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Novel segmentation algorithm for jacquard patterns based on multi-view image fusion

IET image PROCESSING 2020年第17期14卷 4563-4570页

作者： Wang, Wenzhen Deng, Na Xin, Binjie Wang, Yiliang Lu, Shuaigang Shanghai Univ Engn Sci Sch Elect & Elect Engn Longteng Rd Shanghai Peoples R China Shanghai Univ Engn Sci Sch Text & Fash Longteng Rd Shanghai Peoples R China

Pattern regeneration is one of the applications of reverse engineering technology in the textile field, which realises the process of textile-pattern regeneration-textile, and fundamentally provides an intelligent design means of textile. At present, the method of pattern regeneration in the jacquard fabric is to use the image segmentation algorithm to segment the image digitalised by unidirectional imaging, and then the segmented pattern could be identified to regeneration for the design of new fabrics. However, due to the concave and convex pattern textures on the surface of jacquard fabric, the traditional unidirectional imaging method cannot be used for the full characterisation of its structural information, resulting in unsatisfactory pattern segmentation effect. To solve this problem, a novel segmentation algorithm for jacquard patterns based on multi-view image fusion was proposed in this study. Based on multi-view image acquisition and fusion, the pattern image of jacquard fabric could be cluster-segmented by extracting the complete texture information of the fused image and the actual colour information of the calibrated image. Compared with the traditional unidirectional imaging method, the experimental results show that the enhanced texture information of the fused image is more workable for the pattern segmentation, it validates the effectiveness of the proposed method.

关键词： fabrics image texture image fusion image segmentation image colour analysis reverse engineering pattern image jacquard fabric cluster‐segmented complete texture information fused image calibrated image traditional unidirectional imaging method novel segmentation algorithm jacquard patterns multiview image fusion reverse engineering technology textile field textile‐pattern regeneration‐textile image segmentation algorithm segmented pattern convex pattern textures unsatisfactory pattern segmentation effect

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Comparative analysis of surface morphology characteristics of abrasive belt wear in conventional and creep-feed grindings based on the Yolov8 algorithm

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WEAR 2025年 574卷

作者： Zhang, Youdong Li, Duo Ren, Mingjun Zhu, Limin He, Yi Zhang, Xinquan Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat 800 Dongchuan Rd Shanghai 200240 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn 174 Shazhengjie St Chongqing 400044 Peoples R China

Creep-feed belts grinding (CFBG) can improve processing efficiency while ensuring the surface quality of TC4 titanium alloy materials. However, the difficult-to-machine characteristics of titanium alloys and the single-layer abrasive nature of the abrasive belts accelerate belt wear. Meanwhile, grinding creates micron-level scratches on the surface, which can lead to localized stress concentration on the titanium alloy surface and significantly reduce the service performance of workpiece. Due to the randomness of the grain and the complexity of grinding process, which makes it a challenging problem to investigate the regularity and mechanism of the surface morphology characteristics with belt wear under different grinding forms. Therefore, this research proposes a surface topography evaluation method of titanium alloy based on YOLOv8 segmentation algorithm, and explores the regularity of surface topography after creep-feed/conventional abrasive belt (CBG) wear. Firstly, the YOLOv8 algorithm was used to segment the collected titanium alloy surface topography images. Accuracy of the algorithm is 0.85, Precision, Recall and F1 values are 0.85, 0.72, F1 and 0.78 respectively, which indicates that the algorithm performs relatively well. Secondly, the surface morphology characterization regularity was quantitatively characterized;with belt wear, the area ratio, perimeter ratio and depth of scratches in CBG gradually decreased, and the aspect ratio, fractal dimension and radius of curvature gradually increased;while in CFBG, the parameters all changed rapidly (decreasing/increasing) and the value then fluctuated within a range. Finally, a model of titanium alloy surface morphology molding in the whole life cycle of abrasive belts under different grinding methods was established.

关键词： image segmentation algorithm Titanium alloy belt grinding Creep-feed grinding Belt wear Surface topography characterization

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Rendered image denoising method with filtering guided by lighting information

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Optoelectronics Letters 2025年第4期21卷 242-248页

作者： MA Minghui HU Xiaojuan ZHANG Ripei CHEN Chunyi YU Haiyang School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China

The visual noise of each light intensity area is different when the image is drawn by Monte Carlo ***,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed *** we propose a rendered image denoising method with filtering guided by lighting ***,we design an image segmentation algorithm based on lighting information to segment the image into different illumination ***,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination *** different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area ***,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the *** the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on *** shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.

关键词： establish paramet rendered image denoising Monte Carlo method filtering guided lighting information denoising algorithms image segmentation algorithm rendered image denoising method monte carlo methodhoweverthe

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Evaluation of Neonatal Cerebral Circulation Under Hypoxic Ischemic Risk Factors Based on Quantitative Analysis of Cerebral Veins with Magnetic Resonance Susceptibility Weighted Imaging

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CLINICAL NEURORADIOLOGY 2024年第4期34卷 859-869页

作者： Xie, Qi Liao, Yan-Hui He, Wen-juan Han, Peng-peng Wu, Jun South China Univ Technol Guangzhou Peoples Hosp 1 Sch Med Med Imaging Dept Nansha Guangzhou 511457 Peoples R China Inst Software Applicat Technol Guangzhou 511458 Peoples R China Meizhou Peoples Hosp Dept Nucl Med Meizhou 514031 Peoples R China

Purpose To observe the regulation of cerebral circulation in vivo based on image segmentation algorithms for deep learning in medical imaging to automatically detect and quantify the neonatal deep medullary veins (DMVs) on susceptibility weighted imaging (SWI) images. To evaluate early cerebral circulation self-rescue for neonates undergoing risk of cerebral hypoxia-ischaemia in vivo. Methods SWI images and clinical data of 317 neonates with or without risk of cerebral hypoxia-ischaemia were analyzed. Quantitative parameters showing the number, width, and curvature of DMVs were obtained using an image segmentation algorithm. Results The number of DMVs was greater in males than in females (p < 0.01), and in term than in preterm infants (p = 0.001). The width of DMVs was greater in term than in preterm infants (p < 0.01), in low-risk than in high-risk group (p < 0.01), and in neonates without intracranial extracerebral haemorrhage (ICECH) than with ICECH (p < 0.05). The curvature of DMVs was greater in term than in preterm infants (P < 0.05). The width of both bilateral thalamic veins and anterior caudate nucleus veins were positively correlated with the number of DMVs;the width of bilateral thalamic veins was positively correlated with the width of DMVs. Conclusion The DMVs quantification based on image segmentation algorithm may provide more detailed and stable quantitative information in neonate. SWI vein quantification may be an observable indicator for in vivo assessment of cerebral circulation self-regulation in neonatal hypoxic-ischemic brain injury.

关键词： MRI SWI Neonatal Hypoxic-Ischemia DMVs image segmentation algorithm

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Anomaly monitoring and early warning of electric moped charging device with infrared image

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Optoelectronics Letters 2025年第3期21卷 136-141页

作者： LI Jiamin HAN Bo JIANG Mingshun School of Control Science and Engineering Shandong UniversityJinan250002China

Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and *** detection methods have limitations in terms of real-time performance and monitoring *** address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.

关键词： detection methods divide image anomaly monitoring temperature detection median filtering algorithm infrared image processing image segmentation algorithm electric moped charging devicessuch

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Comparative Study On segmentation Methods Of Fundus images 12

Comparative Study On Segmentation Methods Of Fundus Images

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Cao, Juan Liu, JinJia Chongqing Jiaotong Univ Sch Informat Chongqing 400074 Peoples R China

ISBN: (纸本)9798350321050

As a traditional image segmentation method, image segmentation methods based on threshold, edge detection, and region have some differences in the effect of fundus image segmentation. In this paper, the watershed algorithm, Otsu, and Canny operator edge detection algorithms are selected for comparative study. By comparing the segmentation performance of two CHASEDB1 datasets and DRIVE datasets of fundus images, the experimental results showed that the Otsu had the highest segmentation accuracy and best segmentation effect on fundus images.

关键词： image segmentation algorithm Comparative Analysis

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Visualization and Quantification of Broken Wires in Steel Wire Ropes Based on Induction Thermography

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IEEE SENSORS JOURNAL 2021年第17期21卷 18497-18503页

作者： Xia, Hui Yan, Rong Wu, Jianbo He, Sha Zhang, Muchao Qiu, Qiao Zhu, Junzhen Wang, Jie Sichuan Univ Sch Mech Engn Chengdu 610065 Peoples R China CNPC Chuanqing Drilling & Exploration Corp Safety Environm Qual Surveillance & Inspect Res I Guanghan 618300 Peoples R China Army Acad Armored Forces Dept Vehicle Engn Beijing 10072 Peoples R China

Wire ropes (WRs) are widely used in various areas, such as tourist cables, mining, and elevators. Thus, it is very important to detect broken wires (BWs) to prevent safety issues. To guarantee the structural health of WRs, this paper proposes the use of induction thermography (IT) to detect the BWs. An image segmentation algorithm based on scale morphology is proposed to eliminate the background influence of non-uniform heating and visualize the BWs. Further, the area of the BW region, after the image binarization, subtraction, and filtering, is extracted as a feature to quantify the number of BWs. Results show that the proposed methods can effectively visualize and quantify the broken region within 1 BWs.

关键词： Visualization Wires Sensors Heating systems Cameras Spirals Safety Broken wires induction thermography image segmentation algorithm visualization binarization quantification

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Automation of green roof plant cover measurements using machine learning and a comparison of digital and thermal imaging techniques

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APPLIED VEGETATION SCIENCE 2024年第2期27卷

作者： Cao, Ronglan Maclvor, J. Scott Univ Toronto Scarborough Dept Biol Sci 1265 Mil Trail Toronto ON M1C 1A4 Canada

Aims: Post-analyses of digital red, green, blue (RGB) and thermal images have become increasingly popular as modern approaches to plant cover analysis. image analyses are often coupled with semi-automated or automated workflows to reduce the amount of human labor input compared with traditional manual procedures. This study aims to evaluate and compare different image segmentation methods for plant cover analysis using digital RGB and thermal images, focusing on the effectiveness of semi-automated and manual segmentation techniques in monitoring plant cover on green roofs. Location: An Extensive green roof in the City of Toronto. Methods: We surveyed the plant cover of an extensive green roof using digital and thermal imagery. The plant cover values were obtained using three methods: traditional manual segmentation based on a visual examination (MS), imageJ Color Threshold (CT) and Trainable Weka segmentation (TWS), all performed within FIJI (a distribution of imageJ). Manual segmentation based on visual examination was used as a reference standard. Results: Significant correlation was found between the cover estimation using the CT and TWS methods relative to MS, and between cover estimation using the thermal image and the RGB image. TWS overestimated plant cover on thermal images while producing an underestimation on RGB images. CT demonstrated a performance closer to MS than TWS, indicating that manually customized methods produced results more aligned with MS. The estimated cover values by MS were not significantly affected by the image type (digital RGB or thermal). Conclusions: Results suggest that RGB and thermal imaging techniques may provide complementary results and reveal unique information regarding the functioning of green roofs. The accuracy of supervised machine-learning methods could be enhanced with site-specific data to provide a more accurate and efficient estimation of plant cover, which might be beneficial for long-term studies on green roofs an

关键词： green infrastructure image segmentation algorithm imageJ microclimate cooling plant cover Sedum thermal imaging urban agriculture

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Establishing an objective biomarker for corneal cystinosis using a threshold-based Spectral domain optical coherence tomography imaging algorithm

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ACTA OPHTHALMOLOGICA 2021年第2期99卷 E189-E195页

作者： Keidel, Leonie Elhardt, Carolin Hohenfellner, Katharina Priglinger, Siegfried Schworm, Benedikt Wertheimer, Christian Priglinger, Claudia Luft, Nikolaus Ludwig Maximilians Univ Munchen Dept Ophthalmol Mathildenstr 8 D-80336 Munich Germany RoMed Klinikum Rosenhe Dept Pediat Nephrol Rosenheim Germany

Purpose The purpose of the present study was to establish a semi-automated threshold-based image segmentation algorithm to detect and objectively quantify corneal cystine crystal deposition in ocular cystinosis with anterior segment optical coherence tomography (AS-OCT). Methods This prospective, observational, comparative study included 88 eyes of 45 patients from the German Cystinosis Registry Study as well as 68 eyes of 35 healthy control subjects. All eyes were imaged with AS-OCT (Cirrus HD-OCT 5000, Carl Zeiss Meditec AG, Jena, Germany). As an initial step, B-scan images were subjectively analysed for typical changes in morphology in comparison to healthy controls. Based on the experience gained, an objective semi-automated B-scan image segmentation algorithm was developed using a grey scale value-based threshold method to automatically quantify corneal crystals. Results On AS-OCT B-scans, corneal crystals appeared as hyperreflective deposits within the corneal stroma. The crystals were distributed either in all stromal layers (43 eyes, 49%) or confined to the anterior (23 eyes, 26%) or posterior stroma (22 eyes, 25%), respectively. The novel automatic B-scan image segmentation algorithm was most efficient in delineating corneal crystals at higher grey scale thresholds (e.g. 226 of a maximum of 255). Significant differences in suprathreshold grey scale pixels were observable between cystinosis patients and healthy controls (p < 0.001). In addition, the algorithm was able to detect an age-dependent depth distribution profile of crystal deposition. Conclusion Objective quantification of corneal cystine crystal deposition is feasible with AS-OCT and can serve as a novel biomarker for ocular disease control and topical treatment monitoring.

关键词： biomarker cornea cystine crystals cystinosis greyscale image segmentation algorithm optical coherence tomography

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A Hybrid algorithm of Otsu and Adaptive Region for image segmentation

A Hybrid Algorithm of Otsu and Adaptive Region for Image Seg...

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IEEE International Conference on Power, Intelligent Computing and Systems

作者： Luo Chunlei Sha Hao Wang Hu He Yi College of Mechanical and Electrical Engineering Central South University

ISBN: (纸本)9781728137216

The traditional Region Growing (RG) algorithm is a semi-automatic image segmentation algorithm that requires manual selection of seed points, manual setting of thresholds, which may cause the cavity and over-segmentation while deals with the uneven and undivided image for noise and grayscale. Therefore, the classical Otsu method is adopted to find the optimal threshold in HSV space to obtain the initial binarized segmentation image. Then, the Adaptive Region Growing (ARG) algorithm is applied to find the image edge. To be specific, the initial seed point is confirmed automatically through histogram firstly. Secondly, the threshold of growth condition is determined according to the average similarity of image. Finally, the image is grown into the contour edges. This image is mixed together with the initial Otsu segmentation image can obtain the final processed image. Experimental results show that the proposed algorithm has strong anti-interference, which can effectively reduce the mis-segmentation rate.

关键词： image segmentation Machine vision Obstacle detective RG algorithm Otsu algorithm image segmentation robot vision image images imagery (Psychotherapy) edge image suppression of interference region growing image segmentation algorithm algorithms

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