mehnen, Jorn Maier, Anja M. Univ Strathclyde Design Mfg & Engn Management Glasgow Scotland Assiut Univ Fac Engn Mechatron Engn Dept Assiut 71516 Egypt Glasgow Caledonian Univ Dept Mech Engn Cowcaddens Rd Glasgow G4 0BA Scotland

In agricultural settings, handling of soft fruit is critical to ensuring quality and safety. This study introduces a novel opto-tactile sensing approach designed to enhance the handling and assessment of soft fruit, with a case example of strawberries. Our approach utilises a Robotiq 2F-85 gripper equipped with the DIGIT vision-Based Tactile Sensor (vBTS) and attached to a Universal Robot UR10e. In contrast to force-based approaches, we introduce a novel purely image-based processing software pipeline for quantifying localised surface deformations in soft fruit. The system integrates fast and explainable image processing techniques applying image differencing, denoising, K-means clustering for unsupervised classification, morphological operations, and connected components analysis (CCA) to quantify surface deformations accurately. A calibration of the image processing pipeline using a rubber ball showed that the system effectively captured and analysed the rubber ball's surface deformations, benefiting from its uniform elasticity and predictable response to compression. As a soft fruit case example, the image processing pipeline was subsequently applied to strawberries, blueberries, and raspberries, demonstrating that the calibration parameters derived from the rubber ball could effectively assess surface deformations in soft fruits. Despite the complex, nonlinear deformation characteristics inherent to strawberries, blueberries, and raspberries, the pipeline exhibited robust performance, capturing and quantifying subtle surface changes. These findings underscore the system's capacity for precise deformation analysis in delicate materials, offering major potential for further refinement and adaptation. This novel approach of proposing and testing an image processing pipeline lays the groundwork for enhancing the handling and assessment of materials with intricate mechanical properties, paving the way for broader applications in sensitive agricultural and industrial

关键词： Robotic gripper vision-Based Tactile Sensor (vBTS) image processing machine Learning Soft Fruit Handling Robotic manipulation

来源：评论

学校读者我要写书评

暂无评论

Integrated image processing and machine learning framework for precise quantification and prediction of soil erosion

引用

vISUAL COMPUTER 2025年 1-13页

作者： Kumar, Shubham Chauhan, Charu Chauhan, Tanvi Gupta, vivek Uday, Kala venkata Indian Inst Technol Mandi Sch Civil & Environm Engn Mandi 175005 Himachal Prades India Indian Inst Technol Mandi Ctr Climate Change & Disaster Management C3DAR Mandi 175005 Himachal Prades India

Soil erosion, primarily driven by water and wind, poses a significant environmental challenge globally, leading to land degradation and geo-hazards. Despite various empirical methods, image analysis, and machine learning techniques employed to address this issue, effective mitigation tools remain lacking. This study presents an innovative framework integrating image processing (IP) and machine learning (ML) to enhance the understanding, quantification, and prediction of soil erosion processes. Laboratory flume experiments were conducted to capture erosion images, which were pre-processed using techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve image quality. Supervised ML models, including Logistic Regression (LR), K-Nearest Neighbor (KNN), Support vector machine (SvM), Decision Tree (DT), and Random Forest (RF), were applied to classify eroded and non-eroded soil areas. The model's performance was rigorously evaluated using metrics such as precision, recall, and F1-score. The results demonstrated that KNN and RF outperformed other models in predicting soil erosion, with KNN exhibiting the least variation (2.39%) compared to the reference erosion profile. This study underscores the potential of an IP and ML ensemble framework for precise soil erosion quantification and prediction, offering practical applications for erosion mitigation. The open-source code and dataset are available at https://***/mlgeotech/***.

关键词： image processing Erosion quantification machine learning Feature selection Computer vision

来源：评论

学校读者我要写书评

暂无评论

A Novel Pretrained General-purpose vision Language Model for the vietnamese Language

引用

ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION processing 2024年第5期23卷 1-16页

作者： Dinh Anh vu Quang Nhat Minh Pham Giang Son Tran Univ Sci & Technol Hanoi Hanoi Vietnam Aimesoft JSC R&D Hanoi Vietnam Univ Sci & Technol Hanoi ICTLab Vietnam Acad Sci & Technol 18 Hoang Quoc Viet Hanoi Vietnam

Lying in the cross-section of computer vision and natural language processing, vision language models are capable of processing images and text at once. These models are helpful in various tasks: text generation from image and vice versa, image-text retrieval, or visual navigation. Besides building a model trained on a dataset for a task, people also study general-purpose models to utilize many datasets for multitasks. Their two primary applications are image captioning and visual question answering. For English, large datasets and foundation models are already abundant. However, for vietnamese, they are still limited. To expand the language range, this work proposes a pretrained general-purpose image-text model named visualRoBERTa. A dataset of 600k images with captions (translated MS COCO 2017 from English to vietnamese) is introduced to pretrain visualRoBERTa. The model's architecture is built using Convolutional Neural Network and Transformer blocks. Fine-tuning visualRoBERTa shows promising results on the vivQA dataset with 34.49% accuracy, 0.4173 BLEU 4, and 0.4390 RougeL (in visual question answering task), and best outcomes on the sviIC dataset with 0.6685 BLEU 4, 0.6320 RougeL (in image captioning task).

关键词： Computer vision natural language processing visual linguistic image text pretrain vietnamese foundation multi-modal machine learning

来源：评论

学校读者我要写书评

暂无评论

Gaussian mixture model clustering allows accurate semantic image segmentation of wheat kernels from near-infrared hyperspectral images

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2025年 259卷

作者： Kartakoullis, Andreas Caporaso, Nicola Whitworth, Martin B. Fisk, Ian D. Univ Nottingham Sch Biosci Div Div Food Nutr & Dietet Sutton Bonington Campus Nottingham LE12 5RD England Campden BRI Chipping Campden GL55 6LD Glos England Buhler UK Ltd London E16 2LD England

In this study, an ad-hoc image processing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian Mixture Model (GMM), characterized as a soft clustering method, has been employed for this task, yielding noteworthy results in both kernel and germ segmentation. A comparative analysis was conducted, wherein GMM was compared with two hard clustering methods, hierarchical clustering and k-means, as well as other common clustering algorithms prevalent in food HSI applications. Notably, GMM exhibited the highest accuracy, with a Jaccard index of 0.745, surpassing hierarchical clustering at 0.698 and k-means at 0.652. Furthermore, the spectral variations observed in wheat kernel topology can be used for semantic image segmentation, especially in the context of selecting the germ portion within the wheat kernels. These findings carry practical significance for professionals in the fields of hyperspectral imaging (HSI) and machine vision, particularly for food product quality assessment and real-time inspection.

关键词： NIR hyperspectral imaging image segmentation Real time image processing Grain computer vision

来源：评论

学校读者我要写书评

暂无评论

A fast specular removal method for a single real image☆

引用

DISPLAYS 2025年 87卷

作者： Hao, Chuanpeng He, Yan Li, Yufeng Niu, Xiaobo Wang, Yan Chongqing Univ State Key Lab Mech Transmiss Adv Equipment Chongqing 400030 Peoples R China Univ Brighton Sch Comp Engn & Math Brighton BN2 4GJ England

The specular reflection of objects is an important factor affecting image display quality, which poses challenges to tasks such as pattern recognition and machine vision detection. At present, specular removal for a single real image is a crucial pre-processing step to improve the performance of computer vision algorithms. Despite notable approaches tailored for handling synthesized and pre-simplified images with dark backgrounds, real-time separation of specular reflection for a single real image remains a challenging problem. This paper proposes a novel specular removal method to separate the specular reflection for a single real image accurately and efficiently based on the dark channel prior. Initially, a modified-specular-free (MSF) image is developed using the dark channel prior, which can derive a direct estimation of specular reflection. Next, the image chromaticity spaces are established to represent the pixel intensity. Then, the maximum chromaticity value of the modified MSF image is extracted to guide the filtering of the specular reflection, treating the specular pixels as noise in the chromaticity space. Finally, the image without specular reflection can be obtained using the restored maximum chromaticity value based on the dichromatic reflection model. The superiority of this method is to achieve highquality specular reflection separation quickly without destroying the geometric features of the real image. Compared with the state-of-the-art methods, experimental results show that the proposed algorithm can achieve the best subjective visual effect and satisfactory quantitative performance. In addition, this approach can be implemented efficiently to meet real-time requirements, promising to be applied to computer vision measurement and inspection applications.

关键词： Specular removal Highlight Dark channel machine vision image restoration

来源：评论

学校读者我要写书评

暂无评论

Mold breakout prediction based on computer vision and machine learning

引用

Journal of Iron and Steel Research International 2024年第8期31卷 1947-1959页

作者： Yan-yu Wang Qi-can Wang Yong-chang Zhang Yong-hui Cheng Man Yao Xu-dong Wang School of Materials Science and Engineering Dalian University of TechnologyDalian 116024LiaoningChina Key Laboratory of Solidification Control and Digital Preparation Technology Dalian University of TechnologyDalian 116024LiaoningChina

Breakout is the most serious production accident in continuous casting and must be detected and predicted by stable and reliable *** sticking region,which forms on the local copper plate and expanded into a"v"shape,is the typical precursor of ***,computer vision technology was exploited to visualize the temperature change rate of the copper plate based on the temperature signals from thermocouples;then,the static and dynamic features of the abnormal sticking region were ***,logistic regression and Adaboost models were used to study and identify these features,resulting in the development of a mold breakout prediction model based on computer vision and machine *** test results demonstrate that the proposed model can effectively distinguish anomalous temperature patterns and considerably reduce false alarms without any missing *** a result,the proposed method could offer valuable insights into the realm of abnormality detection and prediction during continuous casting process.

关键词： Abnormality detection image processing Adaboost Logistic regression Continuous casting

来源：评论

学校读者我要写书评

暂无评论

Enhanced Classification System for Real-Time Embedded vision applications

引用

IEEE ACCESS 2024年 12卷 162311-162326页

作者： Khelifi, Ramzi Nini, Brahim Berkane, Mohamed Univ Oum El Bouaghi Res Lab Comp Sci Complex Syst ReLa CS 2 Oum El Bouaghi 04000 Algeria Univ Oum El Bouaghi Artificial Intelligence & Autonomous Things Lab Oum El Bouaghi 04000 Algeria

Embedded computer vision systems are increasingly being adopted across various domains, playing a pivotal role in enabling advanced technologies such as autonomous vehicles and industrial automation. Their cost-effectiveness, compact size, and portability make them particularly well-suited for diverse implementations and operations. In real-time scenarios, these systems must process visual data with minimal latency, which is crucial for immediate decision-making. However, these solutions continue to face significant challenges related to computational efficiency, memory usage, and accuracy. This research addresses these challenges by enhancing classification methodologies, specifically in Gray Level Co-occurrence Matrix (GLCM) feature extraction and Support vector machine (SvM) classifiers. To maintain a high level of accuracy while preserving performance, a smaller feature set is selected following a comprehensive complexity analysis and is further refined through Correlation-based Feature Selection (CFS). The proposed method achieves an overall classification accuracy of 84.76% with a feature set reduced by 79.2%, resulting in a 72.45% decrease in processing time, a 50% reduction in storage requirements, and up to a 77.8% decrease in memory demand during prediction. These improvements demonstrate the effectiveness of the proposed approach in improving the adaptability and capabilities of embedded vision systems (EvS), optimizing their performance under the constraints of real-time limited-resource environments.

关键词： Accuracy Support vector machines Real-time systems Feature extraction Memory management Computer vision Surveillance Bandwidth Wildlife machine learning image processing Embedded computer vision limited resource systems machine learning pattern classification real-time image processing

来源：评论

学校读者我要写书评

暂无评论

Computer vision on X-Ray Data in Industrial Production and Security applications: A Comprehensive Survey

引用

IEEE ACCESS 2023年 11卷 2445-2477页

作者： Rafiei, Mehdi Raitoharju, Jenni Iosifidis, Alexandros Aarhus Univ DIGIT Dept Elect & Comp Engn Aarhus Denmark Univ Jyvaskyla Fac Informat Technol Jyvaskyla 40100 Finland

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

关键词： X-ray imaging Security Computer vision Imaging Industrial engineering Three-dimensional displays Deep learning deep learning X-ray industrial applications security applications

来源：评论

学校读者我要写书评

暂无评论

vision-based monitoring of railway superstructure: A review

引用

CONSTRUCTION AND BUILDING MATERIALS 2024年 442卷

作者： Aela, Peyman Cai, Jiafu Jing, Guoqing Chi, Hung-Lin Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Hong Kong Peoples R China Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China

The computer vision-based analysis of railway superstructure has gained significant attention in railway engineering. This approach utilises advanced image processing and machine learning techniques to extract valuable information from visual data captured in the railway track environment. By analysing images from various sources such as cameras, drones, or sensors, computer vision algorithms can accurately detect and classify different components of the ballast superstructure, including the catenary system support, rail surface and profile, fastening system, sleeper, and ballast layer. This enables the automated assessment of the railway track's condition, stability, and maintenance needs. This paper comprehensively reviews the recent advancements, challenges, and potential applications of computer vision techniques in analysing railway superstructure. It discusses various vision-based methodologies and machine-learning approaches utilised in this context. Furthermore, it examines the benefits and limitations of computer vision-based analysis and presents future research directions for improving its applicability in railway track engineering.

关键词： Railway superstructure Track inspection Computer vision machine learning Robotics

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：