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检索条件"任意字段=13th International Conference on Computer Analysis of Images and Patterns"

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20th international conference on computer analysis of images and patterns, CAIP 2023

20th International Conference on Computer Analysis of Images...

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20th international conference on computer analysis of images and patterns, CAIP 2023

ISBN: (纸本)9783031442360

the proceedings contain 54 papers. the special focus in this conference is on international conference on computer analysis of images and patterns. the topics include: Empirical Study of Attention-Based Models for Automatic Classification of Gastrointestinal Endoscopy images;classification of Breast Micro-calcifications as Benign or Malignant Using Subtraction of Temporally Sequential Digital Mammograms and Machine Learning;fourier Descriptor Loss and Polar Coordinate Transformation for Pericardium Segmentation;stroke Risk Stratification Using Transfer Learning on Carotid Ultrasound images;A Comparative Study of Explainable AI models in the Assessment of Multiple Sclerosis;biometric Recognition of African Clawed Frogs;Teacher-Student Synergetic Knowledge Distillation for Detecting Alcohol Consumption in NIR Iris images;performance Assessment of Fine-Tuned Barrier Recognition Models in Varying Conditions;Keyrtual: A Lightweight Virtual Musical Keyboard Based on RGB-D and Sensors Fusion;classification of Honey Pollens with ImageNet Neural Networks;highly Crowd Detection and Counting Based on Curriculum Learning;defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space;unsupervised State Representation Learning in Partially Observable Atari Games;structural analysis of the Additive Noise Impact on the α-tree;Augmented Reality for Indoor Localization and Navigation: the Case of UNIPI AR Experience;a Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial images;Perceptual Light Field Image Coding with CTU Level Bit Allocation;a Comparative Performance Assessment of Different Video Codecs;race Bias analysis of Bona Fide Errors in Face Anti-spoofing;fall Detection with Event-Based Data: A Case Study;towards Accurate and Efficient Sleep Period Detection Using Wearable Devices;RLSTM: A Novel Residual and Recurrent Network for Pedestrian Action Classification;Temporal Sequences of EEG Covariance

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20th international conference on computer analysis of images and patterns, CAIP 2023

20th International Conference on Computer Analysis of Images...

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20th international conference on computer analysis of images and patterns, CAIP 2023

ISBN: (纸本)9783031442391

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19th international conference on computer analysis of images and patterns, CAIP 2021

19th International Conference on Computer Analysis of Images...

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19th international conference on computer analysis of images and patterns, CAIP 2021

ISBN: (纸本)9783030891305

the proceedings contain 87 papers. the special focus in this conference is on computer analysis of images and patterns. the topics include: Preface;simultaneous Bi-directional Structured Light Encoding for Practical Uncalibrated Profilometry;HPA-Net: Hierarchical and Parallel Aggregation Network for Context Learning in Stereo Matching;MTStereo 2.0: Accurate Stereo Depth Estimation via Max-Tree Matching;H-OCS: A Hybrid Optic Cup Segmentation of Retinal images;retinal Vessel Segmentation Using Blending-Based Conditional Generative Adversarial Networks;U-Shaped Densely Connected Convolutions for Left Ventricle Segmentation from CMR images;deep Learning Approaches for Head and Operculum Segmentation in Zebrafish Microscopy images;shape analysis Approach Towards Assessment of Cleft Lip Repair Outcome;MMEC: Multi-Modal Ensemble Classifier for Protein Secondary Structure Prediction;patch-Level Nuclear Pleomorphism Scoring Using Convolutional Neural Networks;automatic Myelofibrosis Grading from Silver-Stained images;Joint Global ICP for Improved Automatic Alignment of Full Turn Object Scans;a Deep Learning-Based Pipeline for Celiac Disease Diagnosis Using Histopathological images;HEp-2 Cell Image Recognition with Transferable Cross-Dataset Synthetic Samples;clinically Guided Trainable Soft Attention for Early Detection of Oral Cancer;small and Large Bile Ducts Intrahepatic Cholangiocarcinoma Classification: A Preliminary Feature-Based Study;A Review on Breast Cancer Brain Metastasis: Automated MRI Image analysis for the Prediction of Primary Cancer Using Radiomics;An Adaptive Semi-automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network;a three-Dimensional Reconstruction Integrated System for Brain Multiple Sclerosis Lesions;Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings;progressive Contextual Excitation for Smart Farming Application.

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19th international conference on computer analysis of images and patterns, CAIP 2021

19th International Conference on Computer Analysis of Images...

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19th international conference on computer analysis of images and patterns, CAIP 2021

ISBN: (纸本)9783030891275

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Method for Detecting Steganographic Changes in images Using Machine Learning 13

Method for Detecting Steganographic Changes in Images Using ...

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13th international conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Denysiuk, Dmytro Savenko, Oleg Lysenko, Sergii Savenko, Bohdan Kashtalian, Antonina Khmelnytskyi national university Department of Computer Engineering and Information Systems Khmelnytskyi Ukraine

ISBN: (纸本)9798350396119

this article presents a method for detecting steganographic changes in images using convolutional neural networks. the use of convolutional neural networks made it possible to automatically detect characteristic features and patterns in images, which ensured the high efficiency of the method. Dividing the image into small segments made it possible to search for changes with limited use of computing resources, in particular for the analysis of images on web resources, which can become part of a firewall to prevent steganographic changes. Experiments confirmed the high efficiency of the method in detecting steganographic changes, in particular when modifying images using various steganography methods. It is also found that the detection efficiency depends on the choice of database for training convolutional neural networks. © 2023 IEEE.

关键词： Image analysis

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Effective Adversarial Attacks on images by Class Activation Mapping 13

Effective Adversarial Attacks on Images by Class Activation ...

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13th international conference on Control, Automation and Information Sciences, ICCAIS 2024

作者： Etehadi-Abari, Mina Naghsh-Nilchi, Ahmad Reza Hoseinnezhad, Reza University of Isfahan Faculty of Computer Engineering Isfahan Iran School of Engineering Rmit University VIC Australia

ISBN: (纸本)9798331542047

Adversarial attacks on images, where intentional noise is added to deceive machine learning models, have emerged as both a significant security concern and a beneficial tool for en-hancing privacy, protecting intellectual property, and innovating in creative industries. this paper introduces a novel information-theoretic approach to crafting effective adversarial attacks, fo-cusing on parts of the image that contain information relevant to decision-making processes in typical deep learning models for object detection and classification. Our method, Fisher-CAM, generates class activation maps (CAM) using Fisher information to identify significant regions in input images. the adversarial noise is crafted by augmenting images based on these regions and iteratively updating perturbation patterns through gradient calculation and momentum updates. Extensive experiments on a subset of the ImageNet dataset demonstrate that our method surpasses state-of-the-art attack performance in both white-box and black-box settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Imaginations Generate images for Multi-modal Machine Translation 1

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13th international conference on computer Engineering and Networks (CENet)

作者： Yang, Xiaona Sun, Wenli Wei, Wei Li, Yinlin Shi, Xiayang Zhengzhou Univ Light Ind Zhengzhou Peoples R China Wuhan Second Ship Design & Res Inst Wuhan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9789819992430

ISBN: (纸本)9789819992454;9789819992430;9789819992423

Multi-modal machine translation (MMT) aims at exploring better translation systems by integrating the visual annotation which presents the content described in the bilingual parallel sentence pair into the conventional only-text neural machine translation (NMT). However, existing methods heavily rely on the manual annotated images data set. the cost of manual image annotation is relatively high at this stage. In this paper, we propose the generative imagination network with transformer to automatically generate visual annotations semantic-equivalent with source and target sentences. the proposed model receives the inputs of source-target bilingual sentences and generates visual annotations for MMT. Experiments analysis demonstrate that our model can generate high-quality annotated images and prompt the performance of MMT. Additionally, we use our model to generate annotated images for a famous English-German IWSLT-2015, the experimental results show the improvement for MMT.

关键词： Multi-modal machine translation Visual annotation

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A Framework for Insect Detection and Classification in Paddy images 13

A Framework for Insect Detection and Classification in Paddy...

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13th international conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Vinodhkumar, Bhuvana Ravi, A. Sivakumar, Sasikala Manmull Jain College Dept. of Computer Science Agurchand Chennai61 India

ISBN: (纸本)9798350380583

India, known for its heavy reliance on agriculture with 70% of its population engaged in farming, places great importance on crops like paddy. However, agricultural productivity faces challenges due to uncontrollable factors such as rainfall patterns, temperature fluctuations, and overall climate conditions. Among these challenges, insect infestations pose a significant threat to crop yields. To address this issue, there is a growing need for innovative solutions such as automated agricultural systems that can accurately identify and manage plant pests through image analysis techniques. In this context, the integration of advanced features like BAG and SURF, along with classifiers such as SVM and k-NN, is pivotal. the performance evaluation of the IR2PI system reveals that employing the k-NN classifier leads to a remarkable 95% accuracy, showcasing its potential for enhancing agricultural productivity and sustainability. © 2024 IEEE.

关键词： Livestock

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Candlestick Pattern Recognition Using Modified YOLOv8 in Financial Market analysis 13

Candlestick Pattern Recognition Using Modified YOLOv8 in Fin...

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13th IEEE international conference on Communication Systems and Network Technologies, CSNT 2024

作者： Sonu thakur, Ankita Kumar, Rajeev National Institute of Technology Department of Computer Science Himachal Pradesh Hamirpur India

ISBN: (纸本)9798350305463

Pattern recognition in candlestick charts poses a formidable challenge due to the intricate shapes and intrinsic noise in financial data. this study addresses the critical need of a dataset to accurate pattern identification, with a specific focus on four highly used patterns: Head and Shoulder, Reverse Head and Shoulder, Double Top, and Double Bottom. the proposed modified YOLOv8 model excels in detecting patterns, achieving a mAP@50 value of 86.1%. the model's stability and consistency across epochs underscore its potential for real-time financial market analysis and short-term trading applications. this work pioneers the application of Object Detection models as an effective strategy for addressing pattern recognition challenges in financial markets, promising advancements in technical analysis. © 2024 IEEE.

关键词： Object detection

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13th international conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) Held as Part of EvoStar conference13th international conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART)

作者： McCormack, Jon Llano, Maria Teresa Krol, Stephen James Rajcic, Nina Monash Univ SensiLab Caulfield Vic 3145 Australia

ISBN: (数字)9783031569920

ISBN: (纸本)9783031569913;9783031569920

Image generation using generative AI is rapidly becoming a major new source of visual media, with billions of AI generated images created using diffusion models such as Stable Diffusion and Midjourney over the last few years. In this paper we collect and analyse over 3 million prompts and the images they generate. Using natural language processing, topic analysis and visualisation methods we aim to understand collectively how people are using text prompts, the impact of these systems on artists, and more broadly on the visual cultures they promote. Our study shows that prompting focuses largely on surface aesthetics, reinforcing cultural norms, popular conventional representations and imagery. We also find that many users focus on popular topics (such as making colouring books, fantasy art, or Christmas cards), suggesting that the dominant use for the systems analysed is recreational rather than artistic.

关键词： Generative AI Prompting Visual Arts & Culture

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