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Special Section Guest Editorial: machine vision-Systems, Methods, and applications

JOURNAL OF ELECTRONIC IMAGING 2022年第5期31卷

作者： Debayle, Johan Osten, Wolfgang Nikolaev, Dmitry MINES St Etienne St Etienne France Univ Stuttgart Stuttgart Germany Inst Informat Transmiss Problems Moscow Russia

Special section editors Johan Debayle, Wolfgang Osten, and Dmitry Nikolaev introduce the Special Section on machine vision: Systems, Methods, and applications.

关键词： machine vision image processing RGB color model image analysis image segmentation Digital image processing Inspection Tumors Optical metrology Physics

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Plant trait estimation and classification studies in plant phenotyping using machine vision - A review

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Information processing in Agriculture 2023年第1期10卷 114-135页

作者： Shrikrishna Kolhar Jayant Jagtap Symbiosis International(Deemed University)(SIU) LavalePuneIndia VPKBIET BaramatiMaharashtraIndia Symbiosis Institute of Technology(SIT) Symbiosis International(Deemed University)(SIU)LavalePuneMaharashtraIndia

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision *** vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the *** phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red,green and blue(RGB)imaging,thermal imaging,chlorophyll fluorescence imaging(CFIM),hyperspectral imaging,3-dimensional(3-D)imaging or high resolution volumetric *** paper provides an overview of imaging techniques and their applications in the field of plant *** paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and *** this paper,information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping *** paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural(2-D and 3-D),physiological and temporal trait estimation,and classification studies in plants.

关键词： Plant phenotyping machine vision Plant trait estimation Imaging techniques Leaf segmentation and counting Plant classification studies

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Natural Language processing Pretraining Language Model for Computer Intelligent Recognition Technology

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION processing 2024年第8期23卷 1-12页

作者： Dong, Jun Ludong Univ Coll Liberal Arts Yantai 264025 Shandong Peoples R China Shandong Prov Key Lab Language Resources Dev & App Yantai 264025 Shandong Peoples R China Chinese Lexicograpyh Res Ctr Yantai 264025 Shandong Peoples R China Chinese Language Promot Base Yantai 264025 Shandong Peoples R China

Computer intelligent recognition technology refers to the use of computer vision, Natural Language processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Tra

关键词： Natural Language processing Computer Intelligent Recognition Technology Pre-trained Language Model Computer vision machine Learning

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Enhancing manufacturing process accuracy: A multidisciplinary approach integrating computer vision, machine learning, and control systems

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JOURNAL OF MANUFACTURING PROCESSES 2025年 142卷 453-467页

作者： Ramesh, Kaki Deshmukh, Sandip Ray, Tathagata Parimi, Chandu Birla Inst Sci & Technol Pilani Dept Mech Engn Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Sci & Technol Pilani Dept Comp Sci & Informat Syst Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Sci & Technol Pilani Dept Civil Engn Hyderabad Campus Hyderabad 500078 Telangana India

Manufacturing industries face significant challenges in producing high-quality, faultless products within limited timeframes. Conventional human-based inspection methods are still prone to errors and cannot guarantee precise component placement, potentially leading to product failures, user hazards, and substantial financial and reputational losses. This research presents a workflow to automate an inspection system that integrates computer vision, machine learning, image processing, and control systems to address these challenges. The proposed system employs a microcontroller and stepper motors to control a highly calibrated camera, enabling precise and efficient product inspection. At its core, the system utilizes the YOLOv5 model for object detection, specifically identifying hole marks and holes on products pre-assembly. This deep learning model was chosen for its real-time detection capabilities and high accuracy, achieving a mean Average Precision (mAP) of 0.95, which surpasses many current industry standards. Following object detection, advanced image processing techniques are applied to determine the precise position of detected features. Our approach achieves a notable error rate of 0.2 %, offering improvements over traditional inspection methods. Our system offers the potential to reduce inspection processing time and improve fault identification accuracy in real-time applications. Our research contributes to the field of industrial automation by introducing a seamless integration of state-of-the-art computer vision techniques with practical control systems. The system's modular design allows for easy adaptation to various manufacturing environments, benefiting industries with complex assembly processes, such as electronics, automotive manufacturing, etc. While the current implementation focuses on hole detection, future work will explore expanding the system's capabilities to identify a broader range of defects and adapt to different product types. This re

关键词： Deep learning image processing Assembly Manufacturing industries Quality assurance

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An edge extraction approach for hot forging images based on discrete grayscale surface properties

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第3-4期137卷 1869-1890页

作者： Pan, Xiaoyu Wang, Zhi Wang, Delun Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

machine vision measurement is desirable to permit real-time non-contact measuring and positioning for hot forgings, among which edge extraction is a most essential issue to extract the contour and effective area. However, conventional edge detection methods are prone to get unsatisfactory edging extraction results, thus have poor effectiveness, and are not suitable for hot forging images. In this paper, an efficient and robust edge extraction approach for passive vision images of hot forgings is proposed. Grayscale images of hot forgings converted into discrete gray surface, the approach is based on the geometric properties and the continuity of the equivalent discrete grayscale surface. The presented algorithm detects three types of edges by various continuity criterions, which are corresponded to the geometric properties and vary with the primary and secondary edges. The geometric properties dependent nature of the algorithm ensures the primary and the secondary edges of the forges are identified in the different environmental conditions and for forging parts with various heat radiation intensities. Moreover, an edge thinning and connection approach is presented by defining the edging direction, which can be used to improve the qualities of types of edges. Finally, experimentations for images of various sorts of hot forgings are carried out to extract three types of edges;the relevant experimental results and validation indicators show that the proposed method takes better performance as 17.4453 in PSNR and 0.1146 in entropy for G0 edge for a typical forging image while 0.0342 for G2 edge compared with existing methods. The results demonstrate that the proposed approach is validated to have satisfactory performance, as well as efficacy and robustness.

关键词： Computer vision Forging Grayscale surface properties image edge detection machine vision Multimedia applications Stereo image processing

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Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

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IEEE ACCESS 2023年 11卷 171-202页

作者： Elbasi, Ersin Mostafa, Nour AlArnaout, Zakwan Zreikat, Aymen I. Cina, Elda Varghese, Greeshma Shdefat, Ahmed Topcu, Ahmet E. Abdelbaki, Wiem Mathew, Shinu Zaki, Chamseddine Amer Univ Middle East Coll Engn & Technol Kuwait 54200 Kuwait

Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

关键词： Artificial intelligence applications agriculture smart farming Internet of Things sensors machine learning deep learning

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image sensing with multilayer nonlinear optical neural networks

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NATURE PHOTONICS 2023年第5期17卷 408-+页

作者： Wang, Tianyu Sohoni, Mandar M. Wright, Logan G. Stein, Martin M. Ma, Shi-Yuan Onodera, Tatsuhiro Anderson, Maxwell G. McMahon, Peter L. Cornell Univ Sch Appl & Engn Phys Ithaca NY 14850 USA NTT Res Inc Phys & Informat Labs Sunnyvale CA 94303 USA Cornell Univ Kavli Inst Cornell Nanoscale Sci Ithaca NY 14850 USA

A nonlinear optical neural network image sensor based on an image intensifier enables efficient all-optical image encoding for a variety of machine-vision tasks. Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object's position or contour, is performed by computational analysis of a digitized image. An emerging image-sensing paradigm relies on optical systems that-instead of performing imaging-act as encoders that optically compress images into low-dimensional spaces by extracting salient features;however, the performance of these encoders is typically limited by their linearity. Here we report a nonlinear, multilayer optical neural network (ONN) encoder for image sensing based on a commercial image intensifier as an optical-to-optical nonlinear activation function. This nonlinear ONN outperforms similarly sized linear optical encoders across several representative tasks, including machine-vision benchmarks, flow-cytometry image classification and identification of objects in a three-dimensionally printed real scene. For machine-vision tasks, especially those featuring incoherent broadband illumination, our concept allows for a considerable reduction in the requirement of camera resolution and electronic post-processing complexity. In general, image pre-processing with ONNs should enable image-sensing applications that operate accurately with fewer pixels, fewer photons, higher throughput and lower latency.

关键词： Applied optics Imaging and sensing

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An image quality assessment method based on edge extraction and singular value for blurriness

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machine vision AND applications 2024年第3期35卷 37-37页

作者： Zhou, Lei Liu, Chuanlin Yadav, Amit Azam, Sami Karim, Asif Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu Peoples R China Sichuan DOOV Intelligent Cloud Valley Co Ltd Yibin Peoples R China Charles Darwin Univ Fac Sci & Technol Darwin NT 0909 Australia

The automatic assessment of perceived image quality is crucial in the field of image processing. To achieve this idea, we propose an image quality assessment (IQA) method for blurriness. The features of gradient and singular value were extracted in this method instead of the single feature in the traditional IQA algorithms. According to the insufficient size of existing public image quality assessment datasets to support deep learning, machine learning was introduced to fuse the features of multiple domains, and a new no-reference (NR) IQA method for blurriness denoted Feature fusion IQA(Ffu-IQA) was proposed. The Ffu-IQA uses a probabilistic model to estimate the probability of each edge detection blur in the image, and then uses machine learning to aggregate the probability information to obtain the edge quality score. After that uses the singular value obtained by singular value decomposition of the image matrix to calculate the singular value score. Finally, machine learning pooling is used to obtain the true quality score. Ffu-IQA achieves PLCC scores of 0.9570 and 0.9616 on CSIQ and TID2013, respectively, and SROCC scores of 0.9380 and 0.9531, which are better than most traditional image quality assessment methods for blurriness.

关键词： image quality assessment No-reference Blur Gradient Singular value machine learning

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Joint loss function design in diffractive optical neural network classifiers for high power efficiency

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Optics Express 2025年第4期33卷 7307-7320页

作者： Fan, Mengguang Jin, Shuping Gu, Yinwei Zhao, Xiangyue Bai, Ningfeng Wang, Qilong Lu, Changgui School of Electronic Science and Engineering Southeast University Nanjing211189 China

The high speed, wide bandwidth, and parallel processing capabilities of a diffractive optical neural network (DONN) stimulate its applications in computer vision for image recognition and information processing tasks. This paper presents a joint loss function (J-SCE) that combines classification performance and diffractive power efficiency, thereby improving the power efficiency of the DONN classifier from 0.92% to 12.89% while maintaining a classification accuracy of 95.36%. The J-SCE function improves the overall power efficiency of the system by directing energy more effectively toward the target area. Furthermore, the J-SCE function enhances the system’s robustness to noise and overall stability. This work significantly contributes to the application of DONN classifiers in practical image recognition and other information processing scenarios. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Imaging systems Information processing Light matter interactions machine vision Optical computing Optical neural systems

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A review of adaptable conventional image processing pipelines and deep learning on limited datasets

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machine vision AND applications 2024年第2期35卷 25-25页

作者： Muenke, Friedrich Rieken Schuetzke, Jan Berens, Felix Reischl, Markus KIT Inst Automat & Appl Informat IAI Hermann von Helmholtz Pl 1 D-76344 Karlsruhe Baden Wurttembe Germany Univ Appl Sci Ravensburg Weingarten Inst Artificial Intelligence Doggenriedstr D-88250 Weingarten Baden Wurttembe Germany

The objective of this paper is to study the impact of limited datasets on deep learning techniques and conventional methods in semantic image segmentation and to conduct a comparative analysis in order to determine the optimal scenario for utilizing both approaches. We introduce a synthetic data generator, which enables us to evaluate the impact of the number of training samples as well as the difficulty and diversity of the dataset. We show that deep learning methods excel when large datasets are available and conventional image processing approaches perform well when the datasets are small and diverse. Since transfer learning is a common approach to work around small datasets, we are specifically assessing its impact and found only marginal impact. Furthermore, we implement the conventional image processing pipeline to enable fast and easy application to new problems, making it easy to apply and test conventional methods alongside deep learning with minimal overhead.

关键词： Deep learning Conventional image processing Comparison Synthetic data

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