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The study of segmentation and recognition technology of breast tumor ultrasound image based on adaptive BP neural network 3

The study of segmentation and recognition technology of brea...

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3rd International conference on Computer Vision and Data Mining, ICCVDM 2022

作者： Zhao, Tianyu Li, Jingyu Dai, Hang Hao, Liguo Liu, Yanan Medical Technology Department Qiqihar Medical University Qiqihar161006 China Foreign Language Department Qiqihar Medical University Qiqihar161006 China

ISBN: (纸本)9781510661363

Breast cancer is a tumor disease with a high incidence of female diseases, but it is also a disease that can reduce the mortality rate through early diagnosis and early treatment. A method of gray level co-occurrence matrix combined with BP neural network is proposed to improve the recognition rate of breast tumors. Using computer intelligent method to detect whether breast tumors are benign or malignant is a pattern recognition problem of breast microscopic images, which can help diagnose diseases in advance, thus improving the tumor treatment effect. How to accurately diagnose breast cancer patients is the key to the prevention and early diagnosis of breast tumors. Ultrasound imaging technology plays an important role in the diagnosis and treatment of breast tumors because of its characteristics of no radiation damage, simplicity, effectiveness and low cost. The traditional ultrasonic image segmentation method usually performs the most basic segmentation function of the image, and the subsequent analysis work often depends on the manual operation of doctors or technicians. The interpretation and diagnosis of near-infrared breast images are subjective, and doctors inevitably have subjective differences in interpretation. In order to reduce the subjective influence of different doctors on the analysis results and reduce the pressure of doctors' work, this paper innovatively proposes an automatic segmentation method of ultrasonic images by combining the target recognition algorithms in the field of pattern recognition. In this paper, the medical images of breast cancer tumor cells are researched, and based on BP neural network, the segmentation scheme of breast cancer medical images and the detection scheme of tumor cells based on feature scale expansion are proposed, which can successfully realize the automatic segmentation of breast cancer ultrasound images, effectively identify the tumor areas in the images, have good stability and accuracy, and bring new ideas fo

关键词： Diseases

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Study on Hyperthyroidism using Thermal image analysis 3

Study on Hyperthyroidism using Thermal Image Analysis

引用

3rd IEEE International conference on Intelligent Technologies, CONIT 2023

作者： Venkatesan, Dharshini Krishnamoorthi, Nirmala Mahadevan, Shriraam Sri Sivasubramaniya Nadar College of Engineering Department of Biomedical Engineering Chennai India Sri Ramachandra Medical Centre Department of Endocrinology Porur Chennai India

ISBN: (纸本)9798350338607

Hyperthyroidism is supposed to impact three major areas of the body: the eye, the neck, and the shin. The participants are examined, with a thermal camera, potentially non-invasive, less expensive technique to measure the level of temperature distributed in the eyes, neck, and shin region due to hyperthyroidism. The temperature distribution is seen through the color map of the images which indicates the minimum and maximum temperatures. The heat distribution in the eye, neck and shin region is studied and a qualitative and quantitative analysis is performed. The pre-processing techniques include cropping of the acquired image using the required threshold values. Then the cropped image are normalized and masked in the eyeball region, left and right eye *** surface plot of the preprocessed image is obtained for visualization of the intensity distribution. The sum of extrema (SX) is computed from the masked image. On the other hand the gradient operator is applied to the normalized image and fractal dimensions (FD) are computed using Higuchi *** intensity levels are analyzed in the red, green and blue regions respectively. The temperature distribution pattern of the hyperthyroid participant and normal participants are determined using the FD and SX. The statistical analysis is also performed to study the complexities in each of the regions for healthy and hyperthyroid participants. © 2023 IEEE.

关键词： Fractal dimension

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Dense Pedestrian Detection Based on Multiple Link Feature Pyramid Networks 3

Dense Pedestrian Detection Based on Multiple Link Feature Py...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Huang, Tao Yang, Siyu Yu, Tingjian Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Software Nanchang Hangkong University Nanchang China

ISBN: (纸本)9781510667563

Pedestrian detection is widely employed in a variety of industries, including security, autonomous driving, and pedestrian flow statistics. However, because most pedestrians in real-world scenes are densely distributed with occlusion issues, scale variation and pose variability, majority pedestrian detection algorithms have low detection accuracy and a high miss detection rate. In this paper, we propose a new FPN neck module, Multiple Link Feature Pyramid Networks (MLFPN) for dense pedestrian detection, which innovative information transfer pathways and incorporation of Poly-Scale Convolution (PSconv) capable of long-range interaction into feature extraction tasks to reduce feature information loss and enhanced representation of effective pedestrian features, achieving 0.6MR-2, 0.35AP, and 0.21JI improvements on CrowdHuman dataset, improving detection accuracy and reducing missed detection rates. © 2023 SPIE.

关键词： Feature extraction

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Secure image Retrieval Based on Deep CNN Features in Cloud Computing 3

Secure Image Retrieval Based on Deep CNN Features in Cloud C...

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3rd International conference on pattern recognition and Machine Learning, PRML 2022

作者： Fan, Zhongkui Guan, YePeng Shanghai University School of Communication and Information Engineering Shanghai China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

In cloud computing era, data owners become increasingly motivated to outsource their images from local sites to the commercial public cloud for great flexibility and economic savings. For the consideration of users' privacy, sensitive images have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. image CNN features are once considered confidential for image retrieval in cloud computing. However, recent studies have shown that these features contain ambiguous semantic information and can be reconstructed to images. We design a computable encryption scheme based on vector and matrix calculations for the risk of CNN feature-based image retrieval. The scheme uses SIFT and CNN features to pre-build a feature-based index to provide feature-related information about each encrypted image and then chooses the efficient image similarity measure as the pruning tool to carry out the retrieval procedure. Security analysis and performance evaluation show that the scheme is INI-CCA. The experimental results show that the proposed method has achieved excellent results on content-based image retrieval. © 2022 IEEE.

关键词： Cloud computing

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Face anti-spoofing based on ASFF and LBP self-supervision branch 3

Face anti-spoofing based on ASFF and LBP self-supervision br...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Ren, Junjie Zhang, Yi Zhang, Ruisheng Luo, Jianfeng Whale Cloud Technology Co. Ltd Nanjing210000 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China

ISBN: (纸本)9781510667563

Since the tremendous advancement of face recognition technology, how to detect real human faces from images has gradually captured the attention of researchers. To address the problem that most face anti-spoofing approaches ignore context information and local information about faces, this paper proposes a face anti-spoofing algorithm that fuses multi-scale features and texture features of images. On the one hand, we extract multi-scale features using adaptively spatial feature fusion (ASFF) and polarized self-attention (PSA) mechanism. On the other hand, local binary patterns (LBP) is used to form an auxiliary self-supervision branch to extract image texture difference information. As a final step, end-to-end live detection is achieved by balancing the difference between two different loss functions based on homoscedastic uncertainty. The EER of the presented method is 0.81% and 1.26% on CASIA-FASD and MSU-MFSD, respectively, and the ACER is only 1.64% and 2.07%±1.05% on the second and third protocols of Oulu_NPU, which indicates the validity of the method. © 2023 SPIE.

关键词： Local binary pattern

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3rd International conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd International conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology;a Comparative analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire Detection Using image Processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions

Templates for 3D Object Pose Estimation Revisited: Generaliz...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Van Nguyen Nguyen Hu, Yinlin Xiao, Yang Salzmann, Mathieu Lepetit, Vincent Univ Gustave Eiffel Ecole Ponts LIGM CNRS Champs Sur Marne France Ecole Polytech Fed Lausanne CVLab Lausanne Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these objects nor real images depicting them, only their CAD models. It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of "templates", rendered images of the CAD models for the new objects. In contrast with the state-of-the-art methods, the new objects on which our method is applied can be very different from the training objects. As a result, we are the first to show generalization without retraining on the LINEMOD and Occlusion-LINEMOD datasets. Our analysis of the failure modes of previous template-based approaches further confirms the benefits of local features for template matching. We outperform the state-of-the-art template matching methods on the LINEMOD, Occlusion-LINEMOD and T-LESS datasets. Our source code and data are publicly available at https://***/nv-nguyen/template-pose.

关键词： Training Solid modeling Computer vision Three-dimensional displays image recognition Impedance matching Pose estimation

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3rd International conference on Mobile Radio Communications and 5G Networks, MRCN 2022

3rd International Conference on Mobile Radio Communications ...

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3rd International conference on Mobile Radio Communications and 5G Networks, MRCN 2022

ISBN: (纸本)9789811979811

The proceedings contain 56 papers. The special focus in this conference is on Mobile Radio Communications and 5G Networks. The topics include: Predictive analysis of Air Pollutants Using Machine Learning;Machine Learning Techniques Applied of Land Use—Land Cover (LULC) image Classification: Research Avenues Challenges with Issues;crime analysis Using Computer Vision Approach with Machine Learning;natural Language Processing Implementation for Sentiment analysis on Tweets;VLSI Implementation of BCH Encoder with Triple DES Encryption for Baseband Transceiver;design and Implementation of image De-hazing Using Histogram Equalization;Improved Hybrid Unified Power Flow Controller Using Fractional Order PID Controlled Systems;Optimized Activation Function-Based SAR Ship Detection;framework for Implementation of Smart Driver Assistance System Using Augmented Reality;Power-Efficient Hardware Design of ECC Algorithm on High Performance FPGA;A Creative Domain of Blockchain Application: NFTs;sign Language recognition Using Machine Learning;an IoT-Based Health Monitoring System for Stress Detection in Human Beings;ioT-Based Driving pattern analysis and Engine Sensor Damage Prediction Using Onboard Diagnostics;Solving the Element Detecting Problem in Graphs via Quantum Walk Search Algorithm (QWSA);critical analysis of Secure Strategies Against Threats on Cloud Platform;pareto Optimal Solution for Fully Fuzzy Bi-criteria Multi-index Bulk Transportation Problem;automatic Candidature Selection by Artificial Natural Language Processing;elimination and Restoring Deduplicated Storage for Multilevel Integrated Approach with Cost Estimation;vulnerability Assessment of Cryptocurrency Wallet and Exchange Websites;greedy Theory Using Improved Performance Prim’s Algorithm, Big Bang Speedup of the Bellman–Ford Algorithm;performance analysis of High-Speed Optical Communication Systems Under the Impact of Four Wave Mixing.

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A Weakly-Supervised Approach for Layout analysis in Music Score images 11th

A Weakly-Supervised Approach for Layout Analysis in Music ...

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11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Ayllon, Eric Castellanos, Francisco J. Calvo-Zaragoza, Jorge Department of Software and Computing Systems University of Alicante Alicante Spain

ISBN: (纸本)9783031366154

In this paper, we propose a data-efficient holistic method for layout analysis in the context of Optical Music recognition (OMR). Our approach can be trained by just providing the number of staves present in the document collection at issue (weak label), thereby making it practical for real use cases where other fine-grained annotations are expensive. We consider a Convolutional Recurrent Neural Network trained with the Connectionist Temporal Classification loss function, which must retrieve a pretext sequence that encodes the number of staves per page. As a by-product, the model learns to relate every image row according to the presence or not of a staff. We demonstrate that our approach achieves performances close to the full supervised scenario on two OMR benchmarks, according to the eventual performance of the full transcription pipeline. We believe that our work will be useful for researchers working on music score recognition, and will open up new avenues for research in this field. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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On the Use of Transformers for End-to-End Optical Music recognition 10th

On the Use of Transformers for End-to-End Optical Music Reco...

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Rios-Vila, Antonio Inesta, Jose M. Calvo-Zaragoza, Jorge Univ Alicante UI Comp Res Alicante Spain

ISBN: (纸本)9783031048814;9783031048807

State-of-the-art end-to-end Optical Music recognition (OMR) systems use Recurrent Neural Networks to produce music transcriptions, as these models retrieve a sequence of symbols from an input staff image. However, recent advances in Deep Learning have led other research fields that process sequential data to use a new neural architecture: the Transformer, whose popularity has increased over time. In this paper, we study the application of the Transformer model to the end-to-end OMR systems. We produced several models based on all the existing approaches in this field and tested them on various corpora with different types of encodings for the output. The obtained results allow us to make an in-depth analysis of the advantages and disadvantages of applying this architecture to these systems. This discussion leads us to conclude that Transformers, as they were conceived, do not seem to be appropriate to perform end-to-end OMR, so this paper raises interesting lines of future research to get the full potential of this architecture in this field.

关键词： Optical Music recognition Transformers Connectionist Temporal Classification image-to-sequence

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