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Real-time analysis of liquid jet sample delivery stability for an X-ray free-electron laser using machine vision

JOURNAL OF APPLIED CRYSTALLOGRAPHY 2024年第Pt 6期57卷 1859-1870页

作者： Patel, Jaydeep Round, Adam de Wijn, Raphael Vakili, Mohammad Giovanetti, Gabriele Monrroy Vilan e Melo, Diogo Filipe E, Juncheng Sikorski, Marcin Koliyadu, Jayanth Koua, Faisal H. M. Sato, Tokushi Mancuso, Adrian Peele, Andrew Abbey, Brian La Trobe Univ Sch Comp Engn & Math Sci Melbourne Vic Australia European XFEL Schenefeld Germany Deutsch Elektronen Synchrotron DESY Ctr Free Electron Laser Sci CFEL Notkestr 85 D-22607 Hamburg Germany Diamond Light Source Didcot Oxon England Australian Nucl Sci & Technol Org ANSTO Australian Synchrotron Clayton Vic Australia La Trobe Univ La Trobe Inst Mol Sci LIMS Melbourne Vic Australia

Automated evaluation of optical microscopy images of liquid jets, commonly used for sample delivery at X-ray free-electron lasers (XFELs), enables real-time tracking of the jet position and liquid jet hit rates, defined here as the proportion of XFEL pulses intersecting with the liquid jet. This method utilizes machine vision for preprocessing, feature extraction, segmentation and jet detection as well as tracking to extract key physical characteristics (such as the jet angle) from optical microscopy images captured during experiments. To determine the effectiveness of these tools in monitoring jet stability and enhancing sample delivery efficiency, we conducted XFEL experiments with various sample compositions (pure water, buffer and buffer with crystals), nozzle designs and jetting conditions. We integrated our real-time analysis algorithm into the Karabo control system at the European XFEL. The results indicate that the algorithm performs well in monitoring the jet angle and provides a quantitative characterization of liquid jet stability through optical image analysis conducted during experiments.

关键词： liquid sample delivery image processing serial crystallography machine vision algorithms X-ray free-electron lasers

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Automatic visual recognition, detection and classification of weeds in cotton fields based on machine vision

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CROP PROTECTION 2025年 187卷

作者： Memon, Muhammad Sohail Chen, Shuren Shen, Baoguo Liang, Runzhi Tang, Zhong Wang, Shuai Zhou, Weiwei Memon, Noreena Jiangsu Univ Key Lab Modern Agr Equipment & Technol Minist Educ Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sch Agr Engn Zhenjiang 212013 Jiangsu Peoples R China Sindh Agr Univ Fac Agr Engn Dept Farm Power & Machinery Tandojam 70060 Pakistan Jiangsu Aviat Tech Coll Zhenjiang Key Lab UAV Applicat Technol Zhenjiang 212134 Peoples R China

Crops and weeds are involved in a continuous competition for equal resources, which may result in a potential decrease in crop yields by up to 31% and an increase in the costs of agricultural inputs by up to 22% of cultivation. Weeds further impact crop production, and their detection is crucial for effective crop management. In this research, we targeted common weeds of cotton field, specifically i) Digitaria sanguinalis (L.) Scop, ii) Amaranthus retroflexus L., iii) Acalypha australis, L., iv) Cephalanoplos segetum, and v) Chenopodium album L. Additionally, image processing techniques such as grayscale conversion, binarization, and Gaussian and morphological filters were also utilized. These methods are based on machine vision and facilitate rapid and straightforward weed detection by segmenting, scrutinizing, and comparing input images. The plant height and area were obtained during cotton planting within 32 days and fitted to develop the growth law concerning planting days for achieving the function of distinguishing cotton from weeds. We conducted recognition experiments by dividing images into four quadrants and categorizing weeds as either inter-row or intra-row. Meanwhile, the inter-row planting information was used to identify weeds, and the leaf pixel area and circularity were used as the identification methods for intra-row weeds, which reduced the algorithm's running time and improved real-time performance. The experimental results indicated that the inter-row weed recognition rate was 89.4%, with an average processing time of 102ms. Whereas in the case of intra-row weeds, the recognition rate was measured at 84.6%, and the overall recognition rate for cotton was 85.0%, with a mean time consumption of 437ms. Furthermore, the present research underscores recent advancements such as machine vision and high-resolution imaging, which have significantly improved the accuracy of automated weed identification in cotton fields while acknowledging ongoing challen

关键词： Weed detection Inter-row weeds Intra-row weeds machine vision algorithms Weed segmentation Cotton crop Precision farming

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Small and Medium Scale Automation in iPS cell Culture utilizing AI Based Learning and machine vision 21

Small and Medium Scale Automation in iPS cell Culture utiliz...

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IEEE 21st International Conference on Industrial Informatics (INDIN)

作者： Artmann, Lucas Sun, Yilun Ameres, Valentin Elbs, Linus Lueth, Tim C. Tech Univ Munich Inst Micro Technol & Med Device Technol Garching Germany

ISBN: (纸本)9781665493130

In this paper, we propose a concept for AI and machine vision for the observation and assessment of induced pluripotent stem (iPS) cell culture based on a differentiation of the two major monitoring aspects of cell expansion - cell health and cell density. The proposed concept is part of a more holistic approach to automating cell cultures in smaller laboratories. The concept embedded the broader automation of basic cell culture procedures using iPS cells and expansion protocols, as well as implementing mechanisms and a multipurpose gripper for discarding old and refilling fresh culture media and handling individual vessels. Based on phase contrast microscopy imaging our approach involves utilizing machine vision algorithms to calculate cell density and decide whether to split the culture or not. We implemented an image algorithm to achieve this and used threshold values derived from the working experiences with iPS cells. We concluded that our approach's achieved accuracy is sufficient to automate this task of cell assessment. The process is modular so that the proposed algorithms can be implemented with manually taken images or fully automated imaging. A trained image-based AI can be used to obtain a decision if the shown phase contrast image only shows healthy cells. If there are any not trained formations, the cells will be discarded. Future work includes completing the set of images for training the AI to recognize any deviation i.e. contamination, premature differentiation, or cell death for the health aspect of cell expansion monitoring. We aim to streamline and automate basic iPS cell culture procedures and make them more accessible to smaller laboratories. The proposed algorithms for cell health and available growing space assessment seem promising and may help with decision-making and ensure the health and growth of the iPS cells.

关键词： iPS cells Automation Cell density Image analysis machine vision algorithms Phase contrast microscopy

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Intelligent Detection System for Power Wireless Signal Based on machine vision Algorithm 3

Intelligent Detection System for Power Wireless Signal Based...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Qin, Weiwei Ma, Xiaoyan Lu, Qiqi Liang, Jian Liu, Jingquan Li, Yi Nanning Power Supply Bureau Guangxi Nanning China

ISBN: (纸本)9798350317022

Due to the complex structure of power cables, which are often laid underground, if a fault occurs and cannot be located and repaired in a timely manner, it would bring huge economic losses. In order to ensure the safety and stability of the power grid, it is very important to study how to quickly and accurately locate faults in the power grid based on the propagation characteristics of wired and wireless waves. This article aimed to solve key problems in cognitive wireless communication systems by studying signal detection algorithms based on machine vision algorithms. In order to overcome the shortcomings of existing detection methods, this article planed to use machine vision algorithms to design a continuous mode detection method, and compare it with the detection methods of the guide line algorithm and the improved ORB (Oriented Fast and Rotated Brief) algorithm. Experiments showed that the signal detection system based on machine vision algorithms had good accuracy and comprehensive signal detection capabilities, and took shorter time than other algorithms when recognizing images (the recall rate and f1-score of this system were above 0.9, and when the number of images increased from 100 to 1000, the time range was 50-100 seconds, with an average accuracy of 95.817%). © 2023 IEEE.

关键词： High-Definition Images machine vision algorithms Power Wireless Signal Detection RadiolmageDet Algorithm Model

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An automatic visual inspection system to scan outer lenses of automotive rear lamps 11

An automatic visual inspection system to scan outer lenses o...

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Conference on Optical Measurement Systems for Industrial Inspection XI held at SPIE Optical Metrology Symposium

作者： Fontanot, Tommaso Ermacora, Denis Simonetti, Giulio Raducci, Sebastian Vesselli, Erik Paroni, Sara Univ Trieste Dept Phys Via A Valerio 2 I-34127 Trieste Italy Automot Lighting Italia SpA Via Ind 17 I-33028 Udine Italy DataMind Srl Via Linussio 51 I-33100 Udine Italy CNR IOM TASC Lab Area Sci PkSS 14 Km 163-5 I-34149 Trieste Italy

ISBN: (数字)9781510627925

ISBN: (纸本)9781510627925

The inspection of defects is an important task in many industrial sectors: from metals to plastics, passing through glass and other materials, these products need to satisfy some aesthetical and quality requirements. Flaws can arise in many different forms: spot of different color, crack, incompleteness, excess and/or lack of material are just some examples of defects deriving from the industrial manufacturing process, which can lead to discard the component or the piece examined. These defects are recognizable by the human eye, but some issues like fatigue, illness of the operator and incorrect lighting of the samples can be tough obstacles in obtaining the right selection of the pieces. To detect faulty pieces and in order to avoid wasting compliant pieces instead, a computer based visual inspection system has been designed and implemented. As benchmark samples we adopt the outer lenses of automotive rear lamps. The surface of an outer lens needs an extreme precision manufacturing procedure and the absence of defects is essential for the quality of the final product. The aim of the work involves the ideation and commissioning of a setup to extract and analyze information about the flaws present in an outer lens, exploiting different image processing techniques depending on the nature of the defects.

关键词： Automatic inspection automotive rear lamps nondestructive fault detection machine vision algorithms high volume production control fast industrial inspection optical properties

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Compensation of thread profile distortion in image measuring screw thread

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MEASUREMENT 2018年 129卷 582-588页

作者： Chen, Manlong Shaanxi Univ Technol Sch Mech Engn Hanzhong 723000 Shaanxi Peoples R China

The thread measuring method possesses an efficient and easy to install with image measurement of axial section but results in thread profile distortion. Through the geometric analysis of the axial section projection image of the screw thread, it is confirmed that phenomenon of thread profile distortion always exist in measuring screw thread by projection image, meanwhile, the requirements of depth of focus is put forward with the method, calculation formula of thread profile distortion is deduced, and the corresponding compensation algorithm is given;through the simulation, analysis of coarse thread, it found the distortion has influence in thread angle and pitch diameter significantly, reaching 2.3% and 3.36% respectively, the unilateral shaded proportion of the thread profile in the root region is up to 7.12%, but the root diameter itself remains unaffected mostly. Take metric coarse thread gauge as measurement object, and compared the results with tool microscope, experiments show that the compensation effect is more than 85% in pitch diameter, more than 70% in thread angle.

关键词： Image detection systems machine vision algorithms Image analysis Thread profile distortion

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NEURAL NETWORK MODEL FOR VIDEO-BASED FACE RECOGNITION WITH FRAMES QUALITY ASSESSMENT

COMPUTER OPTICS

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COMPUTER OPTICS 2017年第5期41卷 732-742页

作者： Nikitin, M. Yu. Konushin, V. S. Konushin, A. S. Moscow MV Lomonosov State Univ Computat Math & Cybernet Fac ASVK Dept Graph & Multimedia Lab Moscow Russia Moscow MV Lomonosov State Univ Moscow Russia Video Anal Technol LLC Moscow Russia Natl Res Univ Higher Sch Econ Moscow Russia

This paper addresses a problem of video-based face recognition. We propose a new neural network model that uses an input set of facial images of a person to produce a compact, fixed-dimension descriptor. Our model is composed of two modules. The feature embedding module maps each image onto a feature vector, while the face quality assessment module estimates the utility of each facial image. These feature vectors are weighted based on their utility estimations, resulting in the image set feature representation. During visual analysis we found that our model learns to use more information from high-quality face images and less information from blurred or occluded images. The experiments on YouTube Faces and Janus Benchmark A (IJB-A) datasets show that the proposed feature aggregation method based on face quality assessment consistently outperforms naive aggregation methods.

关键词： face recognition video analysis neural networks deep learning machine vision algorithms

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vision-Based On-Loom Measurement of Yarn Densities in Woven Fabrics

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2015年第4期64卷 1063-1074页

作者： Schneider, Dorian Gloy, Yves-Simon Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis D-52074 Aachen Germany Rhein Westfal TH Aachen Inst Text Tech D-52074 Aachen Germany

A vision-based measurement system to quantify the yarn density of woven fabrics during production is presented. As an extension to an earlier developed fabric flaw detection system, the proposed framework consists of a combination of basic and custom-made image-processing techniques that allow to precisely track single wefts and warps within fabric images-in real-time. Several adaptations facilitate the measurement of density changes for plain, satin, and twill weaves. In this paper, the algorithmic framework has been evaluated in several comprehensive on-line experiments on a real-world air-jet loom and is additionally compared with three alternative methods for fabric density measurement. It proved to be precise, robust, and applicable for industrial use as it overcomes many of the existing shortcomings of current methods.

关键词： Fabric quality control machine vision algorithms textile inspection vision-based measurement (VBM) yarn density measurement

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REDUCING BACKGROUND FALSE POSITIVES FOR FACE DETECTION IN SURVEILLANCE FEEDS

COMPUTER OPTICS

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COMPUTER OPTICS 2016年第6期40卷 958-967页

作者： Sergeev, A. E. Konushin, A. S. Konushin, V. S. Natl Res Univ Higher Sch Econ Moscow Russia Video Anal Technol LLC Moscow Russia

This paper addresses a problem of false positive detection filtering in surveillance video streams. We propose two methods. The first one is based on automatic hard negative mining from a video stream, which is then used for fine-tuning of the baseline detector. The second one is the detector output filtering by analyzing the frequency of detection of visually similar samples. We demonstrate the proposed methods on cascade-based detectors, but they can be applied to any detector that can be trained in a reasonable amount of time. Experimental results show that the proposed methods improve both the precision and recall rate, as well as reducing the computational time by 47%.

关键词： detectors pattern recognition image analysis machine vision algorithms

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Extraction of corner-edge-surface structure from range images using mathematical morphology

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1995年第12期E78D卷 1636-1641页

作者： Chen, CS Hung, YP Wu, JL Natl Taiwan Univ Taipei Taiwan

Mathematical morphology is inheriently suitable for range image processing because it can deal with the shape of a function in a natural and intuitive way. In this paper, a new approach to the extraction of the corner-edge-surface structure from 3D range images is proposed. Morphological operations are utilized for segmenting range images into smooth surface regions and high-variation surface regions, where the high-variation surface regions are further segmented into regions of edge type and regions of corner type. A new 3D feature, HV-skeleton, can be extracted for each high-variation surface region. The HV-skeletons can be thought of as the skeletons of high-variation surface regions and are useful for feature matching. The 3D features extracted by our approach are invariant to 3D translations and rotations, and can be utilized for higher level vision tasks such as registration and recognition. Experimental results show that the new 3D feature extraction method works well for both simple geometric objects and complex shaped objects such as human faces.

关键词： machine vision algorithms feature extraction range data 3D shapes

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