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image pattern recognition in identification of financial bills risk management

NEURAL COMPUTING & APPLICATIONS 2021年第3期33卷 867-876页

作者： Li, Huahua Huang, Chuanchao Gu, Lihan Shandong Univ Sci & Technol Coll Finance & Econ Tai An 266590 Shandong Peoples R China China Merchants Bank Dept Informat Technol Shenzhen 518040 Peoples R China Taishan Univ Sch Econ & Management Tai An 271000 Shandong Peoples R China

The automatic identification system of financial bills needs to have high recognition rate, high anti-interference and real-time to ensure its recognition effect. Based on the image recognition theory, this study uses the differential projection method to define the boundary of the bill. The horizontal projection gradation of the boundary area of the bill character line is significantly reduced from large to small, and the horizontal difference projection of the grayscale image can be performed to locate the boundary of the bill image. The word height of bills studied in this paper is constant, the character line spacing is equal, and the horizontal differential projection is used to realize row positioning. The main process of the system is to first select the check image, and then the software part realizes the process of image analysis, preprocessing, character segmentation, feature extraction, character recognition, etc. Finally, the recognized amount is output to the interface. It is proved by experiments that the recognition rate of this algorithm is high, which can provide theoretical reference for subsequent related research.

关键词： image pattern recognition Financial instruments Authenticity Financial risk Feature extraction

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Strength modeling for degradation of bioresorbable polyesters based on phase image pattern recognition

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POLYMER DEGRADATION AND STABILITY 2020年 181卷 109291-109291页

作者： Zhang, Taohong Chen, Han Zhao, Yue Yu, Yixuan Aziguli Univ Sci & Technol Beijing USTB Sch Comp & Commun Engn Dept Comp Beijing 100083 Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

Biomedical degradable polymer materials are widely used in medicine because of their excellent mechanical properties and degradation properties. The matching of strength change and degradation rate in the degradation process is a crucial consideration in the design of polymer devices. Establishing a strength model of the polymer degradation process can quantify the intensity evolution during the degradation process. In this research, the structural changes in the process of polymer erosion degradation are taken as the primary consideration, and the evolution of the phases of strength are reflected by the changes of structure. The concept of strength phase images is proposed. The images of the strength phase diagram are identified by the image processing method. The Near Neighbor-Boundary Enlargement Algorithm (NNBE) is put forward to identify features with the hole(block) and crack(-strip) shape patterns. The strength model for different phase patterns is established. The model calculation is compared with the experimental data, and the model simulation value and the experimental value are well fitted, which indicates that the strength model and phase diagram feature extraction algorithm are feasible. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Phase image Polymer degradation Strength model image pattern recognition

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Evaluation of tool scraping wear conditions by image pattern recognition system

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第1-4期105卷 1791-1799页

作者： Jywe, Wen-Yuh Hsieh, Tung-Hsien Chen, Po-Yu Wang, Ming-Shi Lin, Yu-Tso Natl Formosa Univ Smart Machinery & Intelligent Mfg Res Ctr Dept Automat Engn Huwei Township 632 Yunlin Taiwan Natl Cheng Kung Univ Dept Engn Sci Tainan 701 Taiwan

An image pattern recognition system, consisting of a CMOS camera, a set of illumination devices, and a lab-developed machine vision image analysis software, has been developed for evaluation of tool scraping wear conditions. The images of the scraped surface texture on the workpiece were generated by lab-developed automatic scraping machine under controlled conditions;the image was first transformed by Haar wavelet transform with Otsu's algorithm to discriminate the vertical details of surface patterns, and they were then emphasized by weighted calculations according to tool scraping wear conditions. The experimental results show that the average difference between the data obtained from the surface image analysis and the roughness measurement is about +/- 2%, proving the feasibility of the proposed system. Furthermore, a statistic analysis reveals that the standard deviation of the non-zero proportion of the medium frequency domain obtained by image processing might be considered a significant reference for tool replacement. For the field applications, the proposed system provides the capability of online scraping tool wear evaluation for scraping process in manufacturing site, compared with the traditional scraping tool wear evaluation method, which was time consuming;user needs to remove the workpiece to measure instrument.

关键词： image pattern recognition Machine vision Tool scraping wear Surface texture

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A 600-μW ultra-low-power associative processor for image pattern recognition employing magnetic tunnel junction-based nonvolatile memories with autonomic intelligent power-gating scheme

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JAPANESE JOURNAL OF APPLIED PHYSICS 2016年第4期55卷 04EF15-04EF15页

作者： Ma, Yitao Miura, Sadahiko Honjo, Hiroaki Ikeda, Shoji Hanyu, Takahiro Ohno, Hideo Endoh, Tetsuo Tohoku Univ Ctr Innovat Integrated Elect Syst Sendai Miyagi 9800845 Japan Tohoku Univ Elect Commun Res Inst Sendai Miyagi 9808577 Japan Tohoku Univ Grad Sch Engn Sendai Miyagi 9808579 Japan JST ACCEL Sendai Miyagi 9808579 Japan

A novel associative processor using magnetic tunnel junction (MTJ)-based nonvolatile memories has been proposed and fabricated under a 90 nm CMOS/70 nm perpendicular-MTJ (p-MTJ) hybrid process for achieving the exceptionally low-power performance of image pattern recognition. A four-transistor 2-MTJ (4T-2MTJ) spin transfer torque magnetoresistive random access memory was adopted to completely eliminate the standby power. A self-directed intelligent power-gating (IPG) scheme specialized for this associative processor is employed to optimize the operation power by only autonomously activating currently accessed memory cells. The operations of a prototype chip at 20 MHz are demonstrated by measurement. The proposed processor can successfully carry out single texture pattern matching within 6.5 mu s using 128-dimension bag-of-feature patterns, and the measured average operation power of the entire processor core is only 600 mu W. Compared with the twin chip designed with 6T static random access memory, 91.2% power reductions are achieved. More than 88.0% power reductions are obtained compared with the latest associative memories. The further power performance analysis is discussed in detail, which verifies the special superiority of the proposed processor in power consumption for large-capacity memory-based VLSI systems. (C) 2016 The Japan Society of Applied Physics

关键词： magnetic tunnelling PROCESSOR power reduction Processor Core Tunnel junctions magnetoresistive random access memory Static random access storage Energy sources image pattern recognition power performance

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Maschinelles Lernen zur Identifikation pathologischer Myokardregionen: ein innovativer Ansatz für die digitale Pathologie

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RECHTSMEDIZIN 2025年第2期35卷 80-88页

作者： Corvest, E. Mayer, R. S. Kocuk, C. Wilmes, V. Gretser, S. Gradhand, E. Wild, P. J. Verhoff, M. A. Flinner, N. Kauferstein, S. Goethe Univ Univ Med Frankfurt Inst Rechtsmed Zentrum Plotzlichen Herztod & Kardiogenet Frankfurt Germany Goethe Univ Univ Med Frankfurt Dr Senckenberg Inst Pathol & Humangenet Frankfurt Germany Goethe Univ Univ Med Frankfurt Inst Rechtsmed Abt Forens Med Frankfurt Germany

Background/objectiveDigitalization and artificial intelligence (AI) are increasingly being tested and integrated into the evaluation of histological sections. While the application in the field of tumor pathology is already well advanced, experience with the digital analysis of histological sections of the myocardium is limited. We present a project for AI-supported analysis of histological samples in myocardial infarction, where we tested whether a specifically trained algorithm could distinguish healthy myocardial sections from those affected by ischemia. Material and methodsA total of 106 slides with HE-stained myocardial sections from 50 deceased individuals were digitalized and annotated. A convolutional neural network (CNN) based on a ResNet-18 architecture was trained for AI-assisted classification of the digitalized myocardial sections. The data were divided and slide-wise stratified into a training, validation and test dataset and analyzed by the deep learning algorithm for image pattern recognition of the CNN. ResultsThe algorithm developed was able to reliably distinguish healthy myocardium from pathological alterations in most cases during the test runs. Healthy myocardial tissue was detected with a precision of 81%, infarcted tissue with 78% and fibrosis with 85%. DiscussionThe insights gained in this project will be used to further develop the approach of AI-supported analysis of myocardial sections. With the expansion of the sample in terms of the number of cases and the pathologies present, future applications may arise in histological examinations of sudden cardiac death, especially in cases of underlying rare diseases (hereditary cardiomyopathies, etc.), where to date only limited micromorphological correlates could be identified using conventional histological analysis.

关键词： K & uuml nstliche Intelligenz Myokardisch & auml mie Fibrose Pl & ouml tzlicher Herztod Bildmustererkennung Artificial intelligence Myocardial ischemia Fibrosis Sudden cardiac death image pattern recognition

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Rapid detection of imperfect wheat grains based on deep learning technique 2

Rapid detection of imperfect wheat grains based on deep lear...

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2nd International Conference on image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Hu, Kui Zhang, Hongming Lyu, Bo Shen, Yongcai Fu, Jia Wang, Fudi Lin, Zichao Anhui University Institute of Physical Science and Information Technology Hefei China Chinese Academy of Sciences Institute of Plasma Physics HFIPS Hefei China University of Science and Technology of China Science Island Branch Graduate School Hefei China Hefei Normal University Hefei China

ISBN: (纸本)9798350331417

The detection and identification of imperfect wheat grains are of great significance in evaluating their quality. Manual inspection and separation of imperfect grains in wheat are time-consuming and expensive. Therefore, there is a need for a fast, automated, and accurate method to detect imperfect grains in wheat. In this study, we created an image acquisition platform to capture images of wheat grains. Each image was labeled as either spotted, sprouted, moldy, broken, or perfect grains. To balance calculation efficiency and prediction accuracy, it is necessary to identify a suitable model. Thus, we applied several mainstream deep-learning algorithms, including ResNet50, ResNet50 with an attention module, MobileNetv1, MobileNetv2, MobileNetv3-small, and MobileNetv3-large, to construct a model suitable for practical application. It was found that the MobileNetV3-Small model achieved a good balance between computational efficiency and prediction accuracy. The model based on the MobileNetV3-Small architecture achieved high accuracy, recall rate, and F1-score, all exceeding 96%. Although the MobileNetV3-Small model's accuracy is slightly lower than that of the ResNet50 model with an added attention mechanism, it significantly reduces computational costs, improves detection speed, and cuts prediction time by 50%. Compared with other models, the MobileNetv3-small-based model is more suitable for practical applications due to its advantages of high speed, high precision, and stable prediction performance. This study can provide technical guidance for intelligent recognition and detection of wheat grains in the future. © 2023 IEEE.

关键词： deep learning image pattern recognition MobileNetV3-small Wheat imperfect grains

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Computational Methods Toward Unbiased pattern Mining and Structure Determination in Cryo-Electron Tomography Data

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JOURNAL OF MOLECULAR BIOLOGY 2023年第9期435卷 168068页

作者： Kim, Hannah Hyun-Sook Uddin, Mostofa Rafid Xu, Min Chang, Yi-Wei Univ Penn Perelman Sch Med Dept Biochem & Biophys Philadelphia PA 19104 USA Carnegie Mellon Univ Sch Comp Sci Computat Biol Dept Pittsburgh PA 15213 USA

Cryo-electron tomography can uniquely probe the native cellular environment for macromolecular struc-tures. Tomograms feature complex data with densities of diverse, densely crowded macromolecular com-plexes, low signal-to-noise, and artifacts such as the missing wedge effect. Post-processing of this data generally involves isolating regions or particles of interest from tomograms, organizing them into related groups, and rendering final structures through subtomogram averaging. Template-matching and reference-based structure determination are popular analysis methods but are vulnerable to biases and can often require significant user input. Most importantly, these approaches cannot identify novel com-plexes that reside within the imaged cellular environment. To reliably extract and resolve structures of interest, efficient and unbiased approaches are therefore of great value. This review highlights notable computational software and discusses how they contribute to making automated structural pattern discov-ery a possibility. Perspectives emphasizing the importance of features for user-friendliness and accessi-bility are also presented. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Cryo-ET image pattern recognition data mining machine learning cellular structural biology

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Reliable analog resistive switching behaviors achieved using memristive devices in AlO x /HfO x bilayer structure for neuromorphic systems

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SEMICONDUCTOR SCIENCE AND TECHNOLOGY 2022年第3期37卷 035018-035018页

作者： Qi, Meng Fu, Tianquan Yang, Huadong Tao, Ye Li, Chunran Xiu, Xiaoming Bohai Univ Coll Phys Sci & Technol 19 Keji Rd Jinzhou 121013 Peoples R China Northeast Normal Univ Open Fdn Key Lab UV Emitting Mat & Technol Minist Educ 5268 Renmin St Changchun 130024 Peoples R China

Human brain synaptic memory simulation based on resistive random access memory (RRAM) has enormous potential to replace the traditional von Neumann digital computer thanks to several advantages, including its simple structure, its high-density integration, and its capabilities regarding information storage and neuromorphic computing. Herein, the reliable resistive switching (RS) behaviors of RRAM are demonstrated by engineering the AlO (x) /HfO (x) bilayer structure. This allows for uniform multibit information storage. Further, the analog switching behaviors are capable of imitating several synaptic learning functions, including learning experience behaviors, short-term plasticity, long-term plasticity transition, and spike-timing-dependent plasticity (STDP). In addition, the memristor based on STDP learning rules is implemented in image pattern recognition. These results may show the potential of HfO (x) -based memristors for future information storage and neuromorphic computing applications.

关键词： memristors multilevel characteristics synaptic behaviors image pattern recognition

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image-processing based taxonomy analysis of bacterial macromorphology using machine-learning models

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第43-44期79卷 32711-32730页

作者： Sajedi, Hedieh Mohammadipanah, Fatemeh Pashaei, Ali Univ Tehran Pharmaceut Biotechnol Lab Dept Microbiol Sch BiolColl Sci Tehran 141556455 Iran Univ Tehran Coll Sci Ctr Excellence Phylogeny Living Organisms Tehran 141556455 Iran Univ Tehran Coll Sci Dept Microbial Biotechnol Sch Biol Tehran 141556455 Iran Amirkabir Univ Technol Dept Math & Comp Sci Tehran Iran

Classification of bacteria is essential in the medical diagnosis of infectious agents, their phylogenetic study, and their biotechnological exploitation for healthcare, food, industry, and agricultural sectors. Nevertheless, skilled experts and professional human-effort are necessary to identify and classify the bacteria manually. With the advancement of technology, now the task of recognizing details from digital stereomicroscopes is being performed by computers based on machine learning and computer vision technologies. Besides, machine learning methods include Deep Neural Networks (NN) has attained remarkable outcomes in the field of image classification recently. Furthermore, other machine learning methods except for NN methods already have acceptable performance. In this paper, we review the publications, which investigate the discrimination between bacteria genera and suborders based on macroscopic images via image processing and machine learning methods. The published research works in this regard are summarized, and the pros and cons of them are discussed. Moreover, the related databases and resources for this purpose are surveyed, and the lack of such data points in the global catalogue of microorganisms is criticized. In addition, in this paper, we have investigated an approach to automate the process of bacteria recognition and classification with the use of the Gabor transform and XGBoost classification method. We have used a dataset that includes microscopic images of three different Myxobacterial suborders. The trained model was able to recognize and classify all members of three different categories of bacteria, while the experimental results of prediction achieved an accuracy of around 91%, which has enhancement about 2% in term of accuracy.

关键词： Bacteria Macromorphology taxonomy Bacterial colony Bacteria classification image pattern recognition

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Reliable Resistive Switching and Synaptic Behaviors Based on a TiO_x-Doped N Memristor for Information Storage and Neuromorphic Computing

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PHYSICA STATUS SOLIDI-RAPID RESEARCH LETTERS 2021年第10期15卷 2100255-2100255页

作者： Yang, Liu Lin, Dong Qi, Meng Xiu, Xiaoming Dong, Haikuan Wang, Heng Shenyang Aerosp Univ Coll Sci Shenyang 110136 Peoples R China Jimei Univ Coll Informat Engn Xiamen 361021 Peoples R China Bohai Univ Coll Phys Sci & Technol 19 Keji Rd Jinzhou 121013 Peoples R China

Bipolar resistive switching (RS) and synaptic behaviors of resistive random access memory (RRAM) based on TiO x are demonstrated. RS uniformity is improved by introducing nitrogen into the RS layer using radio frequency sputtering in the reactive Ar/N-2 ambient. The conductive mechanism is in good agreement with the space-charge-limited conduction model. The activation energy fit by the Arrhenius equation and conductive atomic force microscopy results indicate that the conductive filaments are formed by oxygen vacancies. More importantly, reliable multilevel RRAM can be achieved by tuning the compliance current, which enables the achievement of distinguishable resistance states. Furthermore, multilevel RRAM enables the simulation of synaptic functions, such as learning-forgetting-relearning, habituation, and spike-timing-dependent plasticity (STDP). image pattern recognition based on STDP learning rules using a digital memristor is demonstrated. The findings may offer a route to the development of future storage and neuromorphic computing.

关键词： image pattern recognition memristors multilevel characteristics resistive switching synaptic behavior

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