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image Reconstruction Utilizing Algebraic Helmoltz Inversion and Passband Filtering Applied to Viscoelasticity 3

Image Reconstruction Utilizing Algebraic Helmoltz Inversion ...

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3rd International conference on Multimedia analysis and pattern recognition, MAPR 2020

作者： Quang-Huy, Tran Duong, Van Tu Hai, Luong Quang Duc-Tan, Tran Hanoi Pedagogical University 2 Faculty of Physics Hanoi Viet Nam Ntt Hi-Tech Insitute Nguyen Tat Thanh University Ho Chi Minh City Viet Nam Le Quy Don Technical University Faculty of Control Engineering Hanoi Viet Nam Phenikaa University Faculty of Electrical and Electronic Engineering Hanoi Viet Nam

ISBN: (纸本)9781728165554

Shear wave viscoelasticity imaging is a quick and convenient imaging model for collecting images of tissues. The mechanical properties of soft tissues (elasticity, viscosity) are expressed through the complex shear modulus (CSM) parameter, a useful information for tissue pathological diagnosis. In this paper, we proposed a new solution in which we use FDTD method for modeling the wave propagation in 2D environment using FDTD method;then apply the a band-pass filter (BPF) to reduce the noise from the measured particle velocity of the shear wave;finally, we use the Algebraic Helmholtz Inversion (AHI) algorithm to directly estimate CSM. The results of numerical simulation have proven the effectiveness of the proposed algorithm. © 2020 IEEE.

关键词： Viscosity

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Blind Robust 3-D Mesh Watermarking Based on Mesh Saliency and QIM Quantization for Copyright Protection 9th

Blind Robust 3-D Mesh Watermarking Based on Mesh Saliency an...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Hamidi, Mohamed Chetouani, Aladine El Haziti, Mohamed El Hassouni, Mohammed Cherifi, Hocine Mohammed V Univ Fac Sci Rabat IT Ctr LRIT CNRST URAC29 Rabat Morocco Univ Orleans PRISME Lab Orleans France Mohammed V Univ FLSH Rabat IT Ctr LRIT CNRST URAC29 Rabat Morocco Univ Bourgogne LIB EA 7534 Dijon France

ISBN: (纸本)9783030313326;9783030313319

Due to the recent demand of 3-D models in several applications like medical imaging, video games, among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased considerably. The majority of robust 3-D watermarking techniques have essentially focused on the robustness against attacks while the imperceptibility of these techniques is still a real issue. In this context, a blind robust 3-D mesh watermarking method based on mesh saliency and Quantization Index Modulation (QIM) for Copyright protection is proposed. The watermark is embedded by quantifying the vertex norms of the 3-D mesh using QIM scheme since it offers a good robustness-capacity tradeoff. The choice of the vertices is adjusted by the mesh saliency to achieve watermark robustness and to avoid visual distortions. The experimental results show the high imperceptibility of the proposed scheme while ensuring a good robustness against a wide range of attacks including additive noise, similarity transformations, smoothing, quantization, etc.

关键词： 3-D mesh watermarking Quantization Index Modulation (QIM) Copyright protection Mesh saliency

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Application analysis of Face recognition Technology Based on Computer Vision

Application Analysis of Face Recognition Technology Based on...

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Science and Technology Innovation (IAECST), International Academic Exchange conference on

作者： Chenran Liu Junting Liu School of Electrical Engineering and Automation Anhui University Hefei China Tongling People's Hospital Tongling China

ISBN: (纸本)9781665402682

With the development of society, all parties are facing increasingly urgent requirements for fast and effective automatic identity verification. Since biological characteristics are intrinsic properties of humans, they have strong stability and individual differences, so they are an ideal basis for identity verification. Compared with biometrics such as fingerprints and iris, face recognition is more direct, friendly, and convenient to be accepted by users. Therefore, face recognition has become a current research hotspot in the field of pattern recognition and artificial intelligence. As an important part of human-computer interaction, the research of face detection and recognition technology has great theoretical significance and application value. In this paper, the face detection method based on skin color information and the face recognition algorithm based on the embedded hidden Markov model are deeply studied, and the development of the development of the face detection and recognition system, performance analysis and specific applications are done. Introduced. In this paper, the detection and positioning of the face and the extraction of facial features are carried out on the frontal color image: the feature vector extraction of the detected face region using the two-dimensional discrete cosine transform and the method based on the embedded hidden Markov model are proposed. Face recognition method. Experiments show that the method of using the embedded hidden Markov model based on two-dimensional discrete cosine transform for face recognition can make better use of the two-dimensional statistical characteristics of face images, and has high recognition efficiency and very high recognition efficiency. Good robustness; researched on the recognition of hand-drawn portraits of human faces, and proposed a novel algorithm for converting photo images into portraits; applied the face detection and recognition system to specific practices, and established An intellig

关键词： Technological innovation image recognition Face recognition System performance Hidden Markov models Feature extraction Stability analysis

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Revealing Mobility Regularities in Urban Rail Systems 11

Revealing Mobility Regularities in Urban Rail Systems

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11th International conference on Ambient Systems, Networks and Technologies (ANT) / 3rd International conference on Emerging Data and Industry 4.0 (EDI)

作者： Jiang, Wenhua Ma, Zhenliang Kim, Inhi Lee, Seonha Monash Univ Inst Transport Studies 23 Coll Walk Clayton Vic 3800 Australia Kongju Natl Univ Dept Civil & Environm Engn Cheonhasi 330717 Chungnam South Korea

Studying mobility patterns in public transport systems is critical for multiple applications from strategic planning to operations control to information provision. Unveiling and understanding the underlying (unobserved) mechanism governing the generation of (observed) mobility patterns are challenging. Considering the recurrent human travel activities and system operations, typical mobility regularities (or variations) patterns of the system can be captured by travel demand. This paper proposes a new paradigm to investigate the regularity in macroscopic mobility patterns for urban rail applications using concepts from image processing and pattern recognition to identify distinct demand patterns. We analyse the within-day demand patterns and day-to-day comparisons by constructing the spatiotemporal eigen-demand images (faces). The case study showed that the entry demand of Hong Kong metro network over 6 months can be grouped into 5 distinct clusters. This new perspective of eigen-demand allows examining the internal essence of large amounts of mobility patterns over time and reveals a global daily demand pattern at the entire system. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Pulic transport eigen-demand principal component analysis clustering

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3rd International conference on Advanced Computing and Intelligent Engineering, ICACIE 2018

3rd International Conference on Advanced Computing and Intel...

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3rd International conference on Advanced Computing and Intelligent Engineering, ICACIE 2018

ISBN: (纸本)9789811510809

The proceedings contain 54 papers. The special focus in this conference is on Advanced Computing and Intelligent Engineering. The topics include: Wavelet Transform Domain Methods for Resolution Enhancement of Satellite images;improving Query Results in Ontology-Based Case-Based Reasoning by Dynamic Assignment of Feature Weights;a Survey on Representation for Itemsets in Association Rule Mining;efficient Clustering Using Nonnegative Matrix Factorization for Gene Expression Dataset;design of Random Forest Algorithm Based Model for Tachycardia Detection;detection of Spam in YouTube Comments Using Different Classifiers;deep Learning Architectures for Named Entity recognition: A Survey;effect of Familiarity on recognition of Pleasant and Unpleasant Emotional States Induced by Hindi Music Videos;early Detection of Breast Cancer Using Support Vector Machine With Sequential Minimal Optimization;the Case Study of Brain Tumor Data analysis Using Stata and R;predictive Data Analytics for Breast Cancer Prognosis;a Semantic Approach of Building Dynamic Learner Profile Model Using WordNet;prioritizing Public Grievance Redressal Using Text Mining and Sentimental analysis;an Automatic Summarizer for a Low-Resourced Language;printed Odia Symbols for Character recognition: A Database Study;importance of Data Standardization Methods on Stock Indices Prediction Accuracy;Feature Relevance analysis and Feature Reduction of UNSW NB-15 Using Neural Networks on MAMLS;video Summarization Based on Optical Flow;Decision Support System for Black Classification of Dental images Using GIST Descriptors;clustering Performance analysis;pattern analysis of Brain Functional Connectivity Parameters After Removal of Artifactual Motifs from EEG During Meditation;Hyper-heuristic image Enhancement (HHIE): A Reinforcement Learning Method for image Contrast Enhancement.

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A Comprehensive analysis of Information Security Policy and Security Program Development 28th

A Comprehensive Analysis of Information Security Policy and ...

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28th International conference on image Processing, Computer Vision, and pattern recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Jackson, Salim Dunn, Willis Olatunbosun, Samuel Norfolk State University NorfolkVA United States

ISBN: (纸本)9783031859328

This study examines in-depth the intricate realm of privacy-preserving technologies in the corporate sector, drawing inspiration from the fundamental tenets of Information Security policy and Security Program Development. It incorporates information from various sources to offer a thorough grasp of the effective implementation of information security policies and the strategic development of security programs. The findings underscore the significance of adherence to the ISO/IEC 27001 standard, which offers a consistent and internationally recognized framework for strengthening policy development and safeguarding sensitive data globally. Furthermore, the study emphasizes the importance of cultivating a cybersecurity mindset, particularly in remote work environments, and the necessity for employees to comply with policies to mitigate emerging cyber risks. Despite notable achievements, ongoing challenges persist in policy implementation and security program development. Continuous innovation and adaptation are required to effectively address the evolving cybersecurity threat landscape. The research offers practical recommendations that empower businesses in strengthening their security postures. These recommendations encompass adherence to international standards, fostering a robust cybersecurity culture, conducting regular assessments, encouraging innovation, and establishing Security Operations Centers. This research significantly enhances the understanding of privacy-preserving technologies and provides practical guidance for businesses in navigating the multifaceted challenges in managing information security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sensitive data

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Impact of Ultrasound image Reconstruction Method on Breast Lesion Classification with Deep Learning 9th

Impact of Ultrasound Image Reconstruction Method on Breast L...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Byra, Michal Sznajder, Tomasz Korzinek, Danijel Piotrzkowska-Wroblewska, Hanna Dobruch-Sobczak, Katarzyna Nowicki, Andrzej Marasek, Krzysztof Polish Acad Sci Inst Fundamental Technol Res Dept Ultrasound Warsaw Poland Polish Japanese Acad Informat Technol Dept Multimedia Warsaw Poland

ISBN: (纸本)9783030313326;9783030313319

In this work we investigate the usefulness and robustness of transfer learning with deep convolutional neural networks (CNNs) for breast lesion classification in ultrasound (US). Deep learning models can be vulnerable to adversarial examples, engineered input image pixel intensities perturbations that force models to make classification errors. In US imaging, distribution of US image pixel intensities relies on applied US image reconstruction algorithm. We explore the possibility of fooling deep learning models for breast mass classification by modifying US image reconstruction method. Raw radio-frequency US signals acquired from malignant and benign breast masses were used to reconstruct US images, and develop classifiers using transfer learning with the VGG19, InceptionV3 and InceptionResNetV2 CNNs. The areas under the receiver operating characteristic curve (AUCs) obtained for each deep learning model developed and evaluated using US images reconstructed in the same way were equal to approximately 0.85, and there were no associated differences in AUC values between the models (DeLong test p-values > 0.15). However, due to small modifications of the US image reconstruction method the AUC values for the models utilizing the VGG19, InceptionV3 and InceptionResNetV2 CNNs significantly decreased to 0.592, 0.584 and 0.687, respectively. Our study shows that the modification of US image reconstruction algorithm can have significant negative impact on classification performance of deep models. Taking into account medical image reconstruction algorithms may help develop more robust deep learning computer aided diagnosis systems.

关键词： Adversarial attacks Breast lesion classification Computer aided diagnosis Deep learning Robustness Ultrasound imaging Transfer learning

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Uncertainty Estimation for Black-Box Classification Models: A Use Case for Sentiment analysis 9th

Uncertainty Estimation for Black-Box Classification Models: ...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Mena, Jose Brando, Axel Pujol, Oriol Vitria, Jordi Eurecat Ctr Tecnol Catalunya Barcelona Spain BBVA Analyt Data & Analyt Madrid Spain Univ Barcelona Barcelona Spain

ISBN: (纸本)9783030313326;9783030313319

With the advent of new pre-trained word embedding models like ELMO, GPT or BERT, that leverage transfer-learning to deliver high-quality prediction systems, natural language processing (NLP) methods are reaching or even overtaking human baselines in some applications. The basic principle of these successful models is to train a model to solve a given NLP task, mainly Language Modelling, using significant volumes of data like the whole Wikipedia. The model is then fine-tuned to solve another NLP task, requiring fewer domain-specific data to achieve state-of-the-art accuracies. The method proposed in the present work assists the practitioner in evaluating the quality of the transferred classification models when applied to new data domains. In this case, we consider the original model as a black box. No matter how complex the original model may be, the method only requires access to the output layer to train a measure of the uncertainty associated with the predictions of the original model. This measure of uncertainty is a measure of how well the black-box model accommodates to the new data. Later on, we show how a rejection system can use this uncertainty to improve its accuracy, effectively enabling the practitioner to find the best trade-off between the quality of the model and the number of rejected cases.

关键词： Sentiment analysis Transfer learning Uncertainty estimation Natural Language Processing

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Seismic image pattern analysis using fuzzy logic controller 3

Seismic image pattern analysis using fuzzy logic controller

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3rd International conference on Intelligent Sustainable Systems, ICISS 2020

作者： Jayachitra, S. Kumar, M. Arun Aravindh, G. Sangeetha, R. Sasikala, P. Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Coimbatore India Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9781728170893

Seismic wave is the wave of energy that travels via the earth as a consequence of an earthquake or a volcano that occurs with low-frequency acoustic energy. By analyzing the Seismic earthquake patterns it is possible to find whether the earthquake will occur or not. In this project, the seismic data are analyzed by a Fuzzy Logic Controller. But the seismic data are affected by some unwanted artifacts. The de-noising method has the capacity to reduce the artifacts. Here the Contourlet transforms scheme gives better noise suppression in seismic images and the signal to noise ratio is improved. Further, the de-noised seismic image is segmented using Wavelet transforms for feature extraction. The extracted features can be analyzed by fuzzy Logic Controller which shows whether the seismic image is normal or abnormal. This analysis gives better results in the earthquake analysis and also in the construction of sensitive power plants. © 2020 IEEE.

关键词： image enhancement

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Logic-Based Neural Network for image Compression Applications

Logic-Based Neural Network for Image Compression Application...

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IEEE Middle East and North Africa Communications conference (MENACOMM)

作者： Ziad Doughan Rola Kassem Ahmad M. El-Hajj Ali M. Haidar Beirut Arab University Debbieh Lebanon

ISBN: (纸本)9781665434447

This paper presents a new lossy image compression technique using Logic-based Weightless Neural Networks, which underwrite two novel network architectures. The system endorses three processing phases, image optimization, inflation, and skimming. This research demonstrates an untraditional approach of auto-compression network guided by horizontal and vertical pixel intensity wavering trend. The performance of this new approach aligns with human’s perception of singularities in a certain pattern. The potential of trend analysis in image compression incorporates with information storage techniques and knowledge accumulation. The weightless network models generate images underlying enough distinct features that preserve the originality of a particular pattern but give superior levels of compression.

关键词： Deep learning image coding image edge detection Neural networks Network architecture Market research Convolutional neural networks

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