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OCF-Layout2Img: Object-Wise Constraint Free Layout to Image Generation 23

OCF-Layout2Img: Object-Wise Constraint Free Layout to Image ...

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15th international conference on Digital Image Processing, ICDIP 2023

作者： Lin, Xiao Liu, Bin He, Zhifen Wang, Kang Li, Bo School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University China Jiangxi Science and Technology Infrastructure Center Jiangxi Computing Center China

ISBN: (纸本)9798400708237

Complex scene generation is an important and challenging image synthesis task. Though latent space based conditional generative methods get impressive results, the accurate locating of objects for more detailed situation and editable contiguous synthesis are still a snag. In addition, previous methods with spatial awareness are easy to lose the location information after a series fusion operation. Focusing on such kind of problems, in this paper we propose an object-wise constraint free and location-lossless way of layout embedding by combining VAE and recent high performance VQGAN to achieve a location accurate and stable synthesis for layouts of complex scene. Instead of conditioning on layout, we apply a pretrained VQGAN to define the latent space and project every region of layout to image latent space directly by aligning their distribution with a novel light Cross-VAE structure. Then the quantization and decoder of VQGAN are reused for synthesis. This VAE plugged into VQGAN framework, which is called Layout-DVGAN in this paper. Experimental results demonstrate our method has competitive performance compared with the approaches which applied explicit spatial awareness way. © 2023 ACM.

关键词： Location

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A Unified Framework for Face Re-Identification Using Entry-Exit Cameras

A Unified Framework for Face Re-Identification Using Entry-E...

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computing for Sustainability and Intelligent Future (COMP-SIF), international conference on

作者： Suhas A Shashi Kumar S S Suraj Varma Praveen Kumar B G RNS Institute of Technology Bengaluru Karnataka India Schmid Vision Soft Bengaluru Karnataka India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Robust re-identification of individuals across multiple cameras has become a fundamental requirement in modern security and surveillance. To address this, we propose an effective system that ensembles detection, tracking, feature extraction, and retrieval mechanisms into a unified pipeline. By leveraging lightweight models, the system minimizes computational overhead and functions effectively on surveillance cameras. The system architecture enables processing and reliable identification, making it suitable for real-time applications. Our approach involves logging entry/exit registries and matching exits with entries through a re-identification pipeline, which allows security personnel to monitor an individual's entry and exit times within a secured premise. We are also releasing an open-source test video dataset with annotations, which is helpful for evaluating the face re-identification approaches for entry-exit cameras. Our approach performed good while considering matching of exit and entry events. The registered persons were identified with much better accuracy considering entry/exit events.

关键词： Accuracy Annotations Face recognition Surveillance Computational modeling Pipelines Streaming media Cameras Real-time systems Security

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Grouping and Categorizing Data from Social Networking Applications for Forensic Analysis

Grouping and Categorizing Data from Social Networking Applic...

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Artificial Intelligence, Blockchain, Cloud computing, and Data Analytics (ICoABCD), international conference on

作者： Handoko Tejo Atmoko Niken Dwi Wahyu Cahyani Syofian Kurniawan School of Computing Telkom University Bandung Indonesia Informatics Applications Control Ministry of Communications and Informatics Jakarta Indonesia

ISBN: (数字)9798350368451

ISBN: (纸本)9798350368468

Social media has become a new trend in the lifestyle of modern society. It provides an indispensable data source for daily life, with widespread use worldwide. This research aims to simplify the complexity of social networking application data and provide key information needed in forensic investigations. Specifically, the objectives are to identify the types of data that can be collected from Twitter and Instagram and develop a methodology to efficiently and effectively group and categorize the data to answer the key forensic questions of Who, Where, What, When, Why, and How. The proposed methodology includes automated web crawling using Apify, grouping and categorizing data based on key parameters, and storing and managing PostgreSQL data. This research targets law enforcement and forensic analysts, providing them with a robust tool for digital forensic investigations. The results of this study show that the methodology successfully simplifies social media data, making it more structured and easier to analyze for forensic purposes.

关键词： Social networking (online) Law enforcement soft sensors Digital forensics Blogs Market research Complexity theory Web sites Multimedia communication Object recognition

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Research on Denoising Algorithm of Orthopedic CT Image Based on Wavelet soft Threshold Filtering

Research on Denoising Algorithm of Orthopedic CT Image Based...

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Networking, Informatics and computing (ICNETIC), international conference on

作者： Haoyu Liu Queen Mary School Medical College Nanchang University Nanchang China School of Biological and Chemical Science Biological Science Queen Mary University of London London UK

Influenced by relevant conditions, digital images are easily disturbed by external noise in the process of generation, transmission and compression, which makes the image edges and texture details blurred, which is not conducive to the recognition and reading of image information. As far as orthopedic CT images are concerned, when the attenuation coefficients of diseased tissues are very close to those of normal tissues, the existence of noise will reduce the signal-to-noise ratio of the images, which will directly lead to some feature details of the images being submerged in the image noise and unrecognizable. The research on denoising algorithm of orthopedic CT image based on wavelet soft threshold filtering is carried out. In this paper, an improved method based on extreme median filtering and wavelet soft threshold denoising algorithm is proposed to remove the mixed impulse noise and Gaussian white noise in the image. Firstly, the image with mixed noise is filtered by the improved median filtering algorithm based on extreme value, and the impulse noise is removed, so that the image with only Gaussian noise is obtained. The wavelet coefficients obtained are quantized by the improved threshold to obtain the estimated wavelet coefficients. The estimated wavelet coefficients are inversely transformed to reconstruct the image, and the denoised image is obtained. The research shows that wavelet soft threshold filtering has the best denoising effect, MSE, EPI and expert evaluation values are 1.2358, 0.9276 and 88 respectively. This method can protect the edge information and improve the visual effect while effectively denoising, which shows that this method also has high denoising ability.

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A Hybrid Pipeline for the Segmentation of Eye Regions from Video Frames 2nd

A Hybrid Pipeline for the Segmentation of Eye Regions from V...

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2nd international conference on Computer Vision, High-Performance computing, Smart Devices, and Networks, CHSN 2021

作者： Rao, Adish Mysore, Aniruddha Guragol, Abhishek Shetty, Rajath Ajri, Siddhanth Sarkar, Poulami Srinivasa, Gowri PES Center for Pattern Recognition PES University Electronic City Campus Bengaluru560100 India Department of Computer Science and Engineering PES Center for Pattern Recognition PES University Bengaluru India

ISBN: (纸本)9789811698842

An accurate tracking and, in turn, an accurate segmentation of key regions of the eye are a sine qua non to provide users with superior quality of immersive experiences in augmented reality (AR), virtual reality (VR), and mixed reality (MR) applications. In this paper, we present the detailed rationale and research behind the design of an image processing pipeline to perform pixel-wise segmentation of eye from the background and labeling key structures, viz. sclera, iris and pupil, of the eye. The images used in this study are from a dataset provided by Facebook as a part of the OpenEDS Challenge 2020 and are sampled frames from a video capture of the eye. The pipeline we present is a hybrid of traditional image preprocessing techniques (such as histogram equalization) with application-specific augmentation (such as emulating a glare pattern) and an ensemble of five powerful, deep learning-based segmentation networks derived from the U-Net and LinkNet, followed by post-processing that harnesses temporal information. With this pipeline, we obtain a final evaluation score of 0.9641, which is well above the baseline score of 0.840 (provided by Facebook as a part of the challenge) and comparable to the top scores reported in the public domain. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pipelines

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Manipulator's Control System with Application of the Machine Learning 11

Manipulator's Control System with Application of the Machine...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems - Technology and Applications (IDAACS)

作者： Kondratenko, Yuriy Sichevskyi, Stanislav Kondratenko, Galyna Sidenko, Ievgen Petro Mohyla Black Sea Natl Univ Intelligent Informat Syst Dept 68th Desantnyk Str10 UA-54003 Kolaiv Ukraine

ISBN: (纸本)9781665426053

This paper provides an analytical review of the subject area, approaches and methods for real-time control systems with application of the machine learning. The review helps to choose approaches to control robotic devices, methods of artificial intelligence to perform pattern recognition and classification, technologies to design a control system and simulate a robotic device. The design and software implementation stage involved the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, software implementation of control system components. The control system was implemented using the Swift programming language and other technologies, and the configuration and creation of a neural network using the CreateML framework.

关键词： real-time system device control system manipulator arm robotics pattern recognition and classification artificial intelligence

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Cloud Based Smart Medical Recommendation System Using pattern recognition

Cloud Based Smart Medical Recommendation System Using Patter...

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Networking and Communications (ICNWC), international conference on

作者： E. Sujatha S. Shanthasheela P.V. Gopirajan Department of Computer Science and Engineering Saveetha Engineering College (Autonomous)Thandalam Chennai Department of Computer Science and Engineering Velammal Institute of Technology Chennai Department of Computational Intelligence School of Computing SRM Institute of Science and TechnologyKattankulathur Chennai

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques.

关键词： Productivity Measurement Laboratories Process control software pattern recognition Task analysis

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Facial Emotion recognition Using a Local Binary pattern based Deep Learning

Facial Emotion Recognition Using a Local Binary Pattern base...

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Computer, Communication and Control (IC4), international conference on

作者： Iftikhar Aslam Tayubi Dilip N. Pawar Ajmeera Kiran Pundru Chandra Shaker Reddy Nipun Sharma D Chitra Faculty of Computing and Information Technology Rabigh King Abdulaziz University Jeddah Saudi Arabia Instrumentation Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai Maharashrra India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Electronics and Communication Engineering Presidency University Bangalore Karnataka India Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India

Facial-emotion-recognition(FER) is being conducted with the goals of analyzing the psychological characteristics of juvenile offenders and promoting the use of deep learning to the extraction of psychological features. Initially, we talk about how certain personality traits are linked to being good at reading facial expressions. To build a facial emotion identification model from this data, we add more convolutional layers to a CNN and combine it with other neural networks like VGGNet, AlexNet, and LeNet-5. Next, a Central-Local-Binary-pattern(CLBP) method, which is optimized, is incorporated into the CNN to build a CNN-CLBP framework for FER. After face photos have been preprocessed and key parameters have been optimized, the algorithm is subjected to a validity study. The average detection rate for the CNN-CLBP is 88.16%, which is greater than the recognition rate for other approaches. In addition, image preparation and parameter tuning boost the recognition accuracy of this approach without resorting to poor-tting. In addition, the CNN-CLBP system accurately identifies 97% of pleased and startled expressions, but 22.54% of sad ones. The study's findings can be used as a starting point for further investigations into the mental health of teen offenders.

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Prediction and Incentive of Number of Participants in Rural Cultural Square Activities by Integrating pattern recognition Algorithm

Prediction and Incentive of Number of Participants in Rural ...

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Intelligent computing, Communication & Convergence (ICI3C), international conference on

作者： Hui Ding School of Marxism Lianyungang Technical College Lianyungang China

ISBN: (数字)9798350343922

ISBN: (纸本)9798350343939

The traditional method relies on historical data and simple statistical model to predict the number of participants in rural cultural square activities, which cannot fully take into account various complex factors and changes, and it is difficult to accurately predict and motivate the number of participants in rural cultural square activities. By integrating pattern recognition algorithms, a feasible method is provided to help organizers better plan and manage rural cultural square activities, in order to achieve the goals of community development and social interaction. This paper collected a large number of rural cultural square activity data and preprocessed the collected data. It combined the Long Short-Term Memory (LSTM) model and the Convolutional Neural Network (CNN) model to build the CNN-LSTM model. According to the number of participants predicted by CNN-LSTM model and the characteristics of activities, the incentive strategy is formulated. The experimental results showed that the accuracy of CNN-LSTM model in predicting the number of participants in rural cultural square activities was as high as 95.8%. The integration of CNN model and LSTM model can effectively improve the prediction of the number of participants in rural cultural square activities and improve the incentive effect.

关键词： Analytical models Time series analysis Predictive models Prediction algorithms Data models pattern recognition Cultural differences Convolutional neural networks Global communication Long short term memory

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international conference on Intelligent Systems and Sustainable computing, ICISSC 2021

International Conference on Intelligent Systems and Sustaina...

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international conference on Intelligent Systems and Sustainable computing, ICISSC 2021

ISBN: (纸本)9789811900105

The proceedings contain 60 papers. The special focus in this conference is on Intelligent Systems and Sustainable computing. The topics include: Mapping of Computational Social Science Research Themes: A Two-Decade Review;Efficiency Analysis of Pre-trained CNN Models as Feature Extractors for Video Emotion recognition;detection of Fake News Using Natural Language Processing Techniques and Passive Aggressive Classifier;machine Learning-Based Human Activity recognition Using Smartphones;a New Supervised Term Weight Measure Based Machine Learning Approach for Text Classification;A Novel ARDUINO Based Self-Defense Shoe for Women Safety and Security;rare pattern Mining from Data Stream Using Hash-Based Search and Vertical Mining;comparative Evaluation of Machine Learning Methods for Network Intrusion Detection System;zero-Day Attack Detection Analysis in Streaming Data Using Supervised Learning Techniques;an Autonomous Intelligent System to Leverage the Post-harvest Agricultural Process Using Localization and Mapping;machine Learning-Based Framework for Malware Analysis in Android Apps;landslide Susceptibility for Communities Based on Satellite Images Using Deep Learning Algorithms;speech Intelligibility Quality in Telugu Speech patterns Using a Wavelet-Based Hybrid Threshold Transform Method;design of Delay-Efficient Carry-Save Multiplier by Structural Decomposition of Conventional Carry-Save Multiplier;cloud-Based Face and Face Mask Detection System;greenput Algorithm for Minimizing Power and Energy Consumption in Hybrid Wireless Sensor Networks;automatic Student Attendance and Activeness Monitoring System;analyzing and Detecting Fake News Using Convolutional Neural Networks Considering News Categories Along with Temporal Interpreter;traffic Density Determination Using Advanced Computer Vision;artificially Ripened Mango Fruit Prediction System Using Convolutional Neural Network;preface;Juvenile Crime Categorization with EM Clustering.

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