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Anomaly Detection and Control in Supply Chains Based on image Feature Modeling

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2025年第7期39卷

作者： Pan, Yue Nanjing Univ Informat Sci & Technol Coll Business Nanjing 210044 Jiangsu Peoples R China

Ensuring process quality in supply chains is a critical challenge due to the complexity and variability of production processes. Conventional methods often struggle to accurately detect and address quality issues in such dynamic environments. This paper proposes a novel anomaly detection and control method based on image feature modeling to enhance process monitoring and decision-making in supply chain operations. By leveraging advanced image processing techniques, key features of production processes are extracted and modeled, enabling accurate identification of deviations and anomalies. Experimental results demonstrate that the proposed method significantly improves detection accuracy and response time compared to traditional approaches. This study contributes to the development of intelligent quality control solutions, offering scalability and robustness for real-world supply chain applications.

关键词： image feature modeling supply chain quality control anomaly detection process monitoring advanced image processing

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Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce

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RADIOGRAPHY 2021年第S1期27卷 S63-S68页

作者： Parkinson, C. Matthams, C. Foley, K. Spezi, E. Cardiff Univ Sch Engn Cardiff Wales Velindre Canc Ctr Cardiff Wales

Objective: Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing techniques, there is an increasing amount of data available to practitioners. In this narrative review, Artificial Intelligence (AI) is used as a reference to machine learning, and its potential, along with current problems in the field of radiation oncology, are considered from a technical position. Key Findings: AI has the potential to harness the availability of data for improving patient outcomes, reducing toxicity, and easing clinical burdens. However, problems including the requirement of complexity of data, undefined core outcomes and limited generalisability are apparent. Conclusion: This original review highlights considerations for the radiotherapy workforce, particularly therapeutic radiographers, as there will be an increasing requirement for their familiarity with AI due to their unique position as the interface between imaging technology and patients. Implications for practice: Collaboration between AI experts and the radiotherapy workforce are required to overcome current issues before clinical adoption. The development of educational resources and standardised reporting of AI studies may help facilitate this. (C) 2021 Published by Elsevier Ltd on behalf of The College of Radiographers.

关键词： Artificial intelligence advanced image processing Radiation oncology Radiography Machine learning Data science

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Heuristic approach for computer-aided lesion detection in mammograms

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SOFT COMPUTING 2016年第10期20卷 4193-4202页

作者： Ogiela, Marek R. Krzyworzeka, Natalia AGH Univ Sci & Technol Cryptog & Cognit Informat Res Grp Al Mickiewicza 30 PL-30059 Krakow Poland

Digital mammography is a common screening method for early detection of breast cancer. Its efficiency varies from 60 to 90 %, depending on various factors such as breast density, quality of the mammogram as well as experience and knowledge of the radiologist (Andolina and Lill, in Mammographic imaging: a practical guide, Lippincott Williams & Wilkins, Philadelphia, 2010). One of the most effective ways to increase the cancer detection rate is double reading (Fernandez-Lozano in Soft Comput 19(9):2469-2480, 2015). Regarding the fact that only the early-stage patients have high chances of survival, a computer-aided detection (CAD) system for mammography that provides a second, independent diagnosis should be considered a valuable and lifesaving tool. In this paper we present a new heuristic approach focused on analyzing characteristics of the mammogram that may indicate the presence of breast cancer. Described soft computing detection algorithm based on local features allows us to extract microcalcifications and possible tumor areas from the image. Because calcifications are associated with certain types of lesions, we believe that this idea would result in improving existing medical information systems. Future inclusion of fuzzy classifiers in the algorithm may also provide additional diagnostic value. Conducted research confirms that proposed procedure correctly identifies the regions of interest and could be used as a base of a CAD system in a double-reading procedures.

关键词： advanced image processing Computer-aided diagnosis Soft computing in lesion detection Mammograms disease recognition

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Machine vision based characterization of particle shape and asphalt coating in Reclaimed Asphalt Pavement

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TRANSPORTATION GEOTECHNICS 2016年 6卷 26-37页

作者： Moaveni, Maziar Cetin, Sedat Brand, Alexander S. Dahal, Sachindra Roesler, Jeffery R. Tutumluer, Erol Univ Illinois Dept Civil & Environm Engn 205 North Mathews Urbana IL 61801 USA Afyon Kocatepe Univ Dept Civil Engn Gazligol Yolu TR-03200 Afyon Turkey

Reclaimed Asphalt Pavement (RAP) particles are created from the impact removal and/or reprocessing of existing asphalt layers. RAP particles contain a combination of asphalt and aggregates with varying degrees of coating and morphology. Particle size and shape properties, amount of asphalt coating the RAP particles, and the binder content of the RAP are among the important engineering properties that control the performance of this material. This paper introduces an innovative machine vision-based inspection system to quantify the percentage of asphalt coating in different RAP aggregate sources. The Enhanced-University of Illinois Aggregate image Analyzer (E-UIAIA) is used to acquire the color RGB images of RAP particles from six different sources with sizes between 1/4 in. (6.35 mm) and 1/2 in. (12.5 mm). The influence of asphalt coating percentage on the RAP particle size and shape properties are quantified in this paper. Then, using the advanced color image thresholding scheme incorporated in the E-UIAIA, the corresponding segmented binary images of RAP particles are generated. A newly defined image mean property is used as an automatic variable threshold limit to segment the bright areas in the associated grayscale version of RAP images to detect the uncoated areas on each particle. A relationship was found between the results of the proposed image processing technique in terms of asphalt coating percentages and the asphalt content of the RAP. Furthermore, the asphalt surface coating percentages could be successfully correlated to the fracture energies of concrete specimens containing these RAP particles blended with other virgin aggregates. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Reclaimed Asphalt Pavement (RAP) Aggregate advanced image processing Asphalt coating Asphalt content

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Automatic image Classification for the Urinoculture Screening 1

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7th KES International Conference on Intelligent Decision Technologies (KES-IDT)

作者： Andreini, Paolo Bonechi, Simone Bianchini, Monica Mecocci, Alessandro Di Massa, Vincenzo Univ Siena Dept Informat Engn & Math Via Roma 56 I-53100 Siena Italy Diesse Ric Srl I-53100 Siena Italy

ISBN: (数字)9783319198576

ISBN: (纸本)9783319198576;9783319198569

Urinary Tract Infections (UTIs) represent a significant health problem, both in hospital and community-based settings. Normally, UTIs are diagnosed by traditional methods, based on cultivation of bacteria on Petri dishes, followed by a visual evaluation by human experts. In this paper, we present a fully automated system for the screening, that can provide quick and traceable results of UTIs. Actually, based on image processing techniques and machine learning tools, the recognition of bacteria and the colony count are automatically carried out, yielding accurate results. The proposed system, called AID (Automatic Infections Detector) provides support during the whole analysis process: first digital color images of the Petri dishes are automatically captured, then specific preprocessing and spatial clustering algorithms isolate the colonies from the culture ground, finally an accurate classification of the infection types and their severity is performed. Some important aspects of AID are: reduced time, results repeatability, reduced costs.

关键词： Artificial Neural Networks advanced image processing Support Vector Machines Urinoculture Screening

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Automatic image Analysis and Classification for Urinary Bacteria Infection Screening 18th

Automatic Image Analysis and Classification for Urinary Bact...

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18th International Conference on image Analysis and processing (ICIAP)

作者： Andreini, Paolo Bonechi, Simone Bianchini, Monica Mecocci, Alessandro Di Massa, Vincenzo Univ Siena Dept Informat Engn & Math I-53100 Siena Italy Diesse Ric SRL I-53100 Siena Italy

ISBN: (纸本)9783319232317;9783319232300

In this paper, we present an automatic system for the screening of urinary tract infections. It is estimated that about 150 million infections of this kind occur world wide yearly, giving rise to roughly five billion health-care expenditures. Currently, Petri plates seeded with infected samples are analyzed by human experts, an error prone and lengthy process. Nevertheless, based on image processing techniques and machine learning tools, the recognition of the bacterium type and the colony count can be automatically carried out. The proposed system captures a digital image of the plate and, after a preprocessing stage to isolate the colonies from the culture ground, accurately identifies the infection type and severity. Moreover, it contributes to the standardization of the analysis process, also avoiding the continuous transition between sterile and external environments, which is typical in the classical laboratory procedure.

关键词： advanced image processing Support vector machines Urinoculture screening

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Analog VLSI layout design of advanced image processing for artificial vision model

Analog VLSI layout design of advanced image processing for a...

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IEEE International Symposium on Industrial Electronics

作者： Kawaguchi, M Jimbo, T Umeno, M Suzuka Natl Coll Technol Dept Elect Engn Suzuka Japan

ISBN: (纸本)0780387384

We propose herein an artificial vision model for the motion detection which uses analog electronic circuits and designed the analog VLSI layout. The proposed model is comprised of four layers. The model was shown to be capable of detecting a movement object in the in the 2-dimensional image. Moreover, the proposed model can be used to detect two or more objects, which is advantageous for detection in an environment in which several objects are moving in multiple directions simultaneously. The number of elements in the model, is reduced in its' realization using the integrated devices. The proposed model is robust with respect to fault tolerance. Moreover, the connection of this model is between adjacent elements, making hardware implementation easy.

关键词： VLSI analogue circuits computer vision image motion analysis advanced image processing analog VLSI layout design artificial vision model fault tolerance motion detection object detection Motion detection image motion analysis Fault tolerance analog circuits Object detection computer vision robot vision Prosthesis image processing

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Automatic Surface Inspection and Quality Control - A Survey

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IFAC Proceedings Volumes 1998年第23期31卷 183-186页

作者： Steffen Burkhardt Uwe Stein Parsytec Computer GmbH Aachen Germany

For automatic surface inspection of sheet steel today’s most advanced image capturing and processing technologies are used. By this the customer gets on the one hand a detailed description of the surface quality of the entire coils, i.e. type, severity and number of defects, and on the other hand the relevant information for quality control in the required production steps. This paper describes the basic functions of automatic surface inspection systems and gives details about Parsytec’s 100% software based solution - an automatic surface inspection system capable of detecting and classifying all relevant defects on strip steel surfaces. This does not only refer to cold rolled, but also to hot rolled steel where high strip speeds as well as high temperatures and the infra-red light of the steel surface aggravate inspection. Previously limiting factors like speed, defect size, textured surfaces, etc. do no longer influence the defect detection and classification process due to appropriate software implemented on high performance PCs in combination with new illumination technologies and high resolution CCD matrix cameras.

关键词： automatic surface inspection quality control advanced image processing

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