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A dwell point detection algorithm for smart pen 2

A dwell point detection algorithm for smart pen

引用

2nd International Conference on Computer Graphics, Image and Virtualization, ICCGIV 2022

作者： Kong, DeHui University of Jinan School of Information Science and Engineering Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9781665492508

This paper presents a dwell point detection algorithm for smart pen. The main innovations of the method are: (a) The tracking of a smart pen is achieved by a common monocular camera. (b) A method of vertex adsorption is proposed. (c) The definition of a dwell point is introduced, and the auxiliary line intent is calculated by detecting the dwell point. Compared to the collision body detection method in Unity3D, the method in this paper improves the accuracy of doing auxiliary line intent and the smoothness of user operation. The effectiveness of the method is demonstrated by the use of the method in a smart pen interacting with a Unity3D-based three-dimensional geometry teaching system. © 2022 IEEE.

关键词： Virtual reality

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Multimodal intention understanding method based on NHMM 2

Multimodal intention understanding method based on NHMM

引用

2nd International Conference on Computer Graphics, Image and Virtualization, ICCGIV 2022

作者： Liu, Hao University of Jinan School of Information Science and Engineering Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9781665492508

This paper proposes a multimodal intention understanding method based on NHMM. Due to the declining expression ability of the elderly, single-mode input during human-computer interaction may reduce the accuracy of robot intention understanding and cannot meet the requirements of helping the elderly. Therefore, in the aspect of intention understanding, this paper uses multi-modal input of voice, gesture and posture to obtain the user's multi-dimensional information as much as possible. Each modal information is processed to obtain three intention sets, and then fused to obtain the fused intention set, By combining the improvement of the core formula of hidden Markov model (HMM) with Bayes, the intention with the highest probability in the fusion intention set is calculated as the result of intention understanding. The results show that the accuracy of this method is 97.7% in the state of user natural interaction, and compared with the other four intention understanding methods, this method is more effective for intention extraction under fuzzy input. © 2022 IEEE.

关键词： Human computer interaction

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Multiple Random Feature Superpixel-based Fuzzy Clustering for Image Segmentation 9

Multiple Random Feature Superpixel-Based Fuzzy Clustering fo...

引用

9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Zhang, Chuanbin Chen, Long Tian, Yifei Shi, Zhaoyin Wang, Yingxu University of Macau Department of Computer and Information Science China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9798350342239

Random feature maps attempt to approximate the kernel method with low computational complexity, and they are efficient and effective algorithms for dealing with the non-linear structure of data. Nevertheless, the existing random feature-based methods are sensitive to hyper-parameters, and their performances are affected by the randomness in feature maps. To improve the robustness of random feature maps and further speed up kernel-based image segmentation algorithms, a multiple random feature superpixel-based fuzzy clustering is proposed in this paper. First, we generate a group of random features from the original image via a novel multiple random feature scheme. Then, we use the superpixel segmentation to reduce the scale of multiple random features. The final segmentation results are obtained by performing the multi-view fuzzy clustering on the multiple random feature superpixels. Experiments on two shared benchmark data sets verify the superiority of our approach. © 2023 IEEE.

关键词： Image segmentation

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Attention-Aware U-Net network for Segmentation of Retinopathy Region

Attention-Aware U-Net Network for Segmentation of Retinopath...

引用

International Conference on Image, Vision and intelligent Systems, ICIVIS 2021

作者： Kong, Wenyang Li, Fan Xing, Ruiwen Gao, Xizhan Zhao, Hui Su, Jie Niu, Sijie School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789811669620

The important function of the computer-aided analysis platform for OCT (Optical Coherence Tomography) retinal images is image segmentation and the accuracy of the segmentation results affects the diagnosis of the disease area. In this paper, we improve the U-Net network by introducing attention mechanism and residual learning for segmenting retinal image lesions. based on the original U-Net architecture, we embed the Squeeze-and-excitation module into the residual learning branch of the residual structure to replace the backbone network of U-Net. As a result, our improved method not only retains the advantages of fast convergence of network and Solve the problem of the weakening of the neural network's ability to extract features in the deep level, but also considers the correlation between attention learning channels, enhances useful features, and suppresses noise features. The data set used for training and testing of the method in this paper contains 2944 images from 23 patients diagnosed with ocular CSC (Central Serous Chorioretinopathy). We performed 4 sets of cross-validation on this dataset. According to experimental data, the method proposed in this paper can get better segmentation results. Compared with seven methods, our improved method can achieve competitive segmentation results, which is helpful for clinical disease diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical tomography

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A Survey of FPGA-based Deep Learning Acceleration Research

A Survey of FPGA-Based Deep Learning Acceleration Research

引用

International Conference on Image, Vision and intelligent Systems, ICIVIS 2021

作者： Lv, Ziyi Zhang, Jing School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789811669620

In a range of fields such as emotion detection, medical image processing and speech recognition, deep learning has recently achieved good results. With the pursuit of more precise results, many scholars try to add more new type network layers to increase the size of the neural network. However, this will lead to deeper and more intricate network models, and training and evaluating models requires intensive CPU calculations and tremendous computing resources which cannot be achieved by general purpose processors. Nowadays, some hardware accelerators such as Field Programmable Gate Array (FPGA) have been employed to accelerate the neural network, and FPGA with reconfigurability and low power consumption are currently applied to improve throughput of deep learning networks at a reasonable price. In this paper, the typical technologies and methods of accelerating deep learning network on FPGA in recent years are reviewed and analyzed with their advantages and disadvantages, and feasible research suggestions for the next research direction are given. It is expected that it will have a certain reference value for researchers in the field of deep learning acceleration and hardware optimization. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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An improved Criminisi method for image inpainting 2

An improved Criminisi method for image inpainting

引用

2022 2nd International Conference on Electrical, Electronics and computing Technology, EECT 2022

作者： Li, C. Chen, H. Han, X. Pan, X. Niu, D. Shandong Provincial Key Laboratory Of Network Based Intelligent Computing University Of Jinan China School Of Computer Science And Technology Shandong University Of Finance And Economics China

The Criminisi method is a classical image inpainting method, which has flaw of unreasonable priority calculation. To overcome this flaw, we propose an improved Criminisi method. To improve the accuracy of a repaired block's priority, we propose a novel method by considering the continuity of structure and color information between the repaired block and its adjacent blocks. When searching the matching block for a repaired block, we compute the similar between two blocks as the sum of squared differences (SSD). Since different blocks within an image may contain different levels of information, we set block to different sizes to determine the best matching block. Experimental results demonstrate the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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Normative Prior network for Anomaly Segmentation in Retinal OCT Images 2nd

Normative Prior Network for Anomaly Segmentation in Retinal ...

引用

2nd International Conference on Image, Vision and intelligent Systems, ICIVIS 2022

作者： Zhou, Xueying Dong, Jiwen Li, Xiaohui Han, Tianhao Hou, Qingtao Gao, Xizhan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China Ji Nan Chao Feng Intelligent Technology Co. LTD. Jinan China

ISBN: (纸本)9789819909223

Anomaly segmentation refers to leveraging only normal images for model training to detect pixel-level anomalies in abnormal images in test phase, which gets rid of the reliance on expensive manual labels and is able to detect rare anomalies that are hard to collect in training dataset. However, previous works performing anomaly segmentation in retinal OCT images heavily relied on complex post-processing methods and cannot detect anomalies in an end-to-end manner. To overcome that, in this paper, a novel anomaly segmentation method is proposed to segment retinopathy based on normative prior network. Specifically, a segmentation network and a variational autoencoder is constructed to extract retinal regions and learn normative prior of normal retinal structure, respectively. In inference phase, the anomalous region can be directly segmented by low structural similarity between abnormal retinal structure and the normal one reconstructed by variational autoencoder. Extensive experiments are conducted to evaluate the performance of the proposed method and the experimental results show that it achieves the state-of-the-art (SOTA) performance for anomaly segmentation in retinal OCT images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning systems

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An Image Enhancement Method for Domestic High-Resolution Remote Sensing Satellite 23

An Image Enhancement Method for Domestic High-Resolution Rem...

引用

6th International Conference on Big Data Technologies, ICBDT 2023

作者： Wang, Can Liu, Kun Shi, Jiechuan Yuan, Hao Wang, Weiwei School of Information Science and Engineering University of Jinan Jinan250024 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China QiLu Research Institute Jinan250024 China

ISBN: (纸本)9798400707667

Remote sensing images have important applications in many fields, but these images are often disturbed by noise, which will affect the subsequent application. based on Gaussian noise removal of remote sensing images, this paper compares four deep learning image denoising algorithms, namely MPRNet, CBDNet, NAFNet and *** use the remote sensing image data set from Shandong Data and Application Center of National High Resolution Earth Observation System, and simulate the real application scenario by slicing and adding noise. Algorithm evaluation indexes PSNR and SSIM are used to compare the performance of each algorithm. In the experiment, we find that NAFNet has better effect on Gaussian noise removal than other algorithms. The main research results are to verify the effect of four algorithms on specific data sets, and summarize their advantages and disadvantages, so as to provide a reference for the application of image denoising algorithms. In addition, this paper is characterized by the use of remote sensing data set to analyze the specific scenario, which has a certain contribution to the research in this *** significance of this paper is to provide a more accurate and efficient algorithm for remote sensing image denoising to solve the interference of Gaussian noise to remote sensing image processing. In short, through the research in this paper, we found that NAFNet had the best effect on image Gaussian noise removal in the remote sensing data set in this paper, and its PSNR reached 28.49 and SSIM reached 0.9928. Among them, PSNR is an index to evaluate image quality, and the higher the value, the better the image quality. It is more conducive to the subsequent target detection of the image;SSIM is an index to measure image similarity. The larger the SSIM value is, the more similar the de-noised image is to the original image, indicating that the image retains the features of the original image to a great extent, and the subsequent target detection

关键词： Image enhancement

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Research on A Multimodal Fusion Intention Understanding Algorithm for Geriatric Carer Robots 2

Research on A Multimodal Fusion Intention Understanding Algo...

引用

2nd International Conference on Computer Graphics, Image and Virtualization, ICCGIV 2022

作者： Li, WeiNa University of Jinan School of Information Science and Engineering Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9781665492508

In the large application background of man-machine collaboration, the elderly are easy to lose direction or can't understand the map. When aging users express their intentions to robots, due to ambiguous language or incorrect gestures, robots may have a variety of intentions to understand and analyze. In this paper, an optimization algorithm model is proposed to avoid robot misunderstanding, which can choose the best intention among various kinds of intention understanding, and perform user feedback evaluation on the output intention, so as to understand the real intention of the user. In view of the traditional serial analysis of user behavior in multimodal fusion algorithm limitations, this paper puts forward the algorithm of model through voice recognition and image recognition input information user behavior, care layer parallel fusion user intent from the voice channel and the visual channel, use the feedback mechanism to determine the true intentions of the user from the user's intent to complete robot navigation links. © 2022 IEEE.

关键词： Speech recognition

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Non-Destructive Judgment of Watermelon Ripeness based on Multi-Feature Fusion of Image

Non-Destructive Judgment of Watermelon Ripeness Based on Mul...

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Hongbao Shi Jun Liu Yimei Huang Jinping Li Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

The nondestructive judgment of watermelon ripeness is a key issue in everyday life. Whether watermelons are ripe or not affects the interests of watermelon farmers and consumers. So far, acoustic, electrical, machine vision and other methods have been applied to the nondestructive judgment of agricultural product ripeness, but the equipment used in the lab, such as spectrometers and laser Doppler vibrometers, is expensive, difficult to carry, and has limited practical value in daily life. based on practical experience, the ripeness of watermelons can be judged by observing specific physical characteristics, such as the rind's glossiness, texture, fuzz on the fruit stalk and navel. In this study, images of the watermelon's texture, fuzz and navel were captured using a smartphone. We utilized ResN et with an embedded Coordinate Attention mechanism and Swin-Transformer networks to independently analyze the ripeness of each feature. Then we combined the output prediction scores using post-fusion technology, with the highest weighted prediction score judging the final ripeness assessment. Our method achieved a 90.2% accuracy rate on the test set. The experiments demonstrate that a deep learning model utilizing multi-feature fusion of image data can reliably and accurately identify watermelon ripeness, even under varying image conditions.

关键词： Deep learning Laser theory Accuracy Vibrometers Machine vision Morphology Knowledge discovery Mobile handsets Reliability Guidelines

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