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检索条件"任意字段=2nd Conference on Image Reconstruction from Incomplete Data"

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image Enhancement for Pedestrian Detection at Night Time 2

Image Enhancement for Pedestrian Detection at Night Time

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2nd International conference for Innovation in Technology, INOCON 2023

作者： Nagajyothi, D. Charan, Panasa Sai Zeeshan, Mohammad Jyothi, V. Vardhaman College of Engineering Department of ECE India

ISBN: (纸本)9798350320923

Present days people are often geographically dispersed unevenly in low-light photographs, making it challenging to differentiate them due to the few photons and inherent noise. Some person detecting models are unable to function in dimly lit environments. Within this project First, we will use gamma correction, a method for improving low-light images and storing the improved photos. Second step is to utilizing the enhanced and low-light pictures, the convolution neural network technique is *** data will be used to train the CNN model, which produces results for accuracy, precision, recall, and f1. The SVM HOG features will be applied during prediction to extract people identification from low-light photographs. Our proposed model provides more prediction results compare to existing project. © 2023 IEEE.

关键词： image enhancement

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Intelligent Optimization Algorithm based data Storage and Management: An Empirical Study 2

Intelligent Optimization Algorithm based Data Storage and Ma...

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2nd International conference on Electronics and Renewable Systems, ICEARS 2023

作者： Ayoub, Shahnawaz Savaliya, Rajesh Kumar Rameshbhai Purushothaman, G. Girdharwal, Nitin Verma, Mrinal Tiwari, Mohit Shri Venkateshwara University Department of Computer Science Uttar Pradesh Amroha India Mibm Dica Sabargam Ambaba Commerce College Bca Department Gujarat Surat India St. Joseph's College of Engineering Tamilnadu Chennai India Medi-Caps University Faculty of Management Indore India Kiet Group of Institutions Delhi India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350346640

Due to developments in digital world, security is developed into an inseparable problem but transferring images. Visual Cryptography (VC) is a present cryptographic approach that is utilized to confidential images shared safely and also its data kept with utmost secrecy. A sender transfers the confidential image that is separated into shares and it holds hidden data. If share is arranged and stacked together, it is inclined to represent the confidential image data to receiver. Previous VC method, the confidentiality of shares could not be retained because all other fake shares are easily modified or inserted remain that continuing challenges. This article develops an Intelligent Optimization Algorithm Based data Storage and Management (IOA-DSAM) technique. The IOA-DSAM technique intends to securely and effectively store the data. To attain this, the IOA-DSAM technique splits the images into different shares. Next, the shares are encrypted using Homomorphic Encryption (HE) technique where the key generation process takes place via modified fruitfly optimization (MFFO) algorithm. Upon reception, the decryption of shares is performed followed by share reconstruction process. To exhibit the performance of the IOA-DSAM system, a widespread experimental study is performed. The resultant outcomes demonstrate the improvement of the IOA-DSAM technique over other models. © 2023 IEEE.

关键词： Information management

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Visual Weather Analytics-Leveraging image Recognition for Weather Prediction 2

Visual Weather Analytics-Leveraging Image Recognition for We...

引用

2nd International conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Swetha, Kora Kumari, E.N. Vijaya Reddy, V. Adinarayana Gupta, K.Gurnadha Mlr Institute of Technology Computer Science and Engineering India Ece Ksrm College of Engineering Kadapa India Koneru Lakshmaiah Education Foundation Dept of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350325799

image classification is a procedure that makes an effort to understand an image in its entirety. The objective is to label the picture so that it may be filed away in the appropriate category. image data that has been collected typically suffers from degradation due to opaque means such as haze, smoke, fog, rain, or snow. De-weathering is the procedure that removes the effects of weathering from an image so that it can be used again. This is done so that similar issues do not arise. As a result, the preprocessing phase is required to concurrently evaluate the quality and other characteristics of an input image, and then additionally execute de-weathering (for example, removals of haze, rain, or snow). These checks and removals must take place in sequence. In this research, an idea for a deep learning-based weather image recognition model is presented. The model takes into account 11 various weather situations, including dew, fog/smog, frost, glazing, hail, lightning, rain, rainbow, rime, snow, and sandstorm. In addition, the paper gives an idea for a deep learning-based weather image recognition model. © 2023 IEEE.

关键词： Weather forecasting

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Enhancing Security in SMS by Combining NLP Models Using Ensemble Learning for Spam Detection with image Steganography Integration 2

Enhancing Security in SMS by Combining NLP Models Using Ense...

引用

2nd International conference on Edge Computing and Applications, ICECAA 2023

作者： Kumar, Aditya Fancy, C. Srm Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (纸本)9798350347579

Spam SMS messages are a prevalent problem in today's world and have become a source of annoyance for users. This research study proposes a novel approach to detect SMS spam using Natural Language Processing (NLP) and creating multiple models and apply ensemble learning on them. The proposed method includes pre-processing the data with NLP techniques such as stopwords removal, stemming, and lemmatizing, extracting relevant features from the text data, and transforming it into numerical representations. The performance of the proposed method was evaluated on a real-world dataset and compared to traditional machine learning algorithms such as Naïve Bayes, SVM. The multiple classifiers are trained on the transformed data, and an Ensemble Learning algorithm is applied to combine their predictions to obtain a more accurate result. The resulting model KNR gave higher accuracy than traditional models. The custom model was integrated into a custom image Steganography tool which can hide textual data into an image using 1 bit Lease Significant Bit (LSB) technique and while decrypting a hidden message, the custom model would detect if the textual data is spam or not. The purpose of the image Steganography tool is to provide users with a secure method of communication. For example, sender can hide sensitive data they wish to send to the receiver by hiding it inside an image and the receiver decrypting it to get the original message. If a malicious actor finds out about this communication method and tried to send spam links or messages by hiding it in an image and sending it to its victim, the custom spam detection model will detect it. © 2023 IEEE.

关键词： Learning systems

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Efficient Construction of Face image dataset for Head Pose Estimation 2

Efficient Construction of Face Image Dataset for Head Pose E...

引用

2nd International conference on Computer Graphics and image Processing, CGIP 2024

作者： Furuya, Yuki Saitoh, Takeshi Tsuchiya, Keiko Sato, Hitoshi Nakamura, Kyota Abe, Takeru Coffey, Frank Kyushu Institute of Technology Iizuka Japan Yokohama City University Yokohama Japan Fukushima Medical University Fukushima Japan Nottingham Univ Hosp NHS Trust Nottingham United Kingdom

ISBN: (纸本)9798350374186

The purpose of this study is to estimate the head pose of a person in an operating room. Most publicly available datasets for head pose estimation (HPE) are images taken from the front, making it difficult to estimate the head pose of the back of the head. In addition, since they are not intended to healthcare professional's head pose, they do not include scenes in which a person is wearing a medical cap and a mask. Therefore, existing datasets do not match the purpose of our project and cannot correctly estimate head pose. Therefore, in this paper, we construct our original dataset. Since constructing a dataset is labor-intensive, we propose a method to construct a dataset in which data is distributed efficiently and uniformly using a turntable. A dataset consisting of approximately 20,000 images was constructed using the proposed method. We evaluated the accuracy of HPE using an existing deep learning model and obtained MAE of 5.67° for the pitch angle and 6.37° for the yaw angle. Furthermore, we compared the results with models trained on other datasets for an actual operating room scene and showed that our constructed dataset is useful. © 2024 IEEE.

关键词： Operating rooms

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Construction of Urban Planning Model Based on Remote Sensing image data Processing Algorithm 2

Construction of Urban Planning Model Based on Remote Sensing...

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2nd International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Xian, Wei Southwest Jiaotong University Sichuan Chengdu610097 China

ISBN: (纸本)9798350323818

Because of its high spatial resolution, high-resolution remote sensing (RS) images can obtain more detailed information of ground objects, thus improving the accuracy of classification of ground objects. Most of the existing remote sensing processing software adopts the traditional pixel-based classification method, but with the application of more and more high-resolution remote sensing satellites in recent years, the traditional methods can no longer meet the application requirements of high-spatial resolution RS images. In urban planning, it is required that not only the fused RS images have good visual and target classification effects, but also the corrected images have high accuracy. In this paper, a RS image data processing algorithm based on data fusion is proposed. With the help of image fusion technology, image interpretation technology and spatial analysis technology, the change information in the implementation of urban planning is extracted, thus providing technical support for the construction of urban planning model. The experimental results show that with the increase of the number of experiments, the accuracy of this algorithm is stable above 90%, and the real-Time wavelength tends to be stable. This scheme is effective in extracting regular buildings from high-resolution RS images. © 2023 IEEE.

关键词： Application programs

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Enhanced image Classification with Integrating DenseNet121 with Mixup Augmentation and Attention Mechanisms for Knee OA 2

Enhanced Image Classification with Integrating DenseNet121 w...

引用

2nd International conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Revathi, A.S. Sathish Babu, B. RV College of Engineering Department of CSE Mysore Road Bangalore India RV College of Engineering Department of AI & ML Mysore Road Bangalore India

ISBN: (纸本)9798331519056

The application of deep learning practices to detect and monitor osteoarthritis (OA) is growing. While magnetic resonance imaging (MRI) provides valuable biomarkers for OA, most deep learning methods traditionally relied on simpler radiographic (X-ray) imaging and demographic data such as age, sex, and body mass index (BMI). This study evaluates the impact of integrating MRI data and patient information to predict knee OA using various deep learning architectures. Specifically, the prediction of knee OA within two years was measured via intermediate-weighted turbo spin-echo (IW-TSE) sequences from 593 participants in the Osteoarthritis Initiative database. We introduce an innovative deep learning approach that integrates the DenseNet121 architecture with Mixup data augmentation and Channel and Spatial Attention mechanisms to address challenges such as high intraclass variance and limited datasets in medical imaging. Experiments conducted using TensorFlow and Keras on a dataset of 186 images across four classes yielded a validation accuracy of 89.78%. These findings highlight the potential of the proposed methodology to enhance early OA diagnosis and support further optimization of deep learning practices for medical imaging applications. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Research on Traditional Art image reconstruction Method Based on Computer Vision Analysis 2

Research on Traditional Art Image Reconstruction Method Base...

引用

2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Supriya, M. Anvesh, Kuncham Muruganandham, B. Manasa Krishnan, K. Muthu, R. Murugan, K. Kpr Institute of Engineering and Technology Department of Ece India

ISBN: (纸本)9798350369960

In the framework of the digital era, the technology of image processing is one of the technologies that is being used increasingly often in all aspects of modern life. image correction may be handled using algorithms based on computer vision, which can increase the correction's influence on the image as a whole. The conventional method of identifying structural systems collects data via the use of wired or wireless measuring systems. The process of establishing sensors and the equipment used for data collection is laborious and takes a lot of time. The process has two stages: the first is the extraction of the line drawing, and the second is the rendering of the line drawing. An edge detection approach that integrates low-level and middle-level visual information is used to extract the line drawing in the process of on-line drawing extraction. This method is used to extract the drawing. The edges are then simplified and linked in accordance with the computer vision theory. Additionally, the image processing technology is used as the research object in order to develop a scientific model for the distorted image. These steps are based on the findings of traditional edge detection. image restoration can be helped along by filtering samples of the image and getting rid of any noise that is there. The fundamentals of computer vision and the technologies that are connected to it are broken down in detail, and the behavioral patterns of visual interaction are investigated. The image of Chinese traditional art serves both the role of art communication and as a medium and symbol for cultural communication. At the same time, it carries on the legacy of a fine traditional culture that has been passed down from generation to generation in China. The image processing technology that is based on computer vision algorithms provides a bigger number of benefits, in addition to a better degree of accuracy. © 2023 IEEE.

关键词： Computer Vision feedback image reconstruction Traditional art Visual analysis

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Medical image Denoising Using BAT Optimization Algorithm 2

Medical Image Denoising Using BAT Optimization Algorithm

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2nd International conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Sankaran, K.Sakthidasan Pradeepa, M. Chandra, Chinta Sharath Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350397635

Denoising is critical in medical imaging for the study of pictures, the diagnosis and treatment of illness. image denoising approaches based on optimization are now effective, however the methods are constrained by the need for a large training set size (i.e., not successful enough for small data size). Medical picture denoising may be accomplished using the discrete wavelet transform (DWT) and a coefficient thresholding-based BAT method (CTB BAT). Denoising images by removing a residual from a noisy image yields denoised images, while most other image denoising methods start with latent clean images and work their way up to learning noise from the noisy images. Additionally, the wavelet transform is incorporated with CTB- BAT to increase model learning accuracy and training time. Denoising strategies are compared to our model's performance in terms of peak signal-to-noise ratio and structural similarity in order to determine how well it performs compared to other medical picture denoising approaches. Our methodology outperforms other approaches in experiments, as shown by the findings. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Exploring Convolution Neural Networks for image Classification in Medical Imaging 2

Exploring Convolution Neural Networks for Image Classificati...

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2nd IEEE International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Vishwa Priya, V. Chattu, Padmini Sivasankari, K. Pisal, Dnyaneshwar Tukaram Renuka Sai, Bantupalli Suganthi, D. Vels Institute Science Technology and Advanced Studies Department of Cs Tamilnadu Chennai India Ace Engineering College India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India SVPMs Institute of Management Baramati Pune Maharashtra Malegaon India Raghu Engineering College Computer Science and Engineering Visakhapatnam India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350306415

Modern healthcare relies on medical imaging to diagnose and cure diseases. Convolution Neural Networks (CNNs) have excelled at image categorization, and their use in medical imaging could change illness diagnosis. This study examines CNN's medical picture categorization performance. This study used X-rays, CT scans, MRIs, and histopathology slides. Traditional CNNs, transfer learning models, and bespoke networks were constructed and trained. The training data included lung ailments, cancer detection, bone fractures, and neurological disorders. Our investigations showed that CNNs can extract complex characteristics from medical photos, improving classification accuracy. Transfer learning, where pre-trained models were fine-tuned on medical data, performed well with 94.8% accuracy. We used cutting-edge data augmentation and attention strategies to improve model generalization and interpretability. In addition to high classification accuracy, we studied model explainability using gradient-based methods and visualization to highlight medical picture regions of relevance that influenced model predictions. Building trust with medical practitioners and understanding deep learning model decision-making requires interpretability. In clinical settings, CNN deployment raises ethical and practical issues such as data privacy, model robustness, and regulatory compliance. According to our research, CNNs may increase medical picture classification accuracy and speed. X-rays had the best accuracy at 95.2%, followed by CT scans at 92.7% and MRIs at 94.1%. Histopathological slides exhibited 88.6% accuracy;however, this shows that CNNs can diagnose diseases from microscopic images. © 2024 IEEE.

关键词： Convolutional Neural Networks image Classification Medical data Analysis Medical Imaging

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