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7th IEEE International conference on Advanced Technologies, signal and image processing, ATSIP 2024

7th IEEE International Conference on Advanced Technologies, ...

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7th IEEE International conference on Advanced Technologies, signal and image processing, ATSIP 2024

ISBN: (纸本)9798350351484

The proceedings contain 118 papers. The topics discussed include: driver action recognition in low-light conditions: a multi-view fusion framework;fuzzy logic obstacle avoidance controller for a mobile robot using lidar;modeling continuous emotions in text data using IEMOCAP database;detection and classification of neurodegenerative diseases by automatic speech analysis;a comparative study of derivative-based and wavelet-based approaches for epilepsy EEG analysis;a novel nonlinear intelligent control approach for optimizing solar PV systems in medical applications under dynamic conditions;access control based on roles and groups: case study building a secure smart hospital using BPMS;credit card fraud detection using synthetic minority oversampling technique and deep learning technique;and in-memory computing architecture for efficient hardware security.

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Edge-Based image Watermarking Method with Weighted Discrete Cosine Transform Coefficients 30

Edge-Based Image Watermarking Method with Weighted Discrete ...

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30th IEEE signal processing and Communications applications conference (SIU)

作者： Karakis, Rukiye Gurkahraman, Kali Sivas Cumhuriyet Univ Yazihm Muhendisligi Bolumu Sivas Turkey Sivas Cumhuriyet Univ Bilgisayar Muhendisligi Bolumu Sivas Turkey

ISBN: (纸本)9781665450928

In image watermarking, hybrid approaches increase imperceptibility and robustness. Also, a scaling factor is used, which should be optimized when combining the cover image and watermark. In this study, discrete wavelet transform and discrete cosine transform (DCT) were used together. The watermark-edge image was obtained by randomly inserting the watermark on the horizontal, vertical and diagonal edge points of the cover image detected with Sobel. The DCT frequency components of the watermark-edge image were weighted with a generated matrix and combined with the DCT of the cover image. According to the obtained results, the proposed method is imperceptible and robust to various attacks, especially JPEG compression and noise attacks.

关键词： image watermarking edge detection scaling factor discrete wavelet transform discrete cosine transform

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image Fusion with CT and MRI images for Improving the Quality of Diagnostics 8th

Image Fusion with CT and MRI Images for Improving the Qualit...

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8th International conference on Computers, Management and Mathematical Sciences, ICCM 2022

作者： Supriya, Bopparaju Keerthi, Jajula Om Sri Rachel, Peeka Ushasvi Manjari, Usha Shaik, Subhani Department of Information Technology Sreenidhi Institute of Science & Technology Telangana Hyderabad India

ISBN: (纸本)9783031511660

To create a single image with the most information possible, two photographs of same model are combined through the process of image fusion. Many image-processing applications, including satellite imaging, remote sensing, and picture fusion, are crucial. The study of fusing images has developed to support the development of the above applications, and it has since expanded to include research on medical imaging. The merger of magnetic resonance (MR) and computerized tomography (CT) images is accomplished by using a curvelet-based method, which is presented in this chapter. The goal of merging an MR image and a CT image of the same organ is to create a unique image that contains the most diagnostically useful information on that organ. wavelet transform has been suggested in some attempts to fuse MR and CT images. Given the number of items and curved forms in medical photos and after fusion, the curvelet transform is expected to perform better than the wavelet transform. According to the simulation results, the curvelet transform outperforms the wavelet transform when merging MR and CT images, in terms of visual quality and peak signal-to-noise ratio (PSNR). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image fusion

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Partition-Based image Exposure Correction via wavelet-Based High Frequency Restoration 20th

Partition-Based Image Exposure Correction via Wavelet-Based ...

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20th International conference on Intelligent Computing (ICIC)

作者： Zhang, Jianming Wu, Mingshuang Cao, Wei Xing, Zi Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410076 Peoples R China Changsha Univ Sci & Technol Key Lab Safety Control Bridge Engn Minist Educ Changsha 410076 Peoples R China

ISBN: (纸本)9789819755967;9789819755974

In the field of image processing, exposure is often a crucial factor influencing image quality. Existing image enhancement methods typically focus on addressing a single exposure issue either under-exposure or over-exposure. Most methods simultaneously addressing multiple exposure issues do not work well, and their effectiveness is also limited. In response to the above challenges, a novel exposure correction method has been proposed. Firstly, utilizing the structural sensitivity of U-shaped network, an Illumination Attention Map Estimation Network (IAMEN) is designed to estimate the partitions of an image. Secondly, a Partition-based Enhancement and Refinement Network (PERN) is proposed. In the enhancement stage, the illumination attention map obtained by IAMEN guides PERN to focus on different exposure areas. The two branches of Partition-based Convolution Enhancement Module (PCEM) incorporate an illumination attention map and its supplement to 1, allowing them to focus more on the underand over-exposure areas, respectively. The encoder in PERN consists of three PCEMs. In the refinement stage, a High-frequency Feature Refinement Module (HFRM) is proposed to extract image high-frequency features using discrete wavelet transform for edge enhancement. Extensive experiments demonstrate that the proposed method consistently achieves remarkable performance compared to several state-of-the-art methods.

关键词： Exposure correction image enhancement Discrete wavelet transform

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Comparative Study: Physiological-based Driver Drowsiness Detection Utilizing Traditional and Hybrid Methods 8

Comparative Study: Physiological-based Driver Drowsiness Det...

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8th IEEE International conference on image and signal processing and their applications, ISPA 2024

作者： Lamouchi, Dorra Yaddaden, Yacine Cherif, Raef Université du Québec À Rimouski Département de Mathématiques Informatique et Génie RimouskiQC Canada

ISBN: (纸本)9798350309249

Driver drowsiness is identified as a major factor leading to traffic accidents and fatalities worldwide. To address this critical public concern, researchers are developing various driver-centered drowsiness detection systems. However, most of the studies in this field rely on either visual-based monitoring techniques or physiological-based indicators. This paper focuses on the latter and presents a comparative study aimed at identifying the most reliable and promising method for detecting the level of drowsiness using physiological signals. This study explores various techniques employing traditional machine learning methods or pre-trained deep neural networks. The former focus on generating features leveraging the temporal aspect of the signals using time series and frequency representation through Fast Fourier Transform, Discrete Cosine Transform, and Discrete wavelet Transform. The latter type utilizes the spectrogram, which encapsulates both temporal and frequency information through visual representation, subsequently employed within a pre-trained Convolutional Neural Network model for feature extraction. The different approaches explored in this study, using the publicly available ULg DROZY dataset, have produced a multitude of results. However, the most promising approach achieved a top accuracy of 88%. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Kinship Verification Using Multiscale Retinex Preprocessing and Integrated 2DSWT-CNN Features 8

Kinship Verification Using Multiscale Retinex Preprocessing ...

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8th IEEE International conference on image and signal processing and their applications, ISPA 2024

作者： Belabbaci, El Ouanas Khammari, Mohammed Chouchane, Ammar Ouamane, Abdelmalik Bessaoudi, Mohcene University of Bejaia LIMED Laboratory Faculty of Technology Bejaia06000 Algeria University of Bejaia LIMED Laboratory Faculty of Exact Sciences Bejaia06000 Algeria University Center of Barika Faculty of Sciences and Technology Barika05001 Algeria University Mohamed Khider of Biskra Laboratory of LI3C Biskra Algeria

ISBN: (纸本)9798350309249

Kinship verification from facial images presents a challenging yet intriguing problem within the fields of pattern recognition and computer vision. In this study, we introduce significant advancements by applying a preprocessing technique known as Multiscale Retinex with Color Restoration (MSRCR) to enhance the quality and contrast of images. Our methodology uniquely combines the strengths of both deep and shallow feature descriptors by integrating them at the score level through the use of Logistic Regression (LR). Specifically, we utilize a novel descriptor, Histograms of a Two-Dimensional Stationary wavelet Transform (Hist-2D-SWT), to capture non-deep features, while employing the CNN model for deep feature extraction. The efficacy of our approach is thoroughly evaluated through extensive experiments conducted on three challenging kinship datasets: Cornell KinFace, UB KinFace, and TS KinFace. © 2024 IEEE.

关键词： image enhancement

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Infrared and visible image Fusion via Variational Bayesian Approximation Method 6

Infrared and Visible Image Fusion via Variational Bayesian A...

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6th International conference on Information Communication and signal processing, ICICSP 2023

作者： Wang, Li Zhao, Puhan Chu, Ning Mohammad-Djafari, Ali Yu, Liang Mathematics and Statistics School Central South University Changsha China Zhejiang Shangfeng Special Blower Company Ltd. Zhejiang University Shaoxing China Zhejiang Shangfeng Special Blower Company Ltd. Cnrs Paris France Shanghai Jiaotong University State Key Laboratory of Mechanical System and Vibration Shanghai China

ISBN: (纸本)9798350339994

The integration of infrared and visible images has become increasingly relevant in the field of remote sensing technologies and image processing applications. This technique has proven beneficial in scenarios such as thermal source monitoring and anomaly detection for industrial machines. The fusion of these two images allows for the creation of a compound image that contains important information from both sources, resulting in an enhanced image with rich background details and clear targets. This paper presents a model for image fusion and probabilistic generation of infrared and visible images. The variational Bayesian approximation method is utilized to fuse the information from both images. The proposed method also introduces a hierarchical prior model using wavelet transform and a variational Bayesian inference algorithm to achieve information fusion. The proposed method provides a feasible and effective solution for health monitoring applications of high-speed direct-drive blowers in industrial scenarios. © 2023 IEEE.

关键词： industrial heath monitoring Infrared and visible image fusion variational Bayesian inference wavelet transformation

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Bearing Fault image Recognition in Strong Noise Based on Optimized CNN Architecture 2

Bearing Fault Image Recognition in Strong Noise Based on Opt...

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2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Li, Qiang Gai, Haowen Hu, Kaijie Northeastern University Shenyang China Tianjin College University of Science and Technology Beijing Tianjin China Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350360240

To address the problem of low accuracy and poor stability of bearing diagnostic models under strong background noise, a bearing fault image recognition method is proposed that reduces the randomness of the model by avoiding the need for artificial parameterization, which can introduce random factors. This method is based on hyperparameter optimization of the GoogLeNet convolutional neural network model and decision fusion. First of all, two-dimensional wavelet time-frequency variation of the original signal of the bearing vibration to construct an image dataset, one-dimensional classification is into a two-dimensional image problem;Secondly, three lightweight convolutional neural network architectures are selected for the noise immunity test, to get the best noise-resistant network architecture GoogLeNet;Finally, hyper-parameter optimization is performed for the network, comparing the mesh method, the progressive mesh method, and the group optimization algorithm, respectively, and getting The optimal parameters are then analyzed for decision fusion and visualization of the network. To verify the method proposed in this paper, we used the open Case Western Reserve University bearing dataset. The experimental verification demonstrates that the proposed method achieves a correctness rate of 94.33% in decision fusion under noise, with good accuracy and stability. © 2024 IEEE.

关键词： Mesh generation

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The Coronary Atherosclerosis Detection: A Diagnostic Approach of Digital Electrocardiography image Interpretation 7

The Coronary Atherosclerosis Detection: A Diagnostic Approac...

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7th International conference on Data Science and Its applications, ICoDSA 2024

作者： Yenila, Firna Yuhandri Okfalisa Universitas Putra Indonesia Yptk Information System Department Padang Indonesia Universitas Putra Indonesia Yptk Information Technology Department Padang Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Indonesia

ISBN: (纸本)9798350365351

Coronary Atherosclerosis is recognized as the predominant cause of Chronic Heart Failure. The standard diagnostic modality for Coronary Atherosclerosis involves echocardiography (ECO);however, the accessibility of such diagnostic equipment remains constrained within several healthcare facilities. Consequently, there is a demand for a more straightforward diagnostic approach, specifically through the utilization of Electrocardiography (ECG). This investigation endeavors to introduce an automated methodology for the detection of Coronary Atherosclerosis, enhancing the precision of ECG interpretation via digital imaging predicated on cardiac signal detection graphs obtained through this modality. The algorithm employed to delineate the ECG signal dynamics for the diagnostic identification of Coronary Atherosclerosis is the Continuous wavelet Transform (CWT). The application of CWT follows the execution of multiple pre-processing phases on the ECG signal graph, including image cropping to isolate the graph within the ECG and segmentation to eliminate background interference, thereby concentrating on the graph for subsequent analysis. This preprocessing facilitates the interpretative process through the CWT algorithm. The analysis proceeds with a classification phase employing a Convolutional Neural Network (CNN), which differentiates between normal patient conditions and those presenting with Coronary Atherosclerosis and normal ECG. The experimental outcomes derived from the application of the proposed algorithm demonstrate a significant enhancement in the accuracy of ECG interpretation, achieving a diagnostic accuracy rate of 96% for the detection of Coronary Atherosclerosis. © 2024 IEEE.

关键词： wavelet transforms

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WaveDiff: Underwater visual Data Enhancement with wavelet-Accelerated Diffusion

WaveDiff: Underwater Visual Data Enhancement with Wavelet-Ac...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Thengane, Shrutika vishal Tan, Yu Xiang Prasetyo, Marcel Bartholomeus Meghjani, Malika Singapore University of Technology and Design Singapore

ISBN: (纸本)9798331540081

Unmanned underwater vehicles generally rely on high quality visual data and low latency for the applications of monitoring, exploration and search. Several methods have been proposed to improve underwater visibility by enhancing images taken by cameras using generative methods such as diffusion models. Although diffusion models have proven very useful for visual data enhancement, they enhance images iteratively, making this process computationally inefficient. Current methods primarily focus on improving visibility without addressing operation latency or speed, rendering these methods unsuitable for low bandwidth or fast information tracking. To overcome this, we propose using discrete wavelet transform (DWT) to decompose the image into four frequency components, reducing the spatial dimension. Out of these four components, we use only one as part of the diffusion process. At the end of the diffusion process, we apply the inverse discrete wavelet transform to obtain the enhanced image. This simple strategy helps reduce processing time by 50% with minimal loss in Peak signal to Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) on the LSUI dataset. © 2024 IEEE.

关键词： Discrete wavelet transforms

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