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检索条件"任意字段=Iranian Conference on Machine Vision and Image Processing"

共 11454 条记录，以下是51-60 订阅

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Research on image Recognition Methods based on Deep Learning 2

Research on Image Recognition Methods based on Deep Learning

引用

2nd International conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Qin, Chunbo Cui, Xuewen School of Electrical and Information Engineering Heilongjiang Institute of Technology Heilongjiang China Center for Faculty Development Heilongjiang Institute of Technology Heilongjiang China

In this paper, on the basis of a brief description of deep learning theory, we study the use of image information recognition technology to detect image data, construct training models, and improve the accuracy of ima... 详细信息

ISBN: (纸本)9798331543037

关键词： Adversarial machine learning

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image Registration Based on Redundant Keypoint Elimination SARSIFT Algorithm and MROGH Descriptor 12

Image Registration Based on Redundant Keypoint Elimination S...

引用

12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Hossein-Nejad, Zahra Nasri, Mehdi Islamic Azad University Sirjan Brnach Department of Electrical Engineering Sirjan Iran Islamic Azad University Khomeinishahr Branch Department of Electrical Engineering Isfahan Iran

ISBN: (纸本)9781665412162

In this article, a new approach is suggested in remote-sensing images registration. In the suggested approach, first, the features extraction process is done based on proposed redundant keypoint elimination method synthetic aperture radar-SIFT (RKEM-SARSIFT). Second, creating descriptors is based on the Multi-Support Region Order-Based Gradient Histogram (MROGH) algorithm. Finally, matching process is done based on nearest neighbor distance ratio (NNDR) and transformation model is done based affine transform. The simulation results on several remote sensing image datasets affirm the suggested approach advantage in comparison with some other basic registration methods in terms of precision matching, SITMMR and SITMMC. © 2022 IEEE.

关键词： image registration

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On-machine Wear Measurement for Milling Cutter Based on machine vision 5

On-machine Wear Measurement for Milling Cutter Based on Mach...

引用

5th International conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Yu, Jiarui Zan, Tao Liu, Weibo Li, Yikun Peng, Junxi Lei, Qichang Beijing Univ Technol Coll Mech & Energy Engn Beijing Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Beijing Univ Technol Coll Beijing Dublin Int Beijing Peoples R China

ISBN: (纸本)9798350363272;9798350363265

As a key factor in the milling process, the wear status of the milling cutter has a significant impact on the machining quality of the workpiece. To detect wear on a milling machine efficiently and precisely, this paper presents the development of a milling machine wear detection system based on machine vision and digital image processing. The system including link mechanisms and industrial camera is designed for auxiliary localization and collection of on-machine images of milling cutter status. The image preprocessing method based on automatic threshold segmentation and Canny edge detection operator is proposed to identify the edge of cutter wear. The Maximum connected domains algorithm is used to screen the wear area of the milling cutter and the amount of wear is obtained based on a calibrated scaling method. Experimental results show that the proposed system is suitable for industrial use due to its rapid detection speed and strong recognition accuracy, which are desirable for engineering applications.

关键词： milling cutter wear detection machine vision image processing auxiliary localization mechanism

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Retrofitting a Legacy Cutlery Washing machine Using Computer vision 16th

Retrofitting a Legacy Cutlery Washing Machine Using Computer...

引用

16th International conference on Computational Collective Intelligence (ICCCI)

作者： Fwa, Hua Leong Singapore Management Univ 81 Victoria St Singapore 188065 Singapore

ISBN: (纸本)9783031702587;9783031702594

Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed and utilization. With the existing operational constraints of our client, frequent re-training of the deep learning model for object detection is not feasible. Thus, we compared the generalizability of the two techniques across 'unseen' cutleries and found traditional image processing to be generalizable across 'unseen' images. Our proposed final solution uses traditional image processing for computation of utilization but a hybrid of traditional image processing and deep learning model for speed computation as it is more reliable. Our client has implemented our proposed solution for one conveyor belt-based cutlery washing machine and will be planning to scale this to multiple conveyor belt-based cutlery washing machines.

关键词： Industry 4.0 Computer vision Deep Learning image processing

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HYBRID SINGLE INPUT AND MULTIPLE OUTPUT METHOD FOR COMPRESSING FEATURES TOWARDS machine vision TASKS 31

HYBRID SINGLE INPUT AND MULTIPLE OUTPUT METHOD FOR COMPRESSI...

引用

2024 International conference on image processing

作者： Zhang, Zifu Li, Shengxi Liu, Tie Xu, Mai Xu, Tao Guan, Zhenyu Lv, Zhuoyi Beihang Univ Beijing Peoples R China Vivo Mobile Commun Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

With the advance of deep learning in the BigData era, image/video coding for machines (VCM) as called for proposals by the moving picture experts group (MPEG) now becomes the pivotal technique for extensive intelligent vision tasks. However, existing VCM methods typically focus on compressing features independently at each scale, ignoring the redundancy of features across multiple scales. This paper thus introduces a simple yet effective architecture called hybrid single input and multiple output (H-SIMO) for VCM, which can significantly reduce the redundancy across scales of features. More specifically, as the pyramid structure is commonly employed for localising multi-scale objects, our HSIMO method proposes to compress all features by inputting a single-scale feature while retaining the ability to decompress all the features. Moreover, an entropy model is seamlessly integrated into the training process to efficiently reduce the statistical redundancy of features. During the testing phase, the hybrid coding method, in conjunction with the versatile video coding (VVC), is employed to compress the features from both images and videos. We comprehensively evaluate the performance of our H-SIMO method in two standard machine vision tasks: object detection and instance segmentation, in which the experimental results verify the superior performances of our H-SIMO method.

关键词： Video coding for machines feature compression image compression

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Flexible and Transportable Illumination Control System for machine vision Applications 10th

Flexible and Transportable Illumination Control System for M...

引用

International conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent Systems

作者： Gusic, Muhamed Sokic, Emir Univ Sarajevo Dept Automat Control & Elect Fac Elect Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

During the development of machine vision setups for industrial applications, namely automatic visual inspection systems, the type of required lighting and correct illumination position and intensity are often not known in advance. In order to solve a specific machine vision task, an engineer needs to propose and implement a suitable image processing algorithm and design the accompanying lighting system. Testing different illumination concepts, especially "in the field", using off-the-shelf lighting systems is time consuming. The paper presents a custom-made, cost effective, transportable and flexible system which may be used for testing different illumination techniques and simultaneously control multiple lighting modules. In addition to the hardware part, the developed software used for control the system is presented in the paper. The proposed device usefulness is demonstrated by solving a suitable multi-light digital image processing task.

关键词： machine vision visual inspection illumination control system embedded systems digital image processing

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Novel Civil Engineering Construction Site Safety Monitoring and Warning System based on machine vision 3

Novel Civil Engineering Construction Site Safety Monitoring ...

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Zhang, Qiankun Cheng, Qibin Zhejiang Construction Investment Innovation Technology Co. Ltd. Zhejiang Hangzhou310000 China Zhejiang Construction Engineering Group Co. Ltd. Zhejiang Hangzhou310013 China

ISBN: (纸本)9798331509675

machine vision is widely used in the engineering field, especially in the intelligent monitoring, safety warning and information recognition scenarios. In this study, the novel civil engineering construction site safety monitoring and warning system based on machine vision is proposed. As a pioneering research, this system realizes all-weather real-time security monitoring by combining the daytime and nighttime image processing technology. In daytime image processing scenarios, this study proposes a new image de-hazing algorithm. This algorithm models the imaging process by improving the atmospheric scattering model. At the same time, the guided filtering algorithm is used for optimization, which significantly improves the image clarity in low-visibility environments. In the night image processing scenario, this study uses infrared imaging technology and filtering algorithms for image enhancement. This operation can effectively solve the challenges of image noise and detail loss in low-light environments. As the core of the model, this study proposes a safety warning framework based on image anomaly detection to meet the special needs of construction sites. This innovative architecture includes human posture rotation detection and image color deviation anomaly recognition. Through such operations, automatic recognition and warning of dangerous behaviors are achieved. Experimental results prove the effectiveness of the recognition algorithm. © 2025 IEEE.

关键词： machine vision

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ON ANNOTATION-FREE OPTIMIZATION OF VIDEO CODING FOR machineS 31

ON ANNOTATION-FREE OPTIMIZATION OF VIDEO CODING FOR MACHINES

引用

2024 International conference on image processing

作者： Windsheimer, Marc Brand, Fabian Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9798350349405;9798350349399

Today, image and video data is not only viewed by humans, but also automatically analyzed by computer vision algorithms. However, current coding standards are optimized for human perception. Emerging from this, research on video coding for machines tries to develop coding methods designed for machines as information sink. Since many of these algorithms are based on neural networks, most proposals for video coding for machines build upon neural compression. So far, optimizing the compression by applying the task loss of the analysis network, for which ground truth data is needed, is achieving the best coding performance. But ground truth data is difficult to obtain and thus an optimization without ground truth is preferred. In this paper, we present an annotation-free optimization strategy for video coding for machines. We measure the distortion by calculating the task loss of the analysis network. Therefore, the predictions on the compressed image are compared with the predictions on the original image, instead of the ground truth data. Our results show that this strategy can even outperform training with ground truth data with rate savings of up to 7.5 %. By using the non-annotated training data, the rate gains can be further increased up to 8.2 %.

关键词： Video Coding for machines Neural Network Compression Computer vision Learned image Coding machine-to-machine Communication

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An AI-Aided Contour Detection Experimental Platform for Quality Inspection by machine vision 21

An AI-Aided Contour Detection Experimental Platform for Qual...

引用

21st International Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2024

作者： Zhou, Rongpeng Luo, Yabo Huang, Sui Zhang, Feng School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan430070 China Hubei tianwei automobile Co. LED. Shijiazhuang Suizhou City441300 China

ISBN: (纸本)9798331519254

With the development and widespread application of machine vision technology, quality inspection based on machine vision has also become a popular research area in the field of statistical quality control. However, research on machine vision in quality inspection mainly focuses on the surface quality of products, and there is still very little research on the detection of contour quality. Effective high-quality image acquisition technology is the foundation for building a machine vision based quality inspection experimental platform. This study determined the optimal light source color for a quality inspection experimental platform based on machine vision through theory and experiment, and proposed a method to avoid image dragging and deformation in dynamic sampling. Based on the image acquisition technology proposed above, a machine vision based quality inspection experimental platform has been constructed, which includes five modules: material transmission module, image acquisition module, defect removal module, image processing module, and human-computer interaction module. It will provide strong instrument support for machine vision based product contour quality inspection. © 2024 IEEE.

关键词： machine vision

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Evaluation of the image processing Technique in Interpretation of Polar Plot Characteristics of Transformer Frequency Response 12

Evaluation of the Image Processing Technique in Interpretati...

引用

12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Vosoughi, Ahmad Samimi, Mohammad Hamed University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran

ISBN: (纸本)9781665412162

Frequency response analysis (FRA) is one of the most efficient methods that can diagnose the mechanical faults of power transformers. Digital image processing of the FRA polar plot characteristics has been recently proposed in the literature for the interpretation of power transformers Frequency response. The important advantage of this method is using the phase angle of the FRA trace in addition to its amplitude for the analysis. The digital image processing techniques implemented on the FRA polar plot detect the fault by extracting and analyzing different features of the image by using texture analysis. In this study, the performance of this method is investigated on real windings to examine its ability in detecting the fault extent and type. This step is mandatory since the method is new in the FRA field and has been investigated only in simulation cases. Three different faults, including axial displacement, disk space variation, and radial deformation, are implemented in the experimental setup for the study. Results of implementation of this approach show that this approach neither can determine the fault extent nor the fault type. Therefore, essential changes need to be implemented in the method before applying it in the field. © 2022 IEEE.

关键词： Frequency response

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