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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Brain tumor classification model of ResNet-50 network based on different data enhancement algorithms 8

Brain tumor classification model of ResNet-50 network based ...

引用

8th International conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Zou, Menghan Ma, Mingze Wang, Anan Li, Yujie Toe, Teoh Teik Harbin Institute of Technology Weihai School of Computer Science and Technology WeiHai China Harbin Institute of Technology Weihai Welding Technology and Engineering WeiHai China Harbin Institute of Technology Weihai Marine Information Engineering WeiHai China Harbin Institute of Technology Weihai Information Management and Information System WeiHai China Nanyang Technological University Ntu Business Ai Lab Singapore

ISBN: (纸本)9798350302455

The accuracy and stability of brain tumor MRI image classification is significant for the healthcare system, but the traditional models have the defects of difficulty in handling complex features and unstable classification. In this paper, we propose a novel brain tumor classification model based on residual neural network, and use three different data enhancement algorithms: geometric transformation, mixup, and SamplePairing to process a dataset containing four different types of brain tumor images, and finally evaluate the classification effect of our proposed model. Among them, the best results were achieved by using geometric transformations for data enhancement. © 2023 IEEE.

关键词： image classification

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The Real-Time High-Voltage Switch Position Detection Method 3

The Real-Time High-Voltage Switch Position Detection Method

引用

3rd IEEE International conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Yang, Jinming Ma, Jing Yang, Jihaoyu Luo, Weiqi Shaanxi University of Science & Technology Xi'an China

ISBN: (纸本)9798350380989

This method discloses the real-time high-voltage switch position detection method, which is implemented in accordance with the following steps: installing a coms photoelectric sensor on the video acquisition unit, and installing the video acquisition unit at a specified position on the isolation switch housing;illuminating the inside of the isolation switch through LED devices in the switch detection device, and collecting the action video of the isolation switch as the raw video signal through the video acquisition unit. The video acquisition unit collects the action video of the isolating switch as the original video signal;the coms photoelectric sensor converts the light signal in the original video signal into an electric signal, and then sends the electric signal to the video server for processing to get the RGB spatial area image;the RGB spatial area image is transmitted to the on-line monitoring backstage system, and the movement trajectory of the high-voltage switch is judged through the LK optical flow method to get the actual position of the contacts. This detection method solves the hidden problem of GIS equipment operation and maintenance that the current GIS products need manual observation and the data cannot be transmitted to the background in real time. © 2024 IEEE.

关键词： High-Voltage Switch Detection Real-time Monitoring Smart Electronics

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Possibilistic Fuzzy C-Means Clustering with region-level Information for image Segmentation 6

Possibilistic Fuzzy C-Means Clustering with region-level Inf...

引用

6th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Li, Honglei Yu, Haiyan School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

The possibilistic fuzzy c-means clustering (PFCM) algorithm is a hybrid clustering algorithm based on the fuzzy c-means clustering (FCM) algorithm. It can improve the anti-noise ability of FCM and the stability of PCM. However, there are great challenges when it is applied in image segmentation for images with strong noise injection. Therefore, we propose an improved Possibilistic Fuzzy C-Means Clustering with adaptive region-level information (RIPFCM). The first is that both the region-level information and the original information are integrated into the objective function of PFCM. The second is that we introduce a method to adaptively set the weighted factors which are used to balance the influence of the region-level information and the original information. Finally, experiments of color image segmentation show that the proposed RIPFCM method achieves better results compared with several clustering algorithms. © 2023 ACM.

关键词： image segmentation

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Prediction Of Coal And Gas Outburst Based On Support Vector Machine 2

Prediction Of Coal And Gas Outburst Based On Support Vector ...

引用

2nd International conference on Advanced algorithms and Signal image processing, AASIP 2022

作者： Yabing, M.A. School of Management Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9781510660571

Coal and gas disasters occur frequently, which not only cause casualties, but also bring economic losses. The prediction of coal and gas outburst has important research significance. In this paper, a security combination prediction model for building projects based on PCA-PSO-SVM is proposed. Principal Component Analysis was used for dimension reduction to remove the principal component with low contribution rate, PSO was used to avoid the blindness of selecting parameters of SVM manually. The average prediction accuracy of this model is 93.85%. Compared with the traditional method, the prediction accuracy and the calculation speed is faster. © 2022 SPIE.

关键词： Support vector machines

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Smart and Quick Parking Spot Detection 15

Smart and Quick Parking Spot Detection

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Veeramalla, Santhosh Kumar Gali, Rama Lakshmi Ranjeeth, M. Tara, Saikumar Hindumathi, V. Bhargava Kumar, L. Bvrit Hyderabad College of Engineering for Women Department of Ece Hyderabad India

ISBN: (纸本)9798350370249

The rapid expansion of urban areas has intensified the challenge of finding parking spaces for drivers. Intelligent parking systems emerge as a crucial solution by providing real-time detection of available spaces. While various methods exist, from basic magnetic sensors to advanced computer vision algorithms, their effectiveness can be hindered by environmental factors. Recognizing the importance of automated systems, this paper focuses on implementing an image processing approach for parking space detection. By strategically placing cameras in parking lots, the system identifies vacant spaces and transforms images to determine availability. Integrating these techniques into mobile applications could further aid drivers in locating parking spots, improving their overall experience. Ultimately, accurate and efficient parking space identification is vital for reducing congestion, enhancing air quality, and improving urban environments. © 2024 IEEE.

关键词： CCTV Computer vision image processing Parking availability Parking guidance Smart parking Sustainable cities

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An Efficient Implementation of the Threshold-based VNG Demosaicing with Reduced Calculations

An Efficient Implementation of the Threshold-based VNG Demos...

引用

2024 Smart systems Integration conference and Exhibition

作者： Alnaser, Yousef Ravichandran, Anbumani Langer, Jan Fraunhofer ENAS Chemnitz Germany TU Chemnitz Chemnitz Germany

ISBN: (纸本)9798350388787;9798350388770

Color Filter Arrays (CFA) are essential components of digital cameras and image sensors to capture the color information needed to produce full-color images from only a single image sensor per pixel. Many methods and algorithms have been proposed to recover the missing color information of CFAs. In this work, we use a simplified version of the Theshold-based Variable Number of Gradients algorithm proposed by Chang et al. to estimate the full-color information from Bayer images. We also show that the slight modification to algorithm does not effect images quality while making it more compatible with hardware. We propose an efficient implementation of the algorithm that reduces the number of calculations per pixel at the cost of increased memory resources. Our implementation targets an image processing pipeline in an FPGA platform which is short on LUTs and FF resources but has DSPs and BRAMs to spare. We buffer the absolute differences and average color components to be shared and re-used between neighboring pixels, on two levels: within the same row, and between different rows. The latter strategy reduces the number of absolute differences calculated every cycle from 32 to 4 and average color components from 32 to 6. However, the memory requirements are increased from storing 4 image rows to 18 image rows. We implement the solutions on an FPGA using high-level synthesis (HLS) and optimize it to further reduce resources.

关键词： Demosaicing Variable Number of Gradients Bayer image FPGA

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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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Research on Insulator Defect Detection Algorithm in Different Environments Based on Multi-head Attention Mechanism 4

Research on Insulator Defect Detection Algorithm in Differen...

引用

4th International conference on Electrical Engineering and Control Science, IC2ECS 2024

作者： Liang, Chengliang Ji, Chao Wang, Boya Zhou, Siyuan Xi'an Polytechnic University Xi'an China State Grid Ankang Power Supply Company Ankang China

ISBN: (纸本)9798331510107

The problem of multiple targets, occlusions, small targets, and a complex background in insulator images make it difficult for conventional image processing algorithms to guarantee detection accuracy. An insulator defect detection algorithm based on the Deep Convolution Network and Multi-head Attention Mechanism(MHAM) was proposed in this paper. Firstly, based on You Only Look Once (YOLO)-v5, the MHAM is proposed to obtain rich contextual information in insulator images and improve the accuracy of defect detection. Secondly, Multi-Scale Spatial Pyramid Pooling (MSPP) module is proposed to enhance the efficiency of targets recognition at different scales. Thirdly, the Simplify Optimal Transport Assignment (SimOTA) algorithm is used to improve the traditional Label Allocation Strategy to improve the accuracy of target box detection. Finally, the PIOU, Focal Loss, and Balanced Cross-Entropy functions are used to improve the Loss Function of the model to address the problem of unbalanced samples and to improve the accuracy of algorithm. By using insulator images of 750kV transmission lines in Qinghai to produce the datasets and compared the proposed algorithm with the current mainstream target detection algorithms. Compared with the traditional YOLO-v5, mAP of the algorithm in this paper is improved by 10.82%, the Precision is improved by 14.62%, and Recall is improved by 11.80%. © 2024 IEEE.

关键词： Gluing

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Classification of word difficulty using M-SVM algorithm 3

Classification of word difficulty using M-SVM algorithm

引用

3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Zhang, Yu Li, Ziyang Lu, Junjie Chong, Mengyue Du, Chen Zhao, Tiantian Cheng, Benmao Xi'An University of Science and Technology Xi'an China Jiangsu Key Lab of IoT Application Technology Wuxi Taihu University Wuxi China

ISBN: (纸本)9781510668522

Objective: The aim of this study is to develop a classification model based on word attributes and address the classification problem of word difficulty. Methods: We constructed word indexes for difficulty classification based on structure and usage habits and then trained the SVM classification model using the constructed database. The optimal penalty coefficient C was determined using Monte Carlo simulation with the dichotomous method to optimize the iterative process. Finally, we evaluated the model objectively using five-fold cross-validation and model comparison. Results: The experimental results showed that the highest classification accuracy of 98.4% and 82% for the training and prediction sets, respectively, was achieved when the optimal penalty coefficients were calculated to be (944, 273, 756, 1064, 671). The average classification accuracy obtained by five-fold cross-validation was 0.892. Comparison with other classification models indicated that this model improved the classification accuracy. In summary, the M-SVM model showed better robustness in solving the multi-classification problem, to some extent avoiding the overfitting phenomenon and demonstrating a better classification effect. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Classification (of information)

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APPLICATION OF UNSUPERVISED LEARNING AND image processing INTO CLASSIFICATION OF DESIGNS TO BE FABRICATED WITH ADDITIVE OR TRADITIONAL MANUFACTURING 24

APPLICATION OF UNSUPERVISED LEARNING AND IMAGE PROCESSING IN...

引用

24th International conference on Engineering Design, ICED 2023

作者： Ördek, Baris Borgianni, Yuri Free University of Bozen-Bolzano Italy

Manufacturing process (MP) selection systems require a large amount of labelled data, typically not provided as design outputs. This issue is made more severe with the continuous development of Additive Manufacturing systems, which can be increasingly used to substitute traditional manufacturing technologies. The objective of this paper is to investigate the application of image processing for classifying MPs in an unsupervised approach. To this scope, k-means and hierarchical clustering algorithms are applied to an unlabelled image dataset. The input dataset is constructed from freely accessible web databases and consists of twenty randomly selected CAD models and corresponding images of machine elements: 35% additively manufactured parts and 65% manufactured with traditional manufacturing technologies. The input images are pre-processed to have the same colour and size. The k-means and hierarchical clustering algorithms reported 65% and 60% accuracy, respectively. The algorithms show comparable performance, however, the k-means algorithm failed to predict the correct subdivisions. The research shows promising potential for MP classification and image processing applications. © The Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

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