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Model-based contour extraction: an enabler for very low-frame SEM images metrology 36

Model-based contour extraction: an enabler for very low-fram...

Conference on Metrology, Inspection, and Process Control XXXVI Part of SPIE Advanced Lithography and Patterning Conference

作者： Sezestre, Elie Scoarnec, Juline Pradelles, Jonathan Perraud, Loic Fay, Aurelien Berard-Bergery, Sebastien Bustos, Jessy Henry, Jean-Baptiste Dubreuil, Olivier Mendes, Ivanie Valade, Charles Moly, Alexandre Batte, Alice Schuch, Nivea Robert, Frederic Figueiro, Thiago Univ Grenoble Alpes CEA Leti F-38000 Grenoble France ASELTA NANOGRAPH 4 Pl Robert Schuman F-38000 Grenoble France

ISBN: (纸本)9781510649828;9781510649811

Among metrology tools in the semi-conductor manufacturing, critical dimension scanning electron microscopes (CD-SEM) are the most broadly used, especially due to their high resolution, low destructivity, and high throughput. Contour metrology on CD-SEM images has become essential for characterization, modelling, and control of advanced lithography processes. In particular, OPC model's accuracy can be highly improved using contours metrology. One of the issues when dealing with CD-SEM metrology is that the results are noise sensitive. Moreover, diminishing noise in CD-SEM acquisition leads to resist shrinkage due to exposure time increase. In addition, post-treatment of these shrinkage effects requires compensation algorithms such as artificial intelligence (AI)driven algorithms, that are another contributor to the error budget of metrology systems. There is thus a need for an accurate, robust to noise, and purely deterministic edge detection algorithm. In this article, we evaluate the benefits of relying on a model-based contour extraction approach for performing measurements. This approach is applied onto both synthetic and experimental CD-SEM images with various patterns (mostly 2D) and noise levels to assess the influence of image integration (frame number) on the contour detection and CD measurement. We demonstrate that a model-based contour extraction algorithm is able to precisely characterize SEM-induced 2D resist shrinkage. We observe that this model-based approach is more robust to noise than standard algorithms by 21% on synthetic data and by 36% on experimental data. Another way of seeing it is, while keeping the same precision, a model-based contour extraction approach can significantly reduce the requested image frame number. The benefits of adopting this approach range from reducing the shrinkage effects to improving SEM image acquisition time. Eventually, no step of shrinkage modelling calibration nor AI-driven image post processing are needed which i

关键词： contour metrology edge detection algorithm robustness to noise ultra-low frame resist shrinkage NTD process CD-SEM accuracy

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Analysis of Image edge Checking algorithms for the Estimation of Pear Size

Analysis of Image Edge Checking Algorithms for the Estimatio...

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2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

作者： Yanru Zhao Jihua Chang The College of Mechanical and Power Engineering Henan Polytechnic University Jiaozuo Henan Province 454000 P. R. China

ISBN: (纸本)9781424472796

The lower-layer image processing algorithm is the basis of pear on-line automatic grading with machine vision. The edge detection algorithms and edge thinning algorithms of the lower-layer image processing were analyzed and the lower layer processing algorithm suitable to fruit grading was obtained in the work. The edge detection technique is one of the technologies to display the image contour distinctly with edge enhancement. The edge thinning is to process the contour lines in different sizes to lines with the width of only one pixel. Several classical difference operators for edge detection, such as Robert edge operator, Sobel edge operator, Prewitt edge operator and Kitsch edge operator were introduced. The edge detected with the classical operators was very thick, and even with complex form or not smooth, so the template detection algorithm was put forward to measure the pear's edge and its steps were shown detailedly. The Hilditch thinning method was adopted to deal with the edge image in the work and the image got with the edge thinning were fine.

关键词： edge detection algorithm edge extraction edge operator template detection algorithm edge thinning Hilditch thinning method

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Framework development for construction safety visualization

Framework development for construction safety visualization

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作者： Shrestha, Kishor University of Nevada Las Vegas

学位级别：M.S.E.

Throughout the history of the construction industry, many fatalities and injuries have occurred in construction sites. One of the major causes of accidents is unsafe site conditions; basically, this is due to inadequate supervision. To improve upon the traditional supervision approach, this study proposes a `Framework Development for Construction Safety Visualization' approach. In addition to this, a computer vision edge detection algorithm was developed and tested to convert construction site still images into edges of the objects in the images. The framework development of this study uses computer vision, robot vision, image compression, pattern recognition, internet transmission, network communication, and image processing.

关键词： Computer vision Construction industry – Accidents Construction industry – Safety measures edge detection algorithm Hard Hat Image processing Pattern recognition systems Civil Engineering Computer Engineering Construction Engineering Construction Engineering and Management

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A traffic image dehaze method based on adaptive transmittance estimation with multi-scale window

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CHINESE OPTICS 2019年第6期12卷 1311-1320页

作者： Huang He Li Xin-rui Song Jing Wang Hui-feng Ru Feng Sheng Guang-feng Changan Univ Sch Elect & Control Engn Xian 710064 Shaanxi Peoples R China Shaanxi Rd Traff Intelligent Detect & Equipment E Xian 710064 Shaanxi Peoples R China

Aiming at the halo effect and the color distortion of bright areas when using traditional dark priori image defogging algorithms, we propose a traffic image dehaze method based on adaptive transmittance estimation with multi-scale window in this paper. Firstly, a new 8-direction edge detection operator is used to detect abrupt changes in field depth in images. According to the dark channel prior theory and the abrupt change of field depth obtained in the previous step 7 a 5 x 5 window is used in the larger area of field depth transformation and a 15 x 15 window is used in the smaller area to obtain a dark primary color estimation image. At the same time, targetting the problem of inaccurate estimation of transmittance when there is a white area in the close-range region due to the dark channel priori principle, we introduce an adaptive transmittance restoration method. An edge-enhanced dark image is obtained by using a guide filter, and the texture difference between the edge-enhanced dark image and the original dark primary image is used to correct the transmittance in the close-range region, and then to complete image dehazing. The experimental results show that the halo phenomenon exists in both the bilateral filter and the gradient bilateral filter, and the color distortion is serious in the bright area containing white objects, causing the objective evaluation index to be meaningless. Compared with the guide filter, the indexes of the dehazing algorithm used in this paper show improvement, wherein the average gradient increased by 8.305%, the PSNR increased by 12.455% and the edge strength factor increased by 7.77%. The algorithm can effectively solve issues arising from the halo effect and color distortion in bright areas in restored images while providing a more effective dehazing effect.

关键词： dark channel image dehazing traffic image edge detection algorithm

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Design of a Groundwater Level Monitoring System Based on Internet of Things and Image Recognition

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TRAITEMENT DU SIGNAL 2021年第4期38卷 979-984页

作者： Bo, Qiuyu Cheng, Wuqun Agr Univ Hebei Inst Urban & Rural Construct Baoding 071001 Peoples R China

This paper designs an intelligent groundwater level monitoring system based on image recognition and Internet of things (IoT). Image recognition technology was employed to process the water level image, and determine the water level line. The IoT was adopted to transmit the collected multimedia data accurately to the monitoring end, thereby realizing the automatic remote monitoring of real-time water level. After analyzing the image recognition technology and the key algorithm of water level recognition, the authors designed the whole process of groundwater level monitoring with two modules: water level monitoring base station, and remote monitoring management center. The water level monitoring base station is embedded with a data acquisition module to periodically collect data, including water level, videos, and images. The collected data were sent to the remote monitoring management center through the cellular network. Then, flood or low water warning could be determined according to the historical data. Finally, the proposed groundwater level monitoring system was tested. The results show that the system not only solves the problem of measurement accuracy, but also improves the work efficiency.

关键词： image recognition internet of things (IoT) groundwater level monitoring edge detection algorithm

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SFMetrics: an analysis tool for scanning force microscopy images of biomolecules

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BMC BIOINFORMATICS 2015年第1期16卷 1-9页

作者： Sanchez, Humberto Wyman, Claire Erasmus Univ Med Ctr Dept Genet NL-3000 CA Rotterdam Netherlands Erasmus Univ Med Ctr Dept Radiat Oncol NL-3000 CA Rotterdam Netherlands

Background: Scanning force microscopy (SFM) allows direct, rapid and high-resolution visualization of single molecular complexes;irregular shapes and differences in sizes are immediately revealed by the scanning tip in three-dimensional images. However, high-throughput analysis of SFM data is limited by the lack of versatile software tools accessible to SFM users. Most existing SFM software tools are aimed at broad general use: from material-surface analysis to visualization of biomolecules. Results: We present SFMetrics as a metrology toolbox for SFM, specifically aimed at biomolecules like DNA and proteins, which features (a) semi-automatic high-throughput analysis of individual molecules;(b) ease of use working within MATLAB environment or as a stand-alone application;(c) compatibility with MultiMode (Bruker), NanoWizard (JPK instruments), Asylum (Asylum research), ASCII, and TIFF files, that can be adjusted with minor modifications to other formats. Conclusion: Assembled in a single user interface, SFMetrics serves as a semi-automatic analysis tool capable of measuring several geometrical properties (length, volume and angles) from DNA and protein complexes, but is also applicable to other samples with irregular shapes.

关键词： edge detection algorithm Scan Force Microscopy Asylum Research Nucleoprotein Filament Irregular Object

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Automatic recognition and statistical quantification of spatial patterns of gene expression in zebra finch brain in response to auditory stimulation

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BMC Neuroscience 2008年第1期9卷 1-2页

作者： Ovidiu D Iancu Tarciso Velho Patrick Roberts Claudio V Mello Neurological Sciences Institute Oregon Health Sciences University Portland OR 97007 USA

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A Model for Detecting Fungal Diseases in Cotton Cultivation using Segmentation and Machine Learning Approaches

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第1期14卷 628-636页

作者： Odukoya, O. H. Aina, S. Degbesse, F. W. Obafemi Awolowo Univ Comp Sci & Engn Dept Ife Nigeria

This research detailed a model for detecting fungal diseases via techniques for processing images of cotton leaves. The work allowed to develop a model based on the set of preprocessed data, to formulate the developed model, to simulate and evaluate the model. It is about detecting fungal diseases in cotton cultivation. The image data records were collected in an online data repository consisting of images of cotton leaves infected with fungal diseases and normal leaf images. In addition, other images of infected and uninfected cotton leaves were collected in cotton production fields in the Segbana region in Benin Republic. The model was formulated based on watershed segmentation technique by applying edge detection algorithm and K-Means Clustering;and Support Vector Machine (SVM) for classification. The simulation was done using MATLAB with Image Processing Toolbox 9.4. The results gave an accuracy of 99.05%, specificity 90%, misclassification rate 0.95%, recall rate 99.5% and precision 99.5%. In addition, with less computational effort and in less than a minute, the best results were obtained, showing the efficiency of the image processing technique for the detection and classification of infected and uninfected leaves. It was concluded that this approach was applied to detect fungal diseases on cotton leaves to promote the production and harvest of good quality cotton and valuable cotton products.

关键词： Fungal diseases watershed segmentation SVM K-means edge detection algorithm

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Eight Direction Improved Sobel algorithm Based on Morphological Processing in 5G Smart Grid

Eight Direction Improved Sobel Algorithm Based on Morphologi...

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作者： Wu Dong Dili Peng Xu Liu Tao Wang Jiao Long Power dispatching control center of Guizhou Power Grid Co. Ltd

Aiming at the 5 G application scenario of smart grid, it is difficult to quickly process massive videos and pictures and extract effective edge information. In this paper, based on the analysis of traditional Sobel edge detection operator, aiming at its shortcomings of noise sensitivity and limited direction template, we improve it. Firstly,morphological processing is added to smooth the image to improve the sensitivity of traditional Sobel operator to noise. Secondly, this paper proposes an algorithm of Gaussian filtering combined with improved difference formula. At the same time, Sobel operator template is improved to eight direction Sobel template based on Manhattan distance. Simulation results show that the proposed algorithm can significantly improve the noise sensitivity and edge ambiguity of existing algorithms in the aspects of boundary point suppression, anti noise, and image edge optimization.

关键词： 5G smart grid edge detection algorithm Roberts edge detection EDISA

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Design on Self-balancing and Track-Searching Cart

Design on Self-balancing and Track-Searching Cart

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2011 International Conference on Image Analysis and Signal Processing(2011第三届图像分析与信号处理国际会议 IASP 2011)

作者： Zhenghuan Shaocheng Qu Department of Electronic & Electrical Engineering WuHan Institute of Shipbuilding Technology Wuhan Department of Information & Technology Central China Normal University Wuhan city Hubei Province

ISBN: (纸本)9781612848792

In this paper, a self-balancing two-wheel cart with track-searching function is designed with the technology of System on a Programmable chip (SOPC). The system checks the inclination angle of a vertical axis on the cart, and the two wheels are driven respectively with different angels and speeds to keep the cart balanced. After balancing, the system activates the camera for track-searching under the frame and the cart advances along a black line on a white background. PID algorithm is applied for keeping balance and an edge detection algorithm helps to identify the black line from the white background.

关键词： self-balance inclination angle edge detection algorithm PID SOPC

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