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A Novel Approach of Multi-string Parsing for Syntactic pattern recognition 13th

A Novel Approach of Multi-string Parsing for Syntactic Pat...

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13th international conference on image processing and Communications, IP and C 2023, 13th international conference on computer recognition Systems, CORES 2023

作者： Flasiński, Mariusz Jurek, Janusz AGH University of Science and Technology al. A. Mickiewicza 30 Kraków30-059 Poland Information Technology Systems Department Jagiellonian University ul. prof. St. Lojasiewicza 4 Kraków30-348 Poland

ISBN: (纸本)9783031416293

A novel approach to syntactic pattern recognition based on multi-string parsing is introduced in the paper. A methodological motivation of the research, which has resulted from application-oriented requirements, is presented. Formal foundations, an architectural model and a generic recognition algorithm of the approach are introduced. the approach allows one to recognize complex objects which should be represented by a series of images instead of one image. the series of images is represented with a sequence of aspectual patterns which are treated as a holistic aspect-integrated description of a recognized object. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Syntactics

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Robot Debater: Debate-styled Text Auto-generation System Based on Large Foundation Language Models 4

Robot Debater: Debate-styled Text Auto-generation System Bas...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Zhu, Yu Ling, Yijun Ling, Xufeng Yang, Jie Shanghai China Shanghai Xiangming High School Shanghai China Shanghai Normal University Tianhua College School of Artificial Intelligence Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9798350324303

We use a large foundation language model, which is fine-tuned with debate corpora, to develop a robot debater application. To address the limitations of requiring immense computational power in large base language models, this study takes advantage of the Low Rank Adaption characteristic prevalent in domain expert knowledge. By applying Low Rank Adaption technology and fine-tuning with a dedicated dataset, the computational load is reduced to just one-thousandth of what is needed for a large language model, greatly expanding the application scenarios of robot debaters using large language models. In view of the characteristics of debate competitions, this model can preset a variety of debate scenarios and supports personalized debate processes. It employs intelligent voice recognition technology combined with a multi-channel voice input method, allowing for precise localization of different human debaters and improving the accuracy of voice input recognition. the system can support multiple large-scale language generation models and utilize various different voice broadcasting systems, including male and female voice styles, as well as a range of voice emotions. this model can be applied to debate competitions held in universities, high schools, and various industries. It can support human-machine debates as well as machine-to-machine debates. © 2023 IEEE.

关键词： Speech recognition

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Manipulation Mask Generator: High-Quality image Manipulation Mask Generation Method Based on Modified Total Variation Noise Reduction 4

Manipulation Mask Generator: High-Quality Image Manipulation...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Yang, Xinyu Zhou, Jizhe Sichuan University School of Computer Science Chengdu China

ISBN: (纸本)9798350324303

In artificial intelligence, any model that wants to achieve a good result is inseparable from a large number of high-quality data. It is especially true in the field of tamper detection. this paper proposes a modified total variation noise reduction method to acquire high-quality tampered images. We automatically crawl original and tampered images from the Baidu PS Bar. Baidu PS Bar is a website where net friends post countless tampered images. Subtracting the original image with the tampered image can highlight the tampered area. However, there is also substantial noise on the final print, so these images can't be directly used in the deep learning model. Our modified total variation noise reduction method is aimed at solving this problem. Because a lot of text is slender, it is easy to lose text information after the opening and closing operation. We use MSER (Maximally Stable Extremal Regions) and NMS (Non-maximum Suppression) technology to extract text information. And then use the modified total variation noise reduction technology to process the subtracted image. Finally, we can obtain an image with little noise by adding the image and text information. And the idea also largely retains the text information. Datasets generated in this way can be used in deep learning models, and they will help the model achieve better results. © 2023 IEEE.

关键词： Noise abatement

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Are Layout Analysis and OCR Still Useful for Document Information Extraction Using Foundation Models? 18th

Are Layout Analysis and OCR Still Useful for Document Inform...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Scius-Bertrand, Anna Fakhari, Atefeh Vogtlin, Lars Cabral, Daniel Ribeiro Fischer, Andreas Univ Fribourg Fribourg Switzerland Univ Appl Sci & Arts Western Switzerland Delemont Switzerland

ISBN: (纸本)9783031705458;9783031705465

With the advent of end-to-end models and the remarkable performance of foundation models, the question arises regarding the relevance of preliminary steps, such as layout analysis and optical character recognition (OCR), for information extraction from document images. We attempt to provide some answers through experiments conducted on a new database of food labels. the goal is to extract nutritional values from cellphone pictures taken in grocery stores. We compare the results of OCR-free models that take the raw images as input (Donut and GPT-4-vision) with two-stage systems that first perform OCR and then extract information using large language models (LLMs) from the recognized text (Mistral, GPT-3, and GPT-4). To assess the impact of layout analysis, we applied the same systems to three different views of the image: the original full image, a large manual crop containing the entire food label, and a small crop focusing on the relevant nutrition information. Comparative experiments are also conducted on the CORD database of receipts. Our results demonstrate that although OCR-free models achieve a remarkable performance, they still require some guidance regarding the layout, and two-stage systems achieve better results overall.

关键词： Document image Analysis Information Extraction OCR Layout Analysis Deep Learning Transformers Large Language Models

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4th international conference on Recent Trends in Advanced Computing - computer vision and Machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

4th International Conference on Recent Trends in Advanced Co...

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4th international conference on Recent Trends in Advanced Computing - computer vision and Machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

ISBN: (纸本)9789811971686

the proceedings contain 30 papers. the special focus in this conference is on Recent Trends in Advanced Computing - computer vision and Machine Intelligence Paradigms for Sustainable Development Goals. the topics include: Advanced Algorithmic Techniques for Topic Prediction and Recommendation—An Analysis;Implementation of an Automatic EEG Feature Extraction with Gated Recurrent Neural Network for Emotion recognition;high Performance Classifier for Brain Tumor Detection Using Capsule Neural Network;Mining Suitable Symptoms to Identify Disease Using Apriori and NBC;background Features-Based Novel Visual Ego-Motion Estimation;livspecs: Design and Implementation of Smart Specs for Hearing and Visually Challenged Persons;Self-balancing Robot Using Arduino and PID Controller;a Survey Based on Online Voting System Using Blockchain Technology;convergence Perceptual Model for Computing Time Series Data on Fog Environment;survey on Collaborative Filtering Technique for Recommender System Using Deep Learning;a Survey on Power Consumption Indicator Using Machine Learning-Based Approach;a Novel Hand Gesture recognition for Aphonic People Using Convolutional Neural Network;comprehensive Analysis of Defect Detection through image processing and Machine Learning for Photovoltaic Panels;covid Analysis Prediction Using Densenet Method in Deep Learning;Feature Extraction Based on GLCM and GLRM Methods on COVID-19 Dataset;memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks;Enhancing Protection Against Scalper Bots with ML;An Enhanced Optimized Abstractive Text Summarization Traditional Approach Employing Multi-layered Attentional Stacked LSTM with the Attention RNN;implementation of Machine Learning Methods on Data to Analyze Emotional Health;SMS Spam Classification Using PSO-C4.5.

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Towards IoT Context Data Analysis and Classification 4

Towards IoT Context Data Analysis and Classification

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4th international Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Bandason, Vivian Sibanda, Khulumani Tamuka, Nyashadzashe University of Fort Hare Department of Computer Science Eastern Cape Alice South Africa Walter Sisulu University Department of Applied Informatics Eastern Cape East London South Africa

ISBN: (纸本)9798350387988

the capability of generating and acquiring data has never been as pronounced as today since the advent of Information Technology in the early 19th century. Big Data is a growing trend where data processing has grown so much that it is beyond the ability of commonly used software tools to capture, manage, and analyse this data. the most fundamental challenge for the Big Data applications is to explore the large volumes of data and extract useful knowledge. Another recent development furthering the accumulation of data is the Internet of things - wherein the amount of 'things' housing different sensors has multiplied. the Internet of things (IoT) is built on sensors and actuators, which send out large quantities of data about their state and the environment. Data processing techniques can be utilized to extract relevant insights from this treasure of data and information that is generated in IoT environments. this research investigates how sensor data from user devices can be used to determine context information about a user. As far as the classification of daily human activities (such as walking, eating, driving and sleeping) is concerned, several studies have proposed traditional computer vision methods. Unlike traditional computer vision methods, which pose mobility limitations, computational complexity, and privacy issues, our approach leverages mobile sensor data for real-time activity recognition. three classification algorithms which are the K-Nearest Neighbours (KNN), Decision Trees, and Support Vector Machines (SVM) were evaluated based on their accuracy in classifying human activities. Findings revealed that the KNN was superior to other algorithms in providing a promising approach for context-aware systems in IoT environments. this study highlights the potential of Machine Learning and sensor data in enhancing user-centric applications through improved context-awareness. © 2024 IEEE.

关键词： Data assimilation

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the Research on Malicious Load recognition Based on Principal Component Analysis 4

The Research on Malicious Load Recognition Based on Principa...

引用

4th international conference on Electronic Information Engineering and computer Technology, EIECT 2024

作者： Nie, Zhangzhen Chen, Yunhe School of Computer and Information Engineering Tianjin Normal University Tianjin China

ISBN: (纸本)9798331528850

As household electrical loads become increasingly diverse and electricity demand continues to rise, the identification of malicious loads has become a critical area of focus in smart grid research. Existing approaches primarily concentrate on current signal processing and the application of machine learning algorithms. Existing approaches, however, face limitations when dealing with complex nonlinear relationships and noise interference, particularly in diverse load environments. To address these challenges, this paper introduces a novel method for malicious load identification based on Principal Component Analysis (PCA) and waveform image generation. the primary innovation of this approach lies in the transformation of current data into waveform images, facilitating enhanced feature extraction and classification. First, the current data is converted into waveform images to visually represent malicious load characteristics. Due to the high dimensionality and redundancy, PCA is used for dimensionality reduction, preserving key features while removing noise and improving efficiency. Given the small sample size and complexity of malicious load data, the K-nearest neighbor (KNN) algorithm is applied for classification. KNN excels in small-sample environments by classifying based on similarity, making it ideal for handling the non-linear characteristics of malicious loads. the combination of PCA and KNN enables effective identification of malicious loads in high-dimensional, small-sample data. Experimental validation demonstrates that the proposed method delivers superior classification performance, achieving an accuracy of 0.9845, significantly surpassing the SVM classifier's 0.9617. these findings underscore the robustness and effectiveness of the waveform image-based approach for malicious load identification. the method offers promising new insights for smart grid monitoring and represents a significant advancement in the accurate and efficient identification of mali

关键词： Dimensionality reduction

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UAV image object detection based on improved Yolov8 4

UAV image object detection based on improved Yolov8

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4th international conference on image processing and Intelligent Control, IPIC 2024

作者： Zhang, Wei Wang, Jiaxin Wang, Dan Kong, Xiangwei Institute of Measurement and Testing Technology Chinese Flight Test Establishment Xi'an710089 China

ISBN: (纸本)9781510682313

In a variety of UAV application scenarios, such as agricultural monitoring, urban safety and traffic management, high-precision image object detection is crucial for real-time analysis and decision-making. Traditional object detection algorithms are often ineffective in dealing with small objects in complex environments. To this end, this paper proposes an improved YOLOv8 model that integrates a NAM (Normalization-based Attention Module) attention mechanism and a Bidirectional Feature Pyramid Network (BiFPN) specifically to optimize the object detection performance of UAV images. the NAM module enhances the model's spatial attention capability by utilizing a normalization process that this enables the model to adaptively emphasize important feature regions in the image, which significantly improves the recognition accuracy of small-scale objects without adding too much computational burden. BiFPN further optimizes the flow of information between different scales, which improves the efficiency of feature utilization and the overall performance of detection. Extensive experiments on the VisDrone2019 dataset show that the improved YOLOv8 model improves the mAP metrics by 11.3% compared to the original model, and performs especially well in scenes with complex backgrounds and the presence of multi-scale objects. this study not only validates the potential application of NAM in UAV vision tasks, but also provides a new technical path for the implementation of deep learning models in real monitoring systems. © 2024 SPIE.

关键词： image analysis

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Identification and Classification of Rare Medicinal Plants 4

Identification and Classification of Rare Medicinal Plants

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4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Bora, Piyush Singh Sharma, Shamneesh Batra, Isha Malik, Arun Ashfaq, Farzeen School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success upGrad Education Private Limited upGrad Campus Bangalore India School of Computer Science Taylor's University Subang Jaya Malaysia

ISBN: (纸本)9798350353266

the identification and conservation of rare medicinal plants are crucial, given their invaluable role in healthcare, pharmaceuticals, and biodiversity preservation. Numerous varieties face extinction according to the international Union for Conservation of Nature (IUCN), necessitating advanced image processing and computer vision techniques for accurate identification and documentation. this paper presents a novel deep learning approach leveraging convolutional neural networks (CNNs) and transfer learning to adapt the MobileNet V2 architecture for automated identification and classification of rare medicinal plants found in different areas, utilizing a curated dataset of 6,900 images across 80 distinct classes. the robust deep learning model extracts discriminative visual features from digital images, facilitating accurate taxonomic classification, and outperforming traditional techniques and shallow models. the proposed system empowers stakeholders with reliable accessible tools and plays a pivotal role in biodiversity conservation and sustainable utilization, ensuring nature's therapeutic bounty for generations. © 2024 IEEE.

关键词： Plant diseases

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PT-MVSNet: Overlapping Attention Multi-view Stereo Network with Transformers 4

PT-MVSNet: Overlapping Attention Multi-view Stereo Network w...

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4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Zhang, Song Li, Lin Qiao, Jiangxuan School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Qinghai Normal University Academy of Plateau Science and Sustainability Xining China National Qinghai-Tibet Plateau Scientific Data Center Qinghai Sub-center Xining China

ISBN: (纸本)9798350326444

In this paper, we propose a new multi-view stereo vision model PT -MVSNet based on multi-view stereo (MVS). Multi-view stereo is a successful reconstruction method that uses multiple images to reconstruct a 3D scene. It has been applied in many practical scenes such as architecture, cultural heritage protection, and map making. MVS still faces a lot of challenges, including inaccurate feature matching, excessive image noise, and overly complex computation. To solve the feature-matching inaccuracy problem, we take the Transformer model as the main structure in the feature-matching and add a patch-based overlap attention module (POLA). In this paper, we proposed PT-MVSNet can solve the image feature extraction problem more effectively. To validate the effectiveness of the model, we conducted experiments on the DTU dataset and evaluated its performance by two evaluation metrics. the experiment results show that our method outperforms the latest methods, whose accuracy and completeness reach 0.386 and 0.271 respectively. © 2023 IEEE.

关键词： Feature extraction

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