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检索条件"任意字段=Advances in Image Processing and Pattern Recognition, Proceedings of the International Conference."

共 8561 条记录，以下是121-130 订阅

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Generate Vector Graphics of Fine-grained pattern based on the Xception Edge Detection 4

Generate Vector Graphics of Fine-grained Pattern based on th...

引用

4th international conference.on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Chen, Anqi Chen, Hao Peng, Yicui Hu, Jinrong Deng, Linjie Wen, Xiang Wang, Xin Chengdu Technological University Chengdu China Chengdu University of Information Technology Chengdu China Industry Research Institute Chengdu China University at Buffalo United States

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

The innovative generation of vector graphics with fine-grained images using Artificial Intelligence has become an important task in edge extraction. In this paper, we take Qiang embroidery image as an example due to its containing fine-grained edges, which is more suitable for the study of image processing and pattern recognition. We firstly adopt appropriate pre-processing methods, improved adaptive median filtering (IAMF) for the image to reduce image noise. Then, the Xception based on convolutional neural networks is used for edge detection and extraction. Results show that Qiang embroidery images, after denoising and edge extraction, can be clearly identified the shape characteristics of the images. Based on this approach, it can be converted into vector graphics for digital preservation and further artistic reinterpretation. The use of the Xception effectively addresses Qiang embroidery extraction in two-dimensional vector images, offering a practical reference for preserving related intangible cultural heritage. © 2025 SPIE.

关键词： Median filters

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Automatic Detection Model of Food Microbial Contamination Combined with image processing and pattern recognition

Automatic Detection Model of Food Microbial Contamination Co...

引用

2024 international conference.on Telecommunications and Power Electronics, TELEPE 2024

作者： Wang, Wei Ruan, Yanchun Lin, Mei Bu, Ruichen College of Cuisine Science and Technology Jiangsu Vocational College of Tourism Jiangsu Yangzhou225100 China College of Food Science and Engineering Yangzhou University Jiangsu Yangzhou225100 China

ISBN: (纸本)9798350369212

Microbial contamination of food is one of the important factors affecting food safety, and it is of great significance to detect microbial contamination of food quickly and accurately for ensuring public health. The purpose of this study is to develop an automatic detection model of food microbial contamination combining image processing and pattern recognition, so as to improve the detection efficiency and accuracy of food microbial contamination. First of all, the paper constructs an image data set containing different categories of food microorganisms, and preprocesses the image data, including denoising, enhancement and other operations to improve the image quality. Then, using deep learning technology and pre-trained convolutional neural network (CNN) to extract features, the high-level abstract features of the image are obtained. Then, a new deep feature fusion classifier (DFFC) is constructed by combining the traditional machine learning method support vector machine (SVM) with deep learning features, which is used for automatic detection of food microbial contamination. In the experiment, the DFFC model is evaluated in detail and compared with the model using CNN features or traditional machine learning methods alone. The experimental results show that DFFC model is superior to other models in Accuracy, Recall, Precision and F1 Score, and achieves 92% Accuracy, 89% Recall, 94% Precision and 91 % F1 score. This shows that DFFC model can effectively identify food microbial contamination, and has high classification accuracy and robustness. The DFFC model proposed in this study provides a reliable solution for the automatic detection of food microbial contamination, and is expected to play an important role in food safety monitoring and management. © 2024 IEEE.

关键词： Automatic detection food microbial contamination image processing pattern recognition

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Startup Algorithm of Pulse Deinterleaving Based on RNN Model

Startup Algorithm of Pulse Deinterleaving Based on RNN Model

引用

2023 international conference.on image, Signal processing, and pattern recognition, ISPP 2023

作者： Yuan, Shuo Zhu, Li-Wen Liu, Zhang-Meng National University of Defense and Technology Hunan Changsha430073 China

ISBN: (纸本)9781510666351

Accurate and timely pulse sorting is the basis of passive signal processing and the common pursuit of many works in this field. This paper improves the previous method of deinterleaving pulses using trained RNN models and proposes a paragraph-startup algorithm, which effectively improves the success rate and timeliness of deinterleaving. © 2023 SPIE.

关键词： Signal processing

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image retrieval method based on k-means block

Image retrieval method based on k-means block

引用

2023 international conference.on image, Signal processing, and pattern recognition, ISPP 2023

作者： Deng, GuanNan Mu, Lu Ma, Zhe Zhang, Mei School of Science Northeast Electric Power University Jilin Jilin China

ISBN: (纸本)9781510666351

In order to improve the ability to describe image information, this paper proposes an image retrieval method based on k-means block. Firstly, under Lab color space, the image is divided into 6 sub-regions using k-means algorithm. And the external rectangle determined by each sub-region is segmented on the original image to obtain 6 sub-blocks that can contain the main information of the image. Then, the color features of each sub-block are extracted separately. Finally, the degree of similarity is reflected by the Euclidean distance between the image feature vectors. The simulation experimental results show that the proposed algorithm in this paper has been improved compared with the comparison scheme. © 2023 SPIE.

关键词： image retrieval

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Research on anomaly detection technology of IoT mobile terminal based on watermark detection

Research on anomaly detection technology of IoT mobile termi...

引用

2023 international conference.on Computer Application and Information Security, ICCAIS 2023

作者： Dai, Dawei Liu, Rui Liu, Yong Tian, Zheng Chen, Zhongwei Sun, Yizhen State Grid Information & Communication Company of Hunan Electric Power Corporation Hunan Changsha410118 China Hunan Provincial Key Laboratory of Internet of Things in Electricity Hunan Changsha410114 China

ISBN: (纸本)9781510675032

The watermark detection method under Android system combines the technology of computer vision, image processing and pattern matching, aiming to provide an effective and automatic watermark detection solution. Through image conversion of input files and pre-processing operations such as filtering, contrast enhancement and brightness adjustment, the watermark features of files in different formats are enhanced. Feature extraction algorithm is adopted to extract features such as the number and hierarchical relationship of image contouring from pre-processed images, and image features are further processed by fitting algorithm. Finally, it is compared and matched with the predefined watermark pattern library. By combining the image recognition algorithm with Android components, the system realizes the watermark detection function in Android applications. © 2024 SPIE.

关键词： pattern matching

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Wide Line Segments Detection in Grey-Level images via Guided Scale Space Radon Transform 6

Wide Line Segments Detection in Grey-Level Images via Guided...

引用

6th IEEE international conference.on image processing, Applications and Systems, IPAS 2025

作者： Goumeidane, Aicha Baya Ziou, Djemel Nacereddine, Nafaa Yala, Nawal CRTI Research Center in Industrial Technologies Algiers Algeria Université de Sherbrooke Département D'Informatique Québec Canada

ISBN: (纸本)9798331506520

Line segment detection is a fundamental procedure in computer vision, pattern recognition, and image analysis applications. The paper proposes a novel method for wide line segment detection especially endpoints determination based on the Guided Scale Space Radon Transform and Hessian orientations. The method begins by determining the centerlines of wide lines and then exploit the image Hessian orientations around these lines to define binary region support of the line segments and then detect endpoints. The method shows to be robust against blur and noise on synthetic images where, the evaluation of the outcomes reveals the correctness of the detection by achieving low errors. In addition, results on real images are very promising. © 2025 IEEE.

关键词： image segmentation

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Interactive Segmentation of Art images based on Point Cloud Pre-Training 3

Interactive Segmentation of Art Images based on Point Cloud ...

引用

3rd international conference.on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Tian, Yuan Tao, Yiran Nanhang Jincheng College Jiangsu Province Nanjing City210000 China

ISBN: (纸本)9798350396157

This study presents an innovative interactive segmentation algorithm for fine art images using point cloud pre-Training principles. With increasing demands for precision and variety in image processing, conventional methods are proving inadequate. image segmentation, a central aspect of visual data manipulation, involves dividing an image into distinct regions based on static or dynamic features. This study reviews supervised and unsupervised segmentation strategies, emphasizing the importance of fuzzy clustering algorithms in practical applications. Despite advances in medical image segmentation, challenges remain due to the complex nature of image processing. A universally applicable segmentation method and standardized evaluation criteria are elusive. To address these challenges, the proposed algorithm combines Local Binary pattern (LBP) feature extraction with a point cloud pre-Training model, utilizing the U-net architecture for accurate segmentation. Experiments conducted on an Ubuntu 14.04 64-bit OS using C++ and Python demonstrate the effectiveness of the algorithm. The visualized results show its potential for practical applications and lay the foundation for future advances in image segmentation. © 2024 IEEE.

关键词： image segmentation

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Artificial Intelligence and image processing Techniques for Blood Group Prediction 5

Artificial Intelligence and Image Processing Techniques for ...

引用

5th IEEE international conference.on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Tannmay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

The classification and prediction of blood group is most important aspect for the transfusion of blood. In present situations, they are done in laboratory using manual process. This is a time-consuming process and hence need manual energy. To overcome the constraints in the prediction of conventional methods in blood group, the artificial intelligence is implemented. This includes the image processing techniques with segmentation process to detect the classification of blood group. They are done through MATLAB simulations to detect the blood components. Through collecting the blood samples and processing and classified the images with feature extraction leads to govern the variety of blood based on ABO and Rh group systems. To overcome the drawbacks in the conventional process, the developed methodology is implemented. This reduces various manual errors. Thus, the image processing technique with artificial intelligence helps to determine the classification of blood rapidly without any errors. © 2024 IEEE.

关键词： artificial intelligence blood group prediction feature extraction image processing pattern recognition segmentation Transfusion

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Deep Learning Technique for image-Based Bladder Cancer Diagnosis: A Comprehensive Review 2

Deep Learning Technique for Image-Based Bladder Cancer Diagn...

引用

2nd international conference.on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Reena, R. Shanthi, S Amala Noorul Islam Centre for Higher Education Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331505745

Bladder cancer represents a significant global health challenge. Recent advances in deep learning (DL) have shown promise in enhancing bladder cancer (BC) detection and diagnosis through improved image analysis and pattern recognition capabilities. This review emphasizes current research on the application of DL methods in BC detection, emphasizing the diverse technique, dataset, and performance metrics employed across studies. DL, particularly convolutional neural network (CNN), has been extensively utilized for processing medical data such as CT, MRI, and cystoscopy images. These models have demonstrated high accuracy in identifying bladder tumors, often surpassing traditional machine learning (ML) approaches. Key methodologies reviewed include data preprocessing techniques, model architectures, and strategies for addressing class imbalances, which are critical in medical datasets. The review further discusses the importance of external validation and the integration of advanced techniques like transfer learning and hybrid models to bolster performance. Future research directions focus on overcoming current limitations by leveraging multi-modal data, improving interpretability, and refining model training processes to ensure reliable clinical application. © 2025 IEEE.

关键词： Convolutional neural networks

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Implementation of Parallel Evolutionary Convolutional Neural Network for Classification in Human Activity and image recognition 22nd

Implementation of Parallel Evolutionary Convolutional Neural...

引用

22nd Mexican international conference.on Artificial Intelligence (MICAI)

作者： Villegas-Cortez, Juan Roman-Alonso, Graciela Fernandez De Vega, Francisco Flores-Morales, Yafte Aaron Cordero-Sanchez, Salomon Univ Autonoma Metropolitana Dept Sistemas Azapotzalco Av Sn Pablo 180 Mexico City 02200 DF Mexico Univ Autonoma Metropolitana Dept Ingn Elect Unidad Iztapalapa Mexico City DF Mexico Univ Extremadura Dept Comp Sci C Santa Teresa Jornet38 Merida 06800 Spain Univ Autonoma Metropolitana Unidad Iztapalapa Dept Chem Mexico City DF Mexico

ISBN: (纸本)9783031477645;9783031477652

pattern recognition has been evolving to include problems posed by new sceneries containing a high number of pattern components. processing this volume of information allows a more exact classification in wider types of applications;however, some of the difficulties of this scheme is the maintenance of numerical precision and mainly the reduction of the execution time. During the last 15 years, several Machine Learning solutions have been implemented to reduce the number of pattern components to be analyzed, such as artificial neural networks. Deep learning is an appropriate tool to accomplish this task. In this paper, a convolutional neural network is implemented for recognition and classification of human activity signals and digital images. It is achieved by automatically adjusting the parameters of the neural network through genetic algorithms using a multiprocessor and GPU platform. The results obtained show the reduction of computational costs and the possibility of better understanding of the solutions provided by Deep Learning.

关键词： Deep Learning pattern recognition HAR image recognition Parallel Genetic Algorithms

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