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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Wang, Yuxin Liu, Xiaowei Qi, Yifeng Guo, Jiangyang Han, Jianing Zhang, Jialiang Zhang, Zhi Lian, Jianguo Nie, Weizhi Tianjin Agricultural University School of Computer and Information Engineering Tianjin China Unicom Video Technology Co. Ltd. Tianjin China Tianjin Huada Technology Co. Ltd. Tianjin China Tianjin University School of Electrical Automation and Information Engineering China

ISBN: (纸本)9798350331417

With the rapid development of big data and the Internet of Things technology, people have easier access to data, which leads to different perspectives when observing data. As a result, multi-view data has emerged, which can effectively demonstrate different manifestations of a sample, but is often more complex to process. Multi-view clustering has received increasing attention in recent years, utilizing the consistency and complementarity among multiple views to conduct more effective digital representation, in order to obtain information that cannot be obtained from a single view and improve clustering effectiveness. This article provides a classification and detailed discussion of existing multi-view clustering methods, and summarizes current challenges and future development trends. © 2023 IEEE.

关键词： data mining deep learning multi-view clustering multi-view data

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2022 international conference on Signal and Information processing, IConSIP 2022

2022 International Conference on Signal and Information Proc...

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2nd international conference on Signal and Information processing, IConSIP 2022

ISBN: (纸本)9781728168852

The proceedings contain 85 papers. The topics discussed include: audio processing for tones validation using deep learning;effective generation of visual questions;elicitation of intracerebral hemorrhage using deep learning;underwater image enhancement using color constancy via homomorphic filtering and depth estimation;fusion based underwater image enhancement and detail preserving;FPGA based feature extraction in real time computer vision - a comprehensive survey;enhancement of liver ultrasound images by guided image filtering technique;exploration of horizontal and vertical components in polarimetric decomposition based on volume scattering;audio source count estimation using deep learning;detection of pneumonia from x-ray images using eigen decomposition and machine learning techniques;audio based detection of saw blade sharpness using machine learning;performance analysis and evaluation of estimator for RF cavity detuning measurement;speaker identification and verification using deep learning;and motor imagery based EEG signal classification using multi-scale CNN architecture.

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Single image Self-Learning Super-Resolution with Robust Matrix Regression

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AATCC JOURNAL OF RESEARCH 2021年第1_suppl期8卷 135-142页

作者： Jian, Zhang Xu Tengteng Qian Jianjun Xiao, Yuchen Zhang, Heng Li, Hongran Li, Cunhua Jiangsu Ocean Univ Lianyungang Jiangsu Peoples R China China Univ Min & Technol Beijing Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

The similarity measure plays the key role in the self-learning framework for single image super-resolution. This paper involves matrix regression with properties of robustness and two-dimensional structure to measure the similarity between image blocks and enhance the effect of super-resolution. Specifically, we use the minimal nuclear norm of representation error as a criterion, and the alternating direction method of multipliers (ADMM) to calculate the similarity between high- and low-resolution image blocks. Evaluation on several images with different interference and experimental results of super-resolution images clearly demonstrate the advantages of our proposed method in visual robustness and super-resolution effects.

关键词： computer vision Fabric Imaging processing image Super-Resolution Matrix Regression Similarity Measure

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An Efficient Salt and Pepper Noise Reduction Approach for Video(s) Using Optimized Filter Approach 2

An Efficient Salt and Pepper Noise Reduction Approach for Vi...

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2nd international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2022

作者： Kumain, Sandeep Chand Singh, Maheep Govil, Mahesh Chandra Pilli, Emmanuel Shubhakar Srinagar India Malaviya National Institute of Technology Jaipur Dept. of Computer Science and Engineering Jaipur India

ISBN: (数字)9781665488754

ISBN: (纸本)9781665488754

In today's digital world, most information is shared through images or videos. images and videos are more infor-mative than textual data. During the image or video capturing process, addition of noise is one of the major problems. The presence of noise in captured data causes results to be misleading, particularly in computer vision and image processing tasks. As a result, preprocessing is required before tackling tasks like edge detection, object detection, object recognition, salient object detection, video summarization, and so on. Gaussian, Salt & Pepper, Poison, and Speckle noise are the most common types of noise which can affect the video. In this article, the author(s) presented an efficient noise reduction approach for reducing the salt and pepper noise present in the image or video. The performance of the proposed approach is compared with traditional and widely used filters for noise reduction, such as mean, mode, and median. The experimental analysis of the proposed approach is done on the dataset where the videos are captured using the camera as well as from the other resources such as Internet. Noisy videos are prepared by mixing the Salt & Pepper noise with different noise densities (levels) in the video dataset. The experimental results show that the proposed approach outperforms the traditional approaches not only in terms of noise reduction but also in preserving the details in the video. © 2022 IEEE.

关键词： Salt and pepper noise

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Attending to Transforms: A Survey on Transformer-based image Captioning

Attending to Transforms: A Survey on Transformer-based Image...

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international conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal processing (PCEMS)

作者： Kshitij Ambilduke Thanmay Jayakumar Luqman Farooqui Himanshu Padole Anamika Singh Visvesvaraya National Institute of Technology Nagpur India Indian Institute of Technology Bhubaneswar India

image captioning is a challenging task that lies at the intersection of computer vision and Natural Language processing. There exists a legion of works that generate meaningful and realistic descriptions of images. Recently, with the advent of attention mechanisms and transformers, there has been a drastic shift in modelling both language and vision tasks. However, there are very few extensive studies that review these approaches based on their progression, advantages and disadvantages. This paper presents a detailed summary of transformer-based models employed for tackling image captioning. In addition to this, we provide an overview of various pre-training tasks, datasets and metrics used for image captioning. Finally, the performance of all the reviewed approaches are compared on the COCO Captions dataset.

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Ensembling Deep Learning And CIELAB Color Space Model for Fire Detection from UAV images 2

Ensembling Deep Learning And CIELAB Color Space Model for Fi...

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2nd international conference on AIML Systems

作者： Jain, Yash Saxena, Vishu Mittal, Sparsh Indian Inst Technol Roorkee Roorkee Uttarakhand India

ISBN: (纸本)9781450398473

Wildfires can cause significant damage to forests and endanger wildlife. Detecting these forest fires at the initial stages helps the authorities in preventing them from spreading further. In this paper, we first propose a novel technique, termed CIELAB-color technique, which detects fire based on the color of the fire in CIELAB color space. We train state-of-art CNNs to detect fire. Since deep learning (CNNs) and image processing have complementary strengths, we combine their strengths to propose an ensemble architecture. It uses two CNNs and the CIELAB-color technique and then performs majority voting to decide the final fire/no-fire prediction output. We finally propose a chain-of-classifiers technique which first tests an image using the CIELAB-color technique. If an image is flagged as no-fire, then it further checks the image using a CNN. This technique has lower model size than ensemble technique. On FLAME dataset, the ensemble technique provides 93.32% accuracy, outperforming both previous works (88.01% accuracy) and individually using either CNNs or CIELAB-color technique. The source code can be obtained from https://***/CandleLabAI/FireDetection.

关键词： computer vision aerial images fire detection CIELAB color space CNN ensemble learning

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A Digital Twin Model of Smart Factory Production System 2nd

A Digital Twin Model of Smart Factory Production System

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2nd international conference on image, vision and Intelligent Systems, ICIVIS 2022

作者： Wang, Yibin Zhang, Shiyue Wu, Shuang Zhou, Yiquan Du, Junhao Li, Heng School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9789819909223

Today's industrial production is developing in the direction of intelligence, in order to improve production efficiency and supervise production. Nowadays, traditional manufacturing industry is gradually paying attention to the role of data twin technology in industrial production. Real-time and accurate production forecasts can help factories generate rough expectations for production results and help factories troubleshoot problems in the production process. Therefore, it is of great significance to improve the prediction accuracy of production results and provide reliable information for factories. This article describes a digital twin technology that leverages machine learning to analyze industrial production processes. Use data processing, clustering, classification and regression algorithms to model the production process, and use GUI to make a visual interface for display. Specifically, preprocessing production data through correlation analysis and data cleaning brings valuable datasets for modeling. And established a regression model based on KNeighborsClassifier algorithm to predict the target variable. This enables accurate prediction of production results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Production efficiency

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Brain Tumor Segmentation with Efficient and Low-Complex Architecture Using RCNN and Modified U-Net 2nd

Brain Tumor Segmentation with Efficient and Low-Complex Ar...

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2nd international conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Raha, Ananta Parvin, Farjana Jannat, Tasmia Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (纸本)9789819989362

In medical applications, the boundless potential of image processing utilizing Deep Neural Networks has grabbed the interest of researchers. Brain tumor segmentation, which is a crucial piece of task, determines the location and extent of tumor areas. Numerous techniques for segmentation have been suggested by researchers. One significant disadvantage of the existing architectures is the presence of a large number of trainable parameters. It makes the system complex, expensive to train, and unsuitable for integration in low-powered devices. In this paper, we present an efficient, two-stage approach for the effective segmentation of brain tumor from MRI images using RCNN and a modified U-Net. The proposed system was evaluated and verified using a publicly available Figshare dataset (Cheng in, 2017 [1]). The system is low-complex with small number of parameters compared to other existing architectures. It was tested and compared to the original U-Net, and despite having a large decrease in total trainable parameters, it obtained a comparable performance with an accuracy of 99.78%, IoU of 89.76%, and a dice score of 94.53% in our experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medical applications

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Face Mask Detection using Single Shot Multibox Detector and Mobile Net 2

Face Mask Detection using Single Shot Multibox Detector and ...

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2nd IEEE international conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Ranjana, P. Ramesh, K. Shivanandhan, S.J. Manoj, B. Hindustan Institute of Technology and Science Department of CSE Chennai India

ISBN: (纸本)9781665493963

During the pandemic time government took many safety measures to protect the public at common gathering places. People are insisted on wearing a face mask to protect themselves from COVID. Even then many people were roaming without a mask in public places. The proposed technique to detect the face mask is to identify the person's face with mask and person's face without mask and reporting to the safety officers about the persons without mask for further action. The proposed Face mask detection is developed using the ML technique which can be used to classify the people wearing masks and not wearing masks with the input given to the model. The proposed face mask detector is a one-stage detector that focuses on detecting the face mask alone. This work is implemented using the Tensor flow and computer vision libraries. NumPy is used for image processing. The data set used in MAFA dataset. The model is trained using this data set to get the accurate results. To enable multiple detection here the single shot with multi box detector is used. The base model used for this process is Mobile Net V2. The proposed model is simple and it can be integrated with several other technologies to provide high accuracy percentage of output in the minimum possible time. © 2022 IEEE.

关键词： computer vision

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Research on Badminton Movement Machine Learning Model Based on computer vision Technology

Research on Badminton Movement Machine Learning Model Based ...

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image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Cheng Zong Hohhot Vocational College Hohhot China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper aims to explore an innovative method combining computer vision and machine learning to accurately identify and analyze various movements in badminton. This paper first summarizes the application prospect of computer vision in the field of sports analysis, and introduces its specific application scenarios in badminton in detail. By constructing a complete technical framework of image preprocessing module, feature extraction algorithm and deep learning model, the complex movements of badminton players such as swing, stroke and moving pace are captured and analyzed. In the research process, we used multi-view image fusion and key point detection technology to accurately extract action features in badminton, combined with convolutional neural network (CNN), recurrent neural network (RNN), long term memory network (LSTM) and other deep learning models to efficiently learn and model these features. Thus, the automatic classification and recognition of badminton movement can be realized. The experimental results show that the model has significant accuracy in badminton action recognition, good generalization ability and practicability, and can be effectively applied in the badminton teaching and training process of athlete performance evaluation, competition data analysis and other aspects. This research result not only expands the practical application of computer vision technology in the field of badminton, but also provides new ideas and tools for further promoting the development of sports intelligence and digitalization.

关键词： Training Deep learning computer vision Analytical models Recurrent neural networks Computational modeling Feature extraction Data models Convolutional neural networks Sports

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