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Industrial Video Surveillance image Denoising based on Non-subsampled Contourlet Transform

Industrial Video Surveillance Image Denoising based on Non-s...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Qingyun Zhang Zhuo Chen Pengfei Xie Yingxu Wang Ruibo Shi Qian Zhou Yuduo Pu Dept. of Electronic and Information Eng. Shandong University of Science and Technology Qingdao China Rizhao Guangming Electric Power Service Co. Ltd. Rizhao China State Grid Information & Telecommunication Branch Beijing China Wuhan Fiberhome Technical Services Co. Ltd Wuhan China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the continuous expansion of industrial video scale, visualization technology has become an indispensable technical means. However, during the collection and transmission of images, they may be affected by factors such as the device itself and environmental factors, which can lead to noise pollution and a decrease in image quality, which is not conducive to image post-processing. therefore, industrial video surveillance image denoising has become an important research topic, and using various methods to improve image quality has become an important research direction. In order to address the ringing effect caused by hard threshold denoising, this paper proposes an adaptive smooth non-subsampled contourlet transform (NSCT) denoising algorithm. the proposed method utilizes the correlation of each sub-band to improve the denoising function, making it adaptive. the simulation results show that the algorithm proposed in this paper improves subjective perception such as contrast and detail clarity of remote sensing images, as well as objective evaluation indicators such as peak signal-to-noise ratio, information entropy, and correlation coefficient

关键词： image quality Correlation Wiener filters Heuristic algorithms Simulation Noise reduction Transforms Video surveillance Remote sensing image denoising

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Skin Cancer Diagnosis Using image processing and Xception-CNN Model

Skin Cancer Diagnosis Using Image Processing and Xception-CN...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Sunitha R Pankaja R Pavithra H C Abhilasha P Kumar Mohanesh B M Department of AI & ML B N M Institute of Technology Bangalore India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Skin cancer is recognized as one of the most lethal forms of cancer, often resulting in death if not detected early. Its aggressive tendency to spread to other parts of the body makes treatment in later stages particularly difficult, emphasizing the crucial need for early detection. As the incidence of skin cancer continues to rise, along with its associated high mortality rates and treatment expenses, there is an urgent demand for more effective early detection methods. Leveraging deep learning algorithms through a model-driven architecture has emerged as a promising solution for improving skin cancer diagnosis. In this work, present the development and implementation of a deep learning model designed to accurately classify images of skin lesions. the proposed approach uses the Xception model, an advanced convolutional neural network (CNN) architecture. through training, the Xception model identifies patterns and features in skin lesion images that indicate whether the condition is benign or malignant. Proposed model achieved an accuracy rate of 86% in the classification task.

关键词： Photography Deep learning Wireless communication Training Computer architecture Skin Classification algorithms Lesions Convolutional neural networks Skin cancer

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An Improved RANSAC Method Using Absolute Median Deviation (MAD) Based on Adaptive threshold

An Improved RANSAC Method Using Absolute Median Deviation (M...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Khalid Akdim Ahmed Roukhe Hassane Roukhe Dept. of Physics Faculty of sciences Moulay Ismail University Meknès Morocco Dept. of Physics Faculty of sciences Mohamed V University Rabat Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

image processing now plays a relevant role in a wide range of fields. image registration, a critical aspect of image processing, encompasses essential issues like image fusion, and remote sensing. One of the most robust algorithms used in feature registration is the Oriented FAST and Rotated BRIEF (ORB). Several optimization methods, including random sample consensus (RANSAC), have been adopted to reduce the outliers in this algorithm ORB and find optimal parameters of the image registration problem. However, RANSAC-based methods exhibit some deficiencies such as a high number of iterations, increased false positives ratio of mismatches, and a fixed threshold value determined through experimentation resulting in lower accuracy of transform model parameters. In this paper, we propose a modified version of RANSAC incorporating a Median Absolute Deviation (MAD) based on an adaptive threshold to address these deficiencies. To verify the validity of our proposed approach, a comprehensive experimental analysis has been done on a database of two images. A study, of the influence of certain factors such as the angle of rotation of the sensed image, the scale of the sensed image, and the noise applied to the sensed image. the simulation results show that the proposed method can achieve good transform model parameters in terms of Mean Square Error (MSE), Structure Similarity Index Method (SSIM), and Peak signal to Noise Ratio (PSNR).

关键词： image registration Adaptation models PSNR Simulation Noise Optimization methods Transforms

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AI-Based image Analysis for Early Disease Detection in Medical Imaging

AI-Based Image Analysis for Early Disease Detection in Medic...

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international conference on Computer, Communication, Control and Information Technology (C3IT)

作者： Pulkit Dwivedi Nikita Rattan Mansi Kajal School of Computer Science & Engineering IILM University Greater Noida India Apex Institute of Technology(CSE) Chandigarh University India K. R. Mangalam University Gurugram India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

Early disease detection and accurate segmentation in medical imaging, particularly using MRI scans, are critical for effective treatment and patient survival. this research focuses on developing a robust system for early disease detection and segmentation utilizing machine learning and deep learning techniques. the Disease Doc 2019 dataset from Kaggle was employed to train and test various models, including Support Vector Machines (SVM), Naive Bayes, and Random Forests. Our methodology involves a comprehensive process starting from data collection and pre-processing to feature extraction, classification, and evaluation. the implementation of a GUI-based interface allows users to upload MRI images, detect diseases, and view results seamlessly. the models demonstrated high performance with metrics such as accuracy, precision, recall, F-score, Sorensen-Dice index, Jaccard index, and specificity. this study underscores the potential of advanced machine learning algorithms in enhancing the accuracy and efficiency of medical image analysis, thereby supporting clinical decision-making and improving patient outcomes.

关键词： Support vector machines Measurement image segmentation Accuracy image analysis Magnetic resonance imaging Indexes Random forests Diseases Biomedical imaging

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Time Based image Synthesis: Light and Color Conversion Using Machine Learning Techniques

Time Based Image Synthesis: Light and Color Conversion Using...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Jyoti Ranjan Labh R.K. Dwivedi College of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

the progress made in camera technology and computational imaging has been astounding when it comes to photography. A specialized neural image signal processor is currently used by many top smartphone cameras and DSLR to convert chaotic raw sensor photos into the final, polished output. A sizable dataset of image pairs-one with a noisy, short-exposure raw picture and another with a clean, long-exposure ground-truth raw image-is needed to train these neural ISPs for low-light, processing It takes a lot of effort and time to capture these aligned image pairs, and motion blur in the long-exposure ground truth frequently makes the process difficult. Artificial nighttime datasets can be used to train various image processing algorithms and neural ISPs that are more resilient and effective. In order to overcome this difficulty, we proposed a model that uses to convert image colour and lighting of a raw picture to artificially create realistic image of different time zone raw photos, brighter photos are typically easier to take, have less noise, and are less likely to suffer from motion blur. the objective of the suggested processing framework is to transform raw photographs taken during the different time into simulated images of different timing with different levels of noise and other low-light features. More ease and flexibility in training neural ISPs for time-based rendering is made possible by this artificial time- based picture dataset. Additionally, in our proposed method the performance of the trained neural ISPs can resemble that of models trained only on real different timings photos if they mix their synthetic data with a tiny percentage of real data. the potential to produce accurate raw photos of midnight and different night time in a synthetic manner holds significant implications for computational photography and image processing applications and even object recognition under difficult nocturnal conditions.

关键词： Photography Training image color analysis Training data Transforms Streaming media Cameras Timing Noise measurement Synthetic data

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Enhanced Medicare Fraud Detection Using Graph Convolutional Networks

Enhanced Medicare Fraud Detection Using Graph Convolutional ...

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Artificial Intelligence and signal processing (AISP), INDIA, international conference on

作者： Molkam Rakesh Pushparaj Shetty D Department of Mathematics and Computational Science National Institute of Technology Karnataka Surathkal India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

this paper explores the applications of Graph neural networks (GNN) for enhancing Medicare fraud detection. Graph convolutional network (GCN) is a type of graph neural network. Governments and insurance companies are continuously adapting new technologies to detect and prevent fraud activities and trying to minimize financial losses and improve services because every year they lose billions of dollars due to Medicare fraud. Machine learning algorithms fail to analyze the graph data structure but Graph neural networks are good at analyzing the complex relational data and they directly integrate with the learning process. Machine learning algorithms are facing scalability and generalization across diverse graphs. GNN works on graph data structure, using unique IDs as nodes in a graph, with edges illustrating their relationships. Graph Neural Networks is used to improve the accuracy and efficiency of fraud detection by learning the complex relational information obtained from providers, beneficiaries, and physicians. We created a graph database based on the healthcare provider dataset. In this graph database, two types of heterogeneous nodes are there that are beneficiary and medicare provider nodes. the connection between the beneficiary and medicare providers is a power edge and the connection between providers is a shared-physician edge. We developed a fraud detection model using both machine learning and graph neural networks. Our Graph convolutional Network (GCN) model performed well compared to the basic machine learning (Logistic regression) model. the complex relationships between provider and beneficiary, provider and physician helped to detect medicare fraud using our model.

关键词： Logistic regression Machine learning algorithms Graph convolutional networks Databases image edge detection Scalability Medical services Machine learning Data structures Fraud

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Adaptive Compressive Sensing for Natural images Using Schur Decomposition and Optimized Reconstruction

Adaptive Compressive Sensing for Natural Images Using Schur ...

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Sustainable Expert Systems (ICSES), international conference on

作者： M. Aarthi Elaveini T. Deepa Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Compressive sensing (CS) is growing as an effective method for efficient imagine capture and recovery by harnessing the fundamental sparseness of natural images. the paper presents a unique framework for CS of live images employing Schur decomposition, a matrix decomposition approach that improves the detection and reconstruction processes. the suggested technique utilizes Schur decomposition to enhance the sensing matrix, facilitating more effective sampling by collecting essential imagine information in both spatial and frequency domains. the Schur-based sensing matrix, in contrast to conventional random sensing matrices, is designed to use the structural characteristics of real pictures, hence enhancing the precision of image reconstruction from a reduced quantity of observations. the integrated Schur decomposition with an iterative image reconstruction algorithm, incorporating Orthogonal Matching Pursuit (OMP) promotes sparsity. the combination improves the robustness of the recovery process while reducing reconstruction artifacts. Extensive simulations demonstrate that proposed Schur-based framework achieves superior performance compared to conventional CS techniques, particularly in terms of Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). the proposed method improves the quality of reconstructed images and expedites the convergence of the reconstructing technique, hence reducing computational complexity. the framework is assessed over several natural imagine datasets, demonstrating substantial improvements in image quality and computing efficiency resulting in a PSNR of 37.263 dB and SSIM 0.813 for CAT image. the Schur decomposition method for CS is an effective solution for applications necessitating quick picture capture and high-quality reconstruction, including medical imaging, remote sensing, and real-time image processing systems.

关键词： image quality PSNR Matching pursuit algorithms Real-time systems Iterative algorithms Sensors Matrix decomposition image reconstruction Compressed sensing Remote sensing

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A Joint Dynamic Domain Adaptation Module and Multi-incentive-aware Based Model for Sentiment Analysis of Autism Paintings

A Joint Dynamic Domain Adaptation Module and Multi-incentive...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Jiayin Min The Experimental High School Attached to Beijing Normal University Beijing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

With the development of society, the number of autistic children is increasing, and there is growing concern about their education and development. Autistic children often have difficulties in language expression, social interaction and emotional understanding. In this context, finding new ways to understand the emotions and needs of autistic children has become particularly important. By analyzing the drawings of autistic children and using deep learning models to mine the emotions and information embedded in them, we can provide educators and parents with a more intuitive and in-depth way to understand the inner world of autistic children. As a non-verbal form of expression, paintings can help autistic children release emotions and express their inner thoughts and feelings. Firstly, based on psychological research results, we propose a new image emotion classification network based on multi-incentive awareness, which consists of three phases, i.e., incentive selection (S), feature extraction (O) and emotion prediction (R). In the first phase, in this paper, we select special emotion excitations (color, object, face) by combining existing target detection methods in deep learning. Subsequently, we design three specific networks to extract different emotion features from multiple incentives synchronously, i.e., global network, semantic network, and expression network. Finally, the model combines all the above features for image emotion prediction. Comparative experiments demonstrate that the accuracy of our constructed sentiment analysis model outperforms existing algorithms. After training the above multi-incentive-aware image sentiment analysis model, we design a transfer learning model to map the features learned by the model on the regular image dataset to the drawing dataset of autistic patients. We perform multi-representation feature extraction using a hybrid structure, which consists of multiple sub-structural feature extraction units. At the same time we us

关键词： Deep learning Analytical models Adaptation models Sentiment analysis image color analysis Transfer learning Psychology Predictive models Feature extraction Painting

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image Feature Extraction with Fourier Transform and Multi-task Swin-Transformer for Alzheimer’s Disease Prediction and Detection

Image Feature Extraction with Fourier Transform and Multi-ta...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Chengli Hu Shenyang Aerospace University Shenyang China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Alzheimer’s disease (AD) patients often miss the optimal treatment time due to their late onset and unclear early clinical manifestations. there is little difference between the CT images of the brain of AD patients and normal people, and the efficiency of manual diagnosis is low. However, classic models (such as Resnet, Swin Transformer, CNN, etc.) do not have strong targeting for brain disease diagnosis, and they are more inclined towards general classification tasks, so there is great room for improving the accuracy of the model. this article (FTM Swin) is based on a learnable mask mechanism with algorithm combined with SVM and swin-transformer, to filter and extract features in the frequency domain graph of 3D images. Fast Fourier transform (FFT) is used to achieve the interaction and combination of frequency domain and spatial domain feature matrices, ultimately completing the classification task of data points in the time domain, which simplifies the feature extraction process and makes the classification boundary of the classifier more accurate. Subsequently, this article utilized the ADNI brain imaging dataset and trained the previous classifier model using a dataset annotated with the hippocampus region, which plays a crucial role in Alzheimer’s disease lesions. this further enhanced the model’s ability to detect lesion sites and provided information on the stage of the condition. Compared with the other eight advanced models for image classification tasks in the past three years, FTM-Swin has an accuracy of 97.18% on the validation set, with the best performance.

关键词： Accuracy three-dimensional displays Frequency-domain analysis Predictive models Brain modeling Feature extraction Transformers Multitasking Alzheimer's disease Time-domain analysis

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Clinical Breast Imaging and Deep Learning Model for image Malignancy Prediction Using Resource-Constrained Embedded Devices

Clinical Breast Imaging and Deep Learning Model for Image Ma...

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Artificial Intelligence and signal processing (AISP), INDIA, international conference on

作者： Sumedha R. Chokhandre Pranjali Jadhav Nilesh Gole Amit N. thakare Akansha R Bondade Priyanka Singh Dept. of Information Technology YCCE Nagpur AI & DS Department Vishwakarma Institute of Information Technology Pune Department of CSE Vidharbha Institute of Technology Nagpur Department of CSE Cummins College of Engineering for Women Nagpur Department of CSE G.H. Raisoni University Saikheda School of Computer Science VIT-AP University Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Computer assisted diagnosis(CAD) of diseases provides more accurate and precise diagnostic reports towards better information regarding the medical condition of patients. A clinician can minimize the error by applying his experience acquired by practice, cognitive intuition or scientific research backed by laboratory reports and computer assisted medical image analysis. the findings by the experts based on the analysis of such data are crucial as the suggested treatment is dependent on evaluation at this stage. Machine learning techniques while applied in the medical field performs decision making by mimicking the steps performed by a medical expert in diagnosing the disease, but using algorithms rather intuitive. It brings out accurate medical data through analysis of images performed by computing devices that can reveal valuable information regarding the disease prognosis. Computer aided disease diagnosis with state-of-the-art machine learning and deep learning offers seamless assistance in medical care with near human accuracy. Technology integrated medical support systems combined with sophisticated algorithms can reduce the number of false positive incidents as well as false negative cases. Convolutional neural network (CNN) models can be trained using handcrafted features to derive conclusive inferences for binary class as well as multi-class classification. Artificial Intelligence (AI) supported techniques in disease diagnosis provide assistance to medical experts in decision making by virtue of cloud based data analytics tools for storage and computing.

关键词： Deep learning Performance evaluation Accuracy Machine learning algorithms Computational modeling Decision making Predictive models Medical diagnosis Medical diagnostic imaging Diseases

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