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Research on image filter template based on cellular neural network

Research on image filter template based on cellular neural n...

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2021 international conference on Electronic Information Engineering and Computer Science, EIECS 2021

作者： Li, Shuo Li, Yuqin Yang, Chen Jiang, Zhengang School of Computer Science and Technology Changchun University of Science and Technology Changchun China The First Bethune Hospital of Jilin University Department of Bone and Joint Surgery Changchun China

ISBN: (纸本)9781665416740

image filtering is an indispensable task in image processing. Although the existing image filtering methods improve the effect of image filtering, most of them ignore the running time of the algorithm. In order to solve this problem, this paper applies the cellular neural network (CNN) to achieve image filtering, and designs three image filter templates: mean filter, median filter and Gaussian filter. Compared with the traditional mean filter, median filter, Gaussian filter, iterative mean filter (IMF), adaptive median filter (AMF) and Gaussian curvature filter (GCF), the simulation results show that the proposed method achieves the same level as the traditional methods in mean square error (MSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The running time of the mean filter and Gaussian filter based on CNN are less than that of traditional mean filter and Gaussian filter. In addition, the three filter templates proposed in this paper are better than IMF, AMF and GCF in dealing with Gaussian noise and multiplicative noise. Running time of the algorithm is also significantly lower than that of IMF, AMF and GCF. The mean filter, median filter and Gaussian filter are implemented by using the theory of cellular neural network. © 2021 ieee.

关键词： Median filters

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Assessing the Robustness of Deep Learning-Assisted Pathological image Analysis Under Practical Variables of Imaging System

Assessing the Robustness of Deep Learning-Assisted Pathologi...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Yuxuan Sun Chenglu Zhu Yunlong Zhang Honglin Li Pingyi Chen Lin Yang College of Computer Science and Technology Zhejiang University China Research Center for Industries of the Future and School of Engineering Westlake University China

With the advancement of deep learning, computer-assisted clinical diagnosis, such as liquid-based cervical cytology, has attracted more attention. However, the fragile robustness of deep learning models has a non-negligible impact on their classification accuracy and reliability. To be more specific, various scanner parameters will be used depending on the pathologist’s preferences during the clinical diagnosis process (e.g., field source brightness, contrast, saturation, etc.), and this variation will lead to the unstable performance of the model. In this paper, we construct an evaluation pathway to assess the stability and consistency of deep learning models under various customized scanner parameters. Specifically, a multi-scanned dataset consists of 4200 whole slide images (WSIs) is generated by scanning 200 stained slices using various scanner parameters. Moreover, we conducted a large number of experiments to investigate the robustness of numerous models, including convolution-based and transformer-based models concerning various scanner parameter settings. Furthermore, we introduce several indicators to analyze the prediction accuracy, consistency and robustness of the model on the constructed dataset. The experimental results indicate that the deep learning models are sensitive to luminance-related scanner parameters. In addition, transformer-based models have better robustness than traditional convolutional neural networks. Our code has been made available 1 .

关键词： Deep learning Pathology image analysis Imaging Predictive models Robustness Stability analysis

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Multiclass Somatosensory Selective Attention BCI Classification based on Riemannian Geometry Approach Combing Time-Frequency Sub-Band Selection and Feature Reduction

Multiclass Somatosensory Selective Attention BCI Classificat...

引用

international conference on Vision, image and signal Processing (ICVISP)

作者： Xiangke Han Xiaobo Guo Shifeng Ruan Department of Mechanical Engineering Anyang Institute of Technology Anyang China Flight College Anyang Institute of Technology Anyang China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Somatosensory selective attention (SSA)-based BCI has received a great deal of attention due to its no training and non-visual characteristics. However, there are still two issues that constrain its application. One is that the control commands of SSA-BCI is limited. The other is that the high-accuracy classification for multiclass SSA modality remains a challenge. Methods To address these issues, this work constructed a multiclass SSA-based BCI system and presented a novel Riemannian geometry (RG) framework combing subject-specific time-frequency sub-band selection and feature reduction. In this study, two linear resonant tactors were separately adhered to both index fingers of each subject, and three SSA tasks (left-SSA, right-SSA, and idle-SSA) were implemented according to the random displayed cues. To validate its performance, 10 subjects took part in the experiment and were collected EEG signals. In addition, a RG framework was provided to identify this three-class SSA modality. Firstly, EEG signals were partitioned into multiple subsegments and bandpass filtered by a filter bank. Subsequently, the optimal time-frequency sub-bands were chosen according to Laplacian score (LS), and then the RG features achieved from each chosen sub-band were reduced using neighborhood component analysis (NCA) algorithm. Finally, a support vector machine (SVM) was trained for SSA-BCI identification. This approach yields an average accuracy of 79.48% in three-class SSA modality, which is considerably better than other competing methods. This study demonstrates that our developed RG framework is an attractive option for enhancing the multiclass SSA-BCI modality's performance.

关键词： Somatosensory Geometry Support vector machines Training Time-frequency analysis Laplace equations Filter banks signal processing algorithms Feature extraction Electroencephalography

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1st international conference on Intelligent systems in Computing and Communications, ISCComm 2023

1st International Conference on Intelligent Systems in Compu...

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1st international conference on Intelligent systems in Computing and Communications, ISCComm 2023

ISBN: (纸本)9783031756078

The proceedings contain 53 papers. The special focus in this conference is on Intelligent systems in Computing and Communications. The topics include: Towards Hands-Free Computing: AI Virtual Mouse Interface Powered by Gestures;smartAgro: Precision Yield Prediction, Crop Insights, and Real-Time Dashboard;Artificial Intelligence with MRI-Guided Radiation Therapy for Cancer Treatment;Software Requirements to UML Class Diagrams Using Machine Learning and Rule-based Approach;CFALEA_LSTM: Adaptive Lotus Effect Algorithm Enabled Long Short-Term Memory for Rainfall Prediction Using Time Series Data;a Deep Learning Survey on Diseases Prediction and Detection in Health Care;fault Diagnosis in Belts Using signal Processing Techniques and Machine Learning;machine Learning Techniques based Chronic Kidney Disease Detection with Performance Analysis of Fuzzy Rough Set and Correlation Attribute Selection;segmentation and Classification of Unharvested Arecanut Bunches Using Deep Learning;enhanced Satellite image Fusion Using Deep Learning and Feature Extraction Techniques: A Survey;intelligent Aircraft Antiskid Braking systems – A Review;artificial Neural Networks Applied in the Detection of Breast Cancer;supervised and Unsupervised Learning Techniques for Malware Classification based on Opcode Frequency Features;predictive Analytics for Diagnosing Alzheimer’s Disease Using Artificial Intelligence and Machine Learning Algorithms;A Comprehensive Study on Artificial Intelligence (AI) Driven internet of Healthcare Things (IOHT);predictive Models for the Early Diagnosis and Prognosis of Knee Osteoarthritis Using Deep Learning Techniques;predicting Salinity Resistance of Rice at the Seedling Stage: An Evaluation of Transfer Learning Methods;Multi-camera HD Pedestrian Dataset for Person Detection and Re-identification;Fire Detection System Using Deep CNN.

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Fire door opening method of cable tunnel patrol robot based on YOLOv4 image recognition algorithm 2

Fire door opening method of cable tunnel patrol robot based ...

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2nd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2021

作者： Li, Chong Li, Xueliang Sun, Zongwei Wang, Kenan Yang, Yuechen Guo, Rui Liu, Piyu State Grid Intelligence Technology Co.Ltd. Jinan China State Grid Shandong Electrical Power Company Jinan China

ISBN: (纸本)9781665432658

This paper first introduces the position signal method, RFID tag identification method, mechanical arm door opening method of three kinds of robot fire door, compared and analyzed the applicable environment and shortcomings of these methods. In order to overcome the above shortcomings, YOLOv4 image recognition algorithm technology was innovatively applied to the door opening method, and a method of robot opening fire door based on YOLOv4 image recognition algorithm was proposed, referred to as image recognition method for short. The test results show that the detection accuracy reaches 98%, which verifies the advantages of this method in the door opening method and can better meet the application requirements of the patrol robot in opening the fire door in the cable tunnel. © 2021 ieee.

关键词： Robots

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image-based Early Classification of Tomato Leaf Diseases Using Deep Neural Networks

Image-Based Early Classification of Tomato Leaf Diseases Usi...

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ieee international conference on Intelligent Techniques in Control, Optimization and signal Processing (INCOS)

作者： Rigved Thorat Gnana Surya Teja Reddy E. Poongothai Department of Computational Intelligence SRM Institute of Science and Technology Kattankulatur India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Food for the world's population is largely produced by plants, however, plant diseases cause substantial output losses. Continuous monitoring can reduce these losses. However, manual disease monitoring of plants is labor-intensive and error-prone. Early disease diagnosis utilizing computer vision and artificial intelligence (AI) has emerged as a viable answer to this problem. This strategy lessens the detrimental effects of illnesses and gets around the drawbacks of ongoing human monitoring. After comparing the Convolutional neural network (CNN) with architectures such as ResNet, MobileNet, DenseNet, and InceptionV3, Cnn was applied to simple images of tomato leaves and classified into several tomato leaf diseases. We assessed the models' performance in a variety of ways, including binary classification (distinguishing between healthy and unhealthy leaves), and seven-class classification (categorizing healthy leaves and various groups of diseased leaves). After opting for Convolutional Neural Network (CNN), we were able to achieve a classification accuracy of 95.8%.

关键词： Plant diseases Sociology Manuals signal processing Convolutional neural networks Medical diagnosis Artificial intelligence

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1st international conference on Intelligent systems in Computing and Communications, ISCComm 2023

1st International Conference on Intelligent Systems in Compu...

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1st international conference on Intelligent systems in Computing and Communications, ISCComm 2023

ISBN: (纸本)9783031756047

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Local and Global Feature Adaptive Adjustment Network for Remote Sensing image Scene Classification

Local and Global Feature Adaptive Adjustment Network for Rem...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Feng Cao Chang Liu Deyu Li Yuhua Qian Chao Zhang Hu Zhang School of Computer and Information Technology (School of Big Data) Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Taiyuan China Institute of Big Data and Industry Shanxi University Taiyuan China

Convolutional neural network (CNN)-based methods have been extensively used for remote sensing scene classification (RSSC) and have obtained remarkable classification results. However, its limitations in extracting global features have hindered further improvement. Transformers can directly capture global features through self-attention mechanisms, but they have deficiencies in modeling local features. Currently, an approach that directly combines CNN and Transformer features may lead to feature imbalance, and introduce redundant information. To address these problems, we propose a local and global feature adaptive adjustment network (LGFAANet) for RSSC. First, we employ a dual-branch network structure to extract local and global features from remote sensing scene images. Second, we design a local and global feature adaptive adjustment module (LGFAA) to dynamically allocate weights to the features. Third, we use a multi-layer feature aggregation module (MLFA) to aggregate the adjusted features, thereby further enhancing feature representation. Finally, we introduce joint loss to accelerate network convergence, while reducing intra-class distance and increasing inter-class distance. Experimental results demonstrate that our proposed method displays enhanced feature representation ability and outperforms existing state-of-the-art methods.

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Evaluation of Panoramic image Projection for "Ulaanbaatar GigaPixel Panaroma" 16th

Evaluation of Panoramic Image Projection for "Ulaanbaatar Gi...

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16th international conference on Intelligent Information Hiding and Multimedia signal Processing, IIH-MSP 2020 in conjunction with the 13th international conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Rentsendorj, Javkhlan Lkhagvasuren, Ganchimeg Oyun-Erdene, Batdorj Altangerel, Baatarbileg Namsrai, Oyun-Erdene DICS—SEAS—National University of Mongolia Ulaanbaatar Mongolia

ISBN: (纸本)9789813367562

As a virtual tour and virtual reality technology advances, resolution with gigapixel panoramic images becomes available. Gigapixel equirectangular panoramic image needs to load and access without any delay through the internet by any device like mobile, tablet, or a computer. Also, several popular visualization formats like equirectangular projection were developed to map a real scene to a panoramic image. However, the most popular panoramic visualization technique equirectangular projection may not the best choice. Visualizing equirectangular images as spherical panorama has distorted nadir and zenith that can be affecting the viewing experience. So, we must consider image quality. In this research, we show the result of the evaluation for the projection formats using various comparison method to find which projection format (equirectangular projection, cubemap projection, and octahedron projection) and visualization technologies provide better image quality and speed of loading over internet. We exclude other format that has lower quality than the equirectangular projection format used in this experiment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： image quality

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Are Objective Explanatory Evaluation Metrics Trustworthy? An Adversarial Analysis

Are Objective Explanatory Evaluation Metrics Trustworthy? An...

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ieee international conference on image Processing

作者： Prithwijit Chowdhury Mohit Prabhushankar Ghassan AlRegib Mohamed Deriche School of Electrical and Computer Engineering OLIVES at the Center for Signal and Information Processing Georgia Institute of Technology Atlanta GA Ajman University United Arab Emirates

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Explainable AI (XAI) has revolutionized the field of deep learning by empowering users to have more trust in neural network models. The field of XAI allows users to probe the inner workings of these algorithms to elucidate their decision-making processes. The rise in popularity of XAI has led to the advent of different strategies to produce explanations, all of which only occasionally agree. Thus several objective evaluation metrics have been devised to decide which of these modules give the best explanation for specific scenarios. The goal of the paper is twofold: (i) we employ the notions of necessity and sufficiency from causal literature to come up with a novel explanatory technique called SHifted Adversaries using Pixel Elimination(SHAPE) which satisfies all the theoretical and mathematical criteria of being a valid explanation, (ii) we show that SHAPE is, infact, an adversarial explanation that fools causal metrics that are employed to measure the robustness and reliability of popular importance based visual XAI methods. Our analysis shows that SHAPE outperforms popular explanatory techniques like GradCAM and GradCAM++ in these tests and is comparable to RISE, raising questions about the sanity of these metrics and the need for human involvement for an overall better evaluation.

关键词： Measurement Visualization Shape Shape measurement Neural networks Reliability theory Predictive models

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