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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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Microstructure Detection by advanced image processing

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TETSU TO HAGANE-JOURNAL OF THE IRON AND STEEL INSTITUTE OF JAPAN 2017年第3期103卷 142-148页

作者： Taguchi, Motoki Hirokawa, Shogo Yasuda, Itaru Tokuda, Kohei Adachi, Yoshitaka Kagoshima Univ Grad Sch Sci & Engn Korimoto 1-21-24 Kagoshima 8900065 Japan

Two kinds of advanced image processing were applied to multi-phase microstructures. One is evolutional image processing where optimized filter set was suggested by genetic programing. Another is trainable WEKA segmentation where features are extracted by many kinds of filters, followed by machine learning for classification. Once an optimized filter set is determined, efficiency of image processing for new data set is improved remarkably in comparison with a case of manual image processing.

关键词： advanced image processing genetic programming microstructure detection trainable segmentation

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Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques

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MEASUREMENT 2025年 240卷

作者： Zhou, Shuangxi Yang, Dan Zhang, Ziyu Zhang, Jinwen Qu, Fulin Punetha, Piyush Li, Wengui Li, Ning Guangzhou Maritime Univ Sch Civil & Engn Management Guangzhou 510725 Peoples R China East China Jiaotong Univ Sch Civil Engn & Architecture Nanchang 330013 Peoples R China Univ New South Wales Ctr Infrastruct Engn & Safety Sch Civil & Environm Engn Sydney NSW 2052 Australia Univ Technol Sydney Sch Civil & Environm Engn Ultimo NSW 2007 Australia Univ Manchester Dept Solids & Struct Manchester M13 9PL England

To enhance the safety and comfort of vehicle travel, detecting pavement cracks is a critical task in road management. This article introduces an advanced single-stage target detection method utilizing the YOLOv5s algorithm to enhance real-time performance and accuracy. Initially, Squeeze-and-Excitation Networks are integrated into the model to facilitate self-learning for improved crack characterization. Subsequently, anchors computed through the K-means clustering algorithm are closely aligned with the fracture dataset, achieving an adaptation rate of 99.9 % and enhancing the recall rate of the model. Furthermore, the inclusion of the SimSPPF module from YOLOv6 diminishes memory usage and expedites detection speed. By replacing the original nearest up- sampling method with transposed convolution, optimization of up-sampling for crack datasets is achieved. Performance assessments reveal that the refined YOLOv5s algorithm attains an F1 score of 91 %, a mean Average Precision (mAP) of 93.6 %, and a 1.54 % increase in frames per second (fps) for pavement crack detection. This enhancement in detection technology signifies a substantial advancement in the maintenance and longevity of road infrastructure.

关键词： Road maintenance Crack detection YOLO algorithm AI integration advanced image processing

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CSMA: A Standalone and imageJ-Compatible Tool for Enhanced Wound Healing Assay Analysis

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IEEE ACCESS 2025年 13卷 69341-69352页

作者： Pham, Tri Thanh Sagymbayeva, Amina Elebessov, Timur Onzhanova, Zhadyra Molnar, Ferdinand Nazarbayev Univ Lab Mechanobiol Astana 010000 Kazakhstan Nazarbayev Univ Sch Sci & Humanities Dept Biol Lab Cell Growth Regulat Astana 010000 Kazakhstan

Accurate quantification of wound closure in cell migration assays is crucial yet challenging. Still, existing methods often underperform due to omitting cell detection within the wound area, resulting in biased outcomes. We developed the CSMA standalone and imageJ-compatible tool, which utilizes advanced image processing techniques, including contrast enhancement, edge detection, and morphological operations, to precisely identify and quantify cells in the wound region. CSMA offers user-friendly features and adjustable parameters to accommodate different imaging conditions, ensuring robust performance across diverse experimental setups. Validation against conventional tools confirms CSMA's superior ability to delineate wound boundaries and provide accurate estimations of area and width at every time point. As applied to SW480-ADH colon cancer cells treated with various compounds, CSMA proves valuable in biomedical research. CSMA represents a significant advancement in wound healing assay analysis, providing researchers with a simple and reliable tool for studying cell migration dynamics with enhanced precision and reproducibility. CSMA is available as a standalone and imageJ-compatible tool with its source code at https://***/AminaSagymbayeva/CSMA_WoundHealing

关键词： Wounds Multiaccess communication image edge detection Colorectal cancer Imaging image processing Solvents Python Erosion Source coding Cell migration wound healing scratch assay analysis tool advanced image processing imageJ integration

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

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2025年第07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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Integrated multispectral remote sensing approach for high-resolution spectral characterization and automated mapping of carbonate lithofacies

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EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 2024年第2期27卷 436-455页

作者： Hammam, Ahmed Korin, Asmaa Herlambang, Adhipa Al-Ramadan, Khalid Koeshidayatullah, Ardiansyah King Fahd Univ Petr & Minerals Coll Petr Engn & Geosci Geosci Dept Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Coll Petr Engn & Geosci Ctr Integrated Petr Res Dhahran Saudi Arabia

Field-based high-resolution carbonate facies mapping is often challenging due to the limited accessible exposure, high-degree of heterogeneity, and lack of distinct natural characteristics between different lithofacies. To mitigate this issue, we proposed a novel approach by integrating multispectral remote sensing, advanced image processing techniques, and supervised classification to perform high-resolution carbonate lithofacies mapping and utilized the extensive Mesozoic carbonate in Saudi Arabia as an example. For this study, the Tuwaiq Mountain Formation (TMF) was selected not only because of its wide aerial distribution but also its importance as conventional and unconventional hydrocarbon reservoirs in the subsurface. Our proposed method was able to map and delineate different members (T1, T2, T3) and key lithofacies in the TMF. In addition, based on the spectral characteristics, the middle member of TMF (T2) can be further subdivided into two subunits (T2 -a of higher reflectance & T2 -b of lower reflectance). These findings are further corroborated by detailed microfacies analysis, which validates the presence of two sub-members of T2 (T2 -a: Spiculitic foraminiferal wackestone and T2 -b: Coralline floatstone facies). This resulted in a revised and accurate lithofacies map that made significant modifications over older maps. The overall accuracy of TMF lithofacies is 93.4 % with a kappa coefficient of 0.88. This study demonstrates that multispectral remote sensing approach are effective at distinguishing different carbonate units and providing high-resolution carbonate facies maps. The proposed approach should be applicable to other carbonate outcrops globally and could help in improving carbonate lithofacies mapping where the outcrops are not accessible.

关键词： Mesozoic Carbonates Lithofacies mapping advanced image processing Tuwaiq limestone Central Saudi Arabia

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Generation of fused visible and thermal-infrared images for uncooperative spacecraft proximity navigation

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ADVANCES IN SPACE RESEARCH 2024年第11期73卷 5501-5520页

作者： Civardi, Gaia Letizia Bechini, Michele Quirino, Matteo Colombo, Alessandro Piccinin, Margherita Lavagna, Michele Politecn Milan Dept Aerosp Sci & Technol Via La Masa 34 I-20156 Milan Italy

State-of-the-art techniques for vision-based relative navigation rely on images acquired in the visible spectral band. Consequently, the accuracy and robustness of the navigation is strongly influenced by the illumination conditions. The exploitation of thermal-infrared images for navigation purposes is studied in the present paper, as a possible solution to improve navigation in close proximity with a target spacecraft. Thermal-infrared images depend on the thermal radiation emitted by the target, hence, they are independent from light conditions;however, they suffer from a poorer texture and a lower contrast with respect to visible ones. This paper proposes pixel-level image fusion to overcome the limitations of the two types of images. The two source images are merged into a more informative one, retaining the strengths of the distinguished sensing modalities. The contribution of this work is twofold: firstly, a realistic thermal infrared images rendering tool for artificial targets is implemented;secondly, different pixel-level visible-thermal infrared images fusion techniques are assessed through qualitative and quantitative performance metrics to ease and improve the subsequent image processing step. The work presents a comprehensive evaluation of the best fusion techniques for on-board implementation, paving the way to the development of a multispectral end-to-end navigation chain. (c) 2023 COSPAR. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Sensor fusion advanced image processing Vision based navigation Thermal infrared imaging

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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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An Object Recognition Approach for Synthetic Aperture Radar images

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MOBILE NETWORKS & APPLICATIONS 2021年第3期26卷 1259-1266页

作者： Ning, Chen Liu, Wenbo Zhang, Gong Wang, Xin Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Nanjing Normal Univ Sch Phys & Technol Nanjing 210023 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 211106 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China

In this paper, an object recognition approach for synthetic aperture radar (SAR) images is addressed, which is based on the enhanced kernel sparse representation of monogenic signal. It consists of two main modules. In the first module, to capture the spatial and spectral properties of a target at the same time, a multi-scale monogenic feature extraction scheme is proposed. In the second module, an enhanced kernel sparse representation-based classifier (KSRC) is designed. Different from the traditional KSRC, in the enhanced KSRC, we first integrate the kernel principal component analysis (KPCA) as well as the kernel fisher discriminant analysis (KFDA) to generate an augmented pseudo-transformation matrix. Then, a new discriminative feature mapping approach is presented by exploiting the augmented pseudo-transformation matrix so that the dimensionality of the kernel feature space can be effectively reduced. At last, the l(1) -norm minimization is utilized to calculate the sparse coefficients for a test sample, and thus the inference can be reached in terms of the total reconstruction error. Experimental results on the public moving and stationary target acquisition and recognition dataset (MSTAR) demonstrate that the proposed method achieves high recognition accuracy for SAR automatic target recognition.

关键词： advanced image processing Target recognition Sparse representation Monogenic signal

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Improving grain size analysis using computer vision techniques and implications for grain growth kinetics

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AMERICAN MINERALOGIST 2022年第2期107卷 262-273页

作者： Ezad, Isra S. Einsle, Joshua F. Dobson, David P. Hunt, Simon A. Thomson, Andrew R. Brodholt, John P. Macquarie Univ Dept Earth & Environm Sci Sydney NSW 2109 Australia Univ Coll London UCL Dept Earth Sci Gower St London WC1E 6BT England Univ Glasgow Sch Geog & Earth Sci Glasgow G12 8QQ Lanark Scotland Univ Manchester Dept Mat Sackville St Bldg Manchester M1 3BB Lancs England

Earth's physical properties and mantle dynamics are strongly dependent on mantle grain size, shape, and orientation, but these characteristics are poorly constrained. Experimental studies provide an opportunity to simulate the grain growth kinetics of mantle aggregates. The experimentally determined grain sizes can be fit to the normal grain growth law (G(n) - G(0)(n)) = k(0)t center dot exp(-Delta H/RT) and then be used to determine grain size throughout the mantle and geological time. The grain growth dynamics of spinel-orthopyroxene mixtures in the upper mantle are modeled here by experimentally producing small grain sizes in the range of 0.5 to 2 mu m radius at pressures and temperatures equivalent to the spinel lherzolite stability field. To accurately measure the sizes of these small grains, we have developed a computer vision workflow;using a watershed transformation, which rapidly measures 68% more grains and produces a 20% improvement in the average grain size accuracy and repeatability when compared with manual methods. Using this automated approach, we have been able to identify a significant proportion of small grains, which have been overlooked when using manual methods. This additional population of grains, when fit to the normal grain growth law, highlights the influence of improved accuracy and sample size on the estimation of grain growth kinetic parameters. Our results demonstrate that automatic computer vision enables a systematic, fast, repeatable method of grain size analysis, across large data sets, improving the accuracy of experimentally determined grain growth kinetics.

关键词： Grain growth kinetics advanced image processing watershed algorithm grain size analyses

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