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Design Extraction and image Replication System Construction of Ceramic Art Based on Machine Vision

Design Extraction and Image Replication System Construction ...

引用

2022 International conference on image, Signal processing. and pattern recognition, ISPP 2022

作者： He, Yueming Fuzhou University Xiamen Academy of Arts and Design Xiamen361021 China

ISBN: (纸本)9781510654846

With the development of high technology, the digitization of works of art and cultural relics is no longer a problem. How to extract enough and accurate information from a large number of complex information for our use is a common problem in practice. With the continuous improvement of digital inkjet technology and color ink quality, many digital products have appeared in the market, such as digital oil painting, digital traditional Chinese painting, digital calligraphy, digital printmaking, digital paper cutting, digital embroidery and other digital art replicas. However, in addition to modeling, pattern is also an important part of art. Chinese traditional patterns have a long history and brilliant achievements. After solving the problem of production automation, leading enterprises in various industries began to seek detection automation and assembly automation based on machine vision. Machine vision technology has become one of the hot spots in the industry. With the rapid development of 3D technology, its application scope is becoming more and more daily, from cultural relic restoration, simulation manufacturing, digital sculpture to ceramic art production, etc. Machine vision should use image recognition technology instead of human eyes to measure and judge, which is more efficient, more accurate and more objective, and can be repeated endlessly, greatly improving the degree of automation of production. This paper focuses on the three-dimensional patterns of ceramic artworks, aiming at high-quality reproduction and design re-creation, deeply studies the collection and image splicing technology of three-dimensional patterns of ceramic artworks, and constructs an image reproduction system, which realizes the whole digital process from three-dimensional pattern shooting, preprocessing. image correction, two-dimensional transformation of three-dimensional images, image registration, image fusion to generation of complete two-dimensional patterns. © 2022 SPIE.

关键词： computer vision

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Follicular Unit Registration Based on Binocular Stereo Vision for Hair Transplantation Surgery Robot 6

Follicular Unit Registration Based on Binocular Stereo Visio...

引用

6th International conference on Data Science and Information Technology, DSIT 2023

作者： Wang, Weiduo Liu, Jinfeng Yang, Jie Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation China Shanghai Jiao Tong University Institute of Medical Robotics China

ISBN: (纸本)9798350304442

In this paper, we focus on the follicular unit registration problem for hair transplantation surgery robots based on binocular stereo vision system. The follicular units in both images are detected with YOLO V5 network. Because of the dull and similar pattern of the follicular units, previous regular methods which are pixel-based are reported with misalignment and poor accuracy. To solve this problem, we propose the context shape to describe the feature of the follicular unit. The distribution of units surrounding the target unit is estimated as the context shape. Then, Otsu's method is used to segment hairs and Hungarian algorithm is used to match the hairs in the matched follicular unit. Then, the hairs are projected to 3D coordinate system and reconstructed. The experiments on the generated hair dataset demonstrate that the proposed method achieves both high accuracy and speed. © 2023 ieee.

关键词： Stereo vision

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HR_BMNet: A boundary perception and multiresolution fusion network for semantic segmentation of remote sensing

HR_BMNet: A boundary perception and multiresolution fusion n...

引用

2024 International conference on image processing.and Artificial Intelligence, ICIPAl 2024

作者： Chen, Yan Wang, Mengyuan Jiang, Wenxiang Kang, Menglei Wang, Xiaofeng Collaborative Innovation Centre for Computer Vision and Pattern Recognition School of Artificial Intelligence and Big Data Hefei University Hefei230601 China

ISBN: (纸本)9781510681514

The conventional approach for semantic segmentation of remote sensing imagery using encoder-decoder convolutional neural networks relies on the output of prior feature maps sequentially without considering the interactions between neighboring contextual feature maps with multiple resolutions. While the standard HRNet proposal has successfully improved multi-resolution semantic and spatial features to address the aforementioned issues, its lack of emphasis on boundary perception often results in inadequate target segmentation. Furthermore, a frequent occurrence of multiresolution contextual interaction in HRNet leads to the addition of a significant quantity of redundant information and amplifies the complexity of the model. Hence, to tackle the abovementioned issues, we propose a semantic segmentation network identified as HR-BMNet, which incorporates boundary sensitivity and multiple-resolution learning. The idea associated with standard HRNet is adopted as the foundational architecture. We extend novel boundary perception and multi-resolution fusion attention modules, integrating channel attention mechanisms. The strategy provides an ex-tensive optimization of edges and the efficient capture of crucial multi-scale features. During the feature combination stage, the boundary insights are employed to augment the semantic information, thereby mitigating the spatial details loss, enhancing the intra-class semantic consistency, and achieving superior segmentation. The efficacy of the proposed method is validated through comparison and ablation experiments conducted on the ISPRS Vaihingen and CSRSD datasets. Among the experiments conducted, the best ones attained a mean Intersection over Union (mIoU) of 72.11% on the Vaihingen dataset and 89.28% on the CSRSD dataset, respectively. © 2024 SPIE.

关键词： Semantic Segmentation

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Fault pattern recognition Method of Rolling Bearing Based on MTF-2DWDCNN 3

Fault Pattern Recognition Method of Rolling Bearing Based on...

引用

3rd International conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xun, Zhiwen Miao, Xiaodong Yu, Hu Gu, Yinji Nanjing Tech University School of Mechanical and Power Engineering Nanjing China Nanjing Tech University School of computer science and technology Nanjing China

ISBN: (纸本)9781665492812

2-D fault feature images have advantages as neural network input for fault pattern recognition, but the most deep image recognition algorithms are non-sensitive to the time-series features of images. This paper proposes a new algorithm named MTF-2DWDCNN based on Markov transfer field(MTF) jointly improved deep convolutional neural networks with wide first-layer Kernel(WDCNN). Firstly, the original 2-D sequence signal is converted into a 2-D time series image by MTF, used as the input of the neural network;secondly, the convolution mode of the original WDCNN is improved to adapt to 2-D time series;finally, the accuracy and error experiments are carried out to verify its effectiveness. Experimental results show that the method proposed in this paper has certain advantages over traditional algorithms regarding recognition accuracy. © 2022 ieee.

关键词： Bearing deep learning fault diagnosis MTF

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Low-Dimensional Feature Representation with Hybrid Attention for Few-Shot image Classification 29

Low-Dimensional Feature Representation with Hybrid Attention...

引用

29th ieee International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yao, Xin-Wei Yuan, Zhi-Heng Fang, Yu-Li He, Chuan Zhang, Yu-Chen Li, Qiang Zhejiang University of Technology College of Computer Science and Technology Hangzhou China Zhejiang University of Technology Taizhou Research Institute Taizhou China Zhejiang University of Technology Institute for Frontier and Interdisciplinary Sciences Hangzhou China

ISBN: (纸本)9798350330717

Learning effective image representation and constructing a suitable metric space are two main challenges in few-shot image classification. Existing methods normally consider the joint characteristic distribution of the image to improve the image representation ability, but it also brings high computational cost and produces high-dimensional embedding vectors, which limit their real-world applicability. In this paper, in order to reduce computational complexity and embedding dimension, we propose an effectively low-dimensional feature representation module (LDFR), which introduces a window mechanism to make the model focus on the correlation between local channels by using Brownian Distance Covariance. Furthermore, we combine LDFR with Hybrid Attention (LDFR-HA) to solve the problem of unbalanced sample features in few-shot image classification. Specifically, weighted average is adopted in the Hybrid Attention to construct the metric space and perform classification. Numerous experiments are conducted on two standard few-shot image classification benchmarks, i.e., general object recognition and fine-grained categorization. Extensive evaluations demonstrate that LDFR-HA significantly outperforms existing approaches. On popular datasets miniimageNet and CUB, LDFR-HA achieves 3.52 percentage point(pp)/2.29pp and 2.1pp/1.45pp gains over the state-of-the-art method on 5-way 1-shot/5-shot tasks, respectively. © 2023 ieee.

关键词： Brownian Distance Covariance Few-shot image classification Hybrid Attention Low-Dimensional Feature

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Research on Intelligent Monitoring Method of External Damage Around Transmission Line Based on Millimeter Wave Radar and Camera

Research on Intelligent Monitoring Method of External Damage...

引用

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

作者： Zhang, Sihang Yang, Zhi Zhang, Chenyu Zhang, Jun Li, Danyu Zhang, Nailong Zhao, Bin Li, Mengxuan Liu, Yi Zhao, Binbin Liu, Chang China Electric Power Research Institute Beijing100192 China JiangSu Electric Power Company Nanjing210024 China JiangSu Electric Power Company Electric Power Research Institute Nanjing211103 China

ISBN: (纸本)9781510666351

In recent years, due to the shortage of transmission line channel resources, power companies have begun to expand the capacity of existing important transmission lines. The safe and stable operation of important transmission lines is critical to the power supply status of users. Timely monitoring of potential hazards of construction machinery near the transmission lines has become a hot topic of transmission line protection against external damage. Aiming at the technical bottleneck of high false alarm rate of the current transmission line using online monitoring camera, this paper proposes a construction vehicle detection method based on the fusion of radar and visual features, which uses the physical features and geometric features of the target. The physical features such as velocity and acceleration are selected from radar. After the fusion of the radar data and camera data, the region of interest (ROI) of the radar target on the image is obtained, and the gradient direction histogram feature is extracted on the ROI. The visual features are calculated by the statistical features of gradient direction histogram, including standard deviation, median and average. This paper constructs a neural network R-V-DenseNet whose input is the fusion feature of radar and vision. Then a data set is made to train the network. The experimental results on the test set prove that the accuracy of R-V-DenseNet is improved compared with the traditional HOG-SVM method and the single sensor based detection method, which means the proposed method gains more accurate detection. © 2023 SPIE.

关键词： computer vision

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image Pre-processing.Algorithms for the Quality Detection of Tea Leaves 2

Image Pre-Processing Algorithms for the Quality Detection of...

引用

2nd International conference on Technological Advancements in Computational Sciences, ICTACS 2022

作者： Gaba, Ira Ramamurthy, B. Computer Science Department Bangalore India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

This Identification and prediction of the tea quality is the essential research focus nowadays in the field of agriculture. Nowadays the Artificial Intelligence has become the latest topic in the region of pattern recognition. The various combination and permutation of the different techniques has resulted in proper solving the problem as well as have better accuracy in recognition. Therefore, there is urge need of a detailed survey AI techniques used for the identification of the tea leaf quality for the different grades of tea plants. In this paper, we aim on the various methods used for the pre- processing.of the input image to extract the processed image which will further be useful for the feature extraction and the classification of the proposed image. It is very important to get the effective and accurate processed data which will further act as an input for the next level modules. This paper shows various methods of edge detection are applied on the image like Canny, Sobel and Laplacian are used. The further results are compared for quality metrics parameters such as the Mean Square Error (MSE) & Structural Similarity Index Metric (SSIM). The main agenda of this paper is to perform the edge detection and to check the quality measure of the processed image. The software used here is python. © 2022 ieee.

关键词： Python

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Malayalam Digit recognition Using CNN 3

Malayalam Digit Recognition Using CNN

引用

3rd International conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Konikkara, Divya Usha, K. Nss College of Engineering Department of Computer Science Palakkad India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Handwritten Digit recognition is the recent prominent field of research with the emergence and evolution of algorithms in machine learning, pattern recognition, computer vision and deep learning algorithms. Handwritten Digit recognition system plays a best role in the field of data statistics, financial affairs like yearly checkup, inhabitant statistics, sorting the mail, processing.of the bank checks, etc. The proposed system deals with the aspects of machine learning, where a model is built to extract and identify the handwritten Malayalam digits from an input image. The system involves in building an Convolutional Neural Network with filters that enhances the ability to recognizing correct features and predict the Malayalam digits accurately. The first stage of Malayalam Digit recognition system is Malayalam Digit dataset creation followed by pre-processing.step and then build the CNN model and extract the features and last the system classifies the digits. The Handwritten Malayalam Digit recognition system recognizes the Malayalam Digits with an accuracy of 83%.The proposed system finds real-time applications. © 2022 ieee.

关键词： Convolutional neural networks

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ICMEW 2022 - ieee International conference on Multimedia and Expo Workshops 2022, proceedings

ICMEW 2022 - IEEE International Conference on Multimedia and...

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2022 ieee International conference on Multimedia and Expo Workshops, ICMEW 2022

ISBN: (纸本)9781665472180

The proceedings contain 69 papers. The topics discussed include: a deep drift-diffusion model for image aesthetic score distribution prediction;augmented-training-aware Bisenet for real-time semantic segmentation;low-power semantic segmentation on embedded systems for traffic in Asian countries;summary of the 2022 low-power deep learning semantic segmentation model compression competition for traffic scene in Asian countries;transformer based multimodal scene recognition in soccer videos;package theft detection from smart home security cameras;watermarking protocol for deep neural network ownership regulation in federated learning;a transformer-based approach for metal 3D printing quality recognition;diversity-based media search;a unified video summarization for video anomalies through deep learning;efficient topology coding and payload partitioning techniques for neural network compression (NNC) standard;and multi-augmentation for efficient self-supervised visual representation learning.

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Translation of Manual Script to Automated Script 1

Translation of Manual Script to Automated Script

引用

1st International conference on Computational Science and Technology, ICCST 2022

作者： Dhaarani, A. Rajpriya, R. Manimala, G. Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665476553

Handwritten Recently, one of the most difficult problems in image processing.and pattern recognition has been character recognition. Digitally repurposing a picture of a paper document known as a handwritten signature As an input, an optical picture of a character is sent into OCR, which generates an output character based on that character. Convolutional Neural Network (CNN), a prominent deep neural network architecture, is used to recognize written characters in our suggested system. When ahigh-definition picture of a handwritten document is sent into the system, the system separates out the individual characters and recognizes them. The desired result is an image format for the input picture that has been provided. © 2022 ieee.

关键词： Optical character recognition

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