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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XII"

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Heart Diseases Classification by ECG Trace images Using Deep Learning 4th

Heart Diseases Classification by ECG Trace Images Using Deep...

引用

4th International conference on Digital Technologies and Applications (ICDTA)

作者： Ali, Sajad M. Daway, Hazim G. Kadhim, Ahlam M. Mustansiriyah Univ Coll Sci Dept Phys Baghdad Iraq

ISBN: (纸本)9783031686498;9783031686504

Recently, many deep learning algorithms have emerged as advanced techniques in the medical field for diagnosing diseases, including heart disease. In this study, an approach was followed that is based on electrocardiogram (ECG) images to detect different heart diseases. Pre-processing was performed for the data images using morphology technology to remove lines from the background ECG paper image to obtain an image containing only the changes of electrical activity for the potion's heart. The pre-processed data images are trained at a rate of 80% of each class data image in the training stage and 20% of each class image used for the testing stage in the efficiency evaluating stage of each model. Seven classification models have been proposed in binary classification. Models 1-7 have been trained to classify the natural ECG case (Nrm) with the other diseases. Models' efficiency is calculated using four measures, where the accuracy reaches 100%, the precision reaches 100%, the specificity is 100%, and the f1-score is 100%. For models 6 and 7, the results of the accuracy reached (88.1366 and 91.0978)%, precision (80.7443 and 91.0834)%, specificity (79.1734 and 88.8665)%, and f1-score (79.4476 and 89.8999) %. The proposed diagnostic system is fast, accessible, more sensitive, and harmless. It is also more cost-effective than any other diagnostic method.

关键词： CNN Deep Learning ECG images pre-processing data

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algorithms of Signal processing in the System for High-Speed Corrugation Monitoring of Rails 6

Algorithms of Signal Processing in the System for High-Speed...

引用

6th International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2024

作者： Koluchkin, Vasiliy Ya. Marenov, Nikita E. Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9798350382891

In real operating conditions of the control systems based on the parallax method with structured laser illumination, due to background solar illumination, nonlinear distortions of signals, known as the blooming effect, may occur. Such distortions lead to a decrease in the accuracy of the systems of operational control of straightness of railroad rails based on the above method. The purpose of the research presented in this paper is to evaluate the effectiveness of the subpixel peak position refinement algorithm under normal conditions and under background illumination conditions in an operational high-precision inspection system. As a result of numerical experiments of the rail surface straightness control system model, the effectiveness of subpixel refinement algorithms is investigated in the presence of internal noise of the image registration device, as well as in the presence of distortions caused by the blooming effect. © 2024 IEEE.

关键词： Geometrical optics

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Vision Transformer Based Hash Coding for Efficient image and Audio Retrieval with Global and Local Equilibrium Distance Constraints

Vision Transformer Based Hash Coding for Efficient Image and...

引用

2024 IEEE International conference on systems, Man, and Cybernetics, SMC 2024

作者： Liu, Ye Pan, Yan Yin, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Lizhi Inc. Artificial Intelligence and Big Data Department Guangzhou Beijing China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665410205

With the application of efficient retrieval in information systems and retrieval augmented generation with vector database for large language models, hash coding algorithms have made progress in recent years. The rise of transformer technology in the field of deep learning has brought the possibility to further improve the effect of hash coding algorithms. We introduce the vision transformer framework to both images and audios, and propose a novel approach for the tasks of multi-label image retrieval and audio event retrieval. In the proposed hash coding model, global and local equilibrium distance constraints are integrated, so that the hash codes for images can be better obtained through the global hash centers and local similar samples. In order to realize end-to-end training and hash code generation for audios, we adopt the adapter of mel spectrogram, thus the proposed approach can be simply converted and applied to audio hash coding. Comparative experiments verify that better results can be achieved on multiple image and audio datasets. © 2024 IEEE.

关键词： image coding

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Recognition of Lawn Weeds based on Retinex Algorithm with Local Density Fusion 15

Recognition of Lawn Weeds based on Retinex Algorithm with Lo...

引用

15th International conference on Signal processing systems, ICSPS 2023

作者： Hua, Chunjian Li, Xiuqin Zhang, Airong School of Mechanical Engineering Jiangnan University Jiangsu China Wuxi Pioneer Intelligent Equipment Company China

ISBN: (纸本)9781510675056

In order to solve the problem of difficulty in segmenting the foreground of lawn weed images due to the similarity between the foreground and background grayscale, this paper proposes a Retinex enhancement algorithm based on local density fusion. Firstly, image preprocessing is completed based on local variance to realize prominent image foreground and smooth cluttered background;Secondly, using multi threshold segmentation and operational differentiation, pixels are divided into three categories: foreground, background, and pixels to be subdivided. Local density is used to extract spatial information of pixels to be subdivided;Finally, the sigmoid function is used to optimize the reflection component grayscale transformation coefficient, achieving the fusion of local density information and obtaining the final image. The results show that the algorithm proposed in this paper expands the gray difference between weeds and lawn grass while suppressing background noise;Compared with other enhancement algorithms, the peak signal-to-noise ratio, average running time, and segmentation accuracy have all been improved greatly. At the same time, through image analysis, it has been found that the algorithm results in occasional problems of uneven brightness and over segmentation of fine leaf weeds in the image. In response to these two problems, this article finally proposes two corresponding optimization measures, namely global brightness adjustment based on a new adaptive gamma function and Retinex reflection component saturation adjustment based on AHE. © 2024 SPIE.

关键词： image enhancement

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ENHANCED BIT-RATE PERFORMANCE FOR VISIBLE LIGHT COMMUNICATION systems BETWEEN LED AND MOBILE CAMERA 10

ENHANCED BIT-RATE PERFORMANCE FOR VISIBLE LIGHT COMMUNICATIO...

引用

10th IEEE International conference on Communications and Electronics (IEEE ICCE)

作者： Nam Hoang Nguyen Quan Dinh Nguyen Hanoi Univ Sci & Technol HUST Sch Elect & Elect Engn Hanoi Vietnam Hanoi Univ Sci & Technol HUST Sch Elect & Elect Engn Dept Automat Hanoi Vietnam

ISBN: (纸本)9798350379808;9798350379792

Multiple Pulse Position Modulation (MPPM) has become an important method in optical communication, especially between LEDs and mobile cameras. This paper proposes an MPPM modulation and demodulation method for Visible Light Communication (VLC) systems using LED bulbs and the camera for the transceiver that addresses data transmission performance barriers when increasing the distance between receiver and transmitter, as well as helps minimize comparison error rates compared with other modulation techniques. The PPM and MPPM modulation methods are both highly rated for their power and bandwidth efficiency. Using binary codes and image data processing algorithms at the receiver, along with optimized mapping, aids in minimizing character errors and enhancing communication performance. Additionally, integrating MPPM into the VLC system solves the problem of brightness control in real-world scenarios. The MPPM-based brightness control system is capable of dynamically adjusting brightness, providing higher communication performance and stability for the VLC system.

关键词： Multiple Pulse Position Modulation (MPPM) Visible Light Communication (VLC) Optical Camera Communication (OCC) image processing CMOS sensor

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An image processing Approach for Weed Detection Using Deep Convolutional Neural Network 6th

An Image Processing Approach for Weed Detection Using Deep C...

引用

6th International conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Aparna, Yerrolla Bhaskar, Nuthanakanti Srujan Raju, K. Divya, G. Ali Ahammed, G.F. Banu, Reshma Department of CSE CMR Technical Campus Telangana Hyderabad India Department of IT CMR Technical Campus Telangana Hyderabad India Department of CSE VTU Centre for Post Graduate Studies Karnataka Mysuru India Department of CSE VVIET Karnataka Mysuru India

ISBN: (纸本)9789819984503

According to weeds increased competition with crops, they have been given responsible for 45% of crop losses in the agricultural industry. This percentage can be decreased with an effective method of weed detection. The conventional weeding techniques take a long time and largely require manual labor. So, this process has to be automated. As a result, an image processing approach for weed detection implementing Deep Convolutional Neural Network is provided in this analysis. The purpose of this study, the possibilities of classifying and detecting weeds from Unmanned Aerial Vehicle (UAV) images using deep learning techniques. The presented method will achieve high accuracy when compared to state-of-the-art algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Learning algorithms

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image Compression and Reconstruction Based on Quantum Network

Image Compression and Reconstruction Based on Quantum Networ...

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1st International conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Ji, Xun Liu, Qin Huang, Shan Chen, Andi Wu, Shengjun Nanjing Univ Sch Phys Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Collaborat Innovat Ctr Adv Microstruct Nanjing 210093 Peoples R China Nanjing Univ Inst Brain Sci Nanjing 210023 Peoples R China Nanjing Univ Kuang Yarning Honors Sch Nanjing 210023 Peoples R China Univ Sci & Technol China Hefei Natl Lab Hefei 230088 Peoples R China

ISBN: (纸本)9798350364613;9798350364606

Quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks make image reconstruction more efficient and accurate. They can also process more complex image information using fewer bits and faster parallel computing capabilities. Therefore, this paper will discuss image reconstruction methods based on our quantum network and explore their potential applications in image processing. We will introduce the basic structure of the quantum network, the process of image compression and reconstruction, and the specific parameter training method. Through this study, we can achieve a classical image reconstruction accuracy of 97.57%. Our quantum network design will introduce novel ideas and methods for image reconstruction in the future.

关键词： image compression and reconstruction quantum network parameter training

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Hierarchy-Agnostic Unsupervised Segmentation: Parsing Semantic image Structure 38

Hierarchy-Agnostic Unsupervised Segmentation: Parsing Semant...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Rossetti, Simone Pirri, Fiora DIAG Sapienza University of Rome Italy DeepPlants Italy

Unsupervised semantic segmentation aims to discover groupings within images, capturing objects' view-invariance without external supervision. Moreover, this task is inherently ambiguous due to the varying levels of semantic granularity. Existing methods often bypass this ambiguity using dataset-specific priors. In our research, we address this ambiguity head-on and provide a universal tool for pixel-level semantic parsing of images guided by the latent representations encoded in self-supervised models. We introduce a novel algebraic approach that recursively decomposes an image into nested subgraphs, dynamically estimating their count and ensuring clear separation. The innovative approach identifies scene-specific primitives and constructs a hierarchy-agnostic tree of semantic regions from the image pixels. The model captures fine and coarse semantic details, producing a nuanced and unbiased segmentation. We present a new metric for estimating the quality of the semantic segmentation of discovered elements on different levels of the hierarchy. The metric validates the intrinsic nature of the compositional relations among parts, objects, and scenes in a hierarchy-agnostic domain. Our results prove the power of this methodology, uncovering semantic regions without prior definitions and scaling effectively across various datasets. This robust framework for unsupervised image segmentation proves more accurate semantic hierarchical relationships between scene elements than traditional algorithms. The experiments underscore its potential for broad applicability in image analysis tasks, showcasing its ability to deliver a detailed and unbiased segmentation that surpasses existing unsupervised methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep Learning-Driven Black Box Approach For image Segmentation 1

Deep Learning-Driven Black Box Approach For Image Segmentati...

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1st IEEE International conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sudha, Nuthalapati Kasetty, Sai Bhargav Madhuri, K. Sai Nikitha, Jajala Margret, Issac Neha Rajakumar, K. Mahindra University School of Engineering Telangana Hyderabad India V.I.T - University School of Computer Science and Engineering Tamilnadu Vellore India BVRIT Narsapur Dept. of Computer Science and Data Science Telangana Hyderabad India VNR VIET Dept. of CSE - AIML and IoT Telangana Hyderabad India

ISBN: (纸本)9798350356816

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. Computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation. © 2024 IEEE.

关键词： image segmentation

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Radar based Continuous Indoor Activity Recognition using Deep Learning 6

Radar based Continuous Indoor Activity Recognition using Dee...

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conference on Big Data VI - Learning, Analytics, and Applications

作者： Breaker, Henry Hamza, Syed Ali Widener Univ Sch Engn Chester PA 19013 USA

ISBN: (纸本)9781510673915;9781510673908

Radar-based sensing emerges as a promising alternative to cameras and wearable devices for indoor human activity recognition. Unlike wearables, radar sensors offer non-contact and unobtrusive monitoring, while being insensitive to lighting conditions and preserving privacy as compared to cameras. This paper addresses the task of continuous and sequential classification of daily life activities, unlike the problem to isolate distinct motions in isolation. Upon acquiring raw radar data containing sequences of motions, an event detection algorithm, the Short-Time-Average/Long-Time-Average (STA/LTA) algorithm, is utilized to detect individual motion segments. By recognizing breaks between transitions from one motion type to another, the STA/LTA detector isolates individual activity segments. To ensure consistent input shapes for activities of varying durations, image resizing and cropping techniques are employed. Furthermore, data augmentation techniques are applied to modify micro-Doppler signatures, enhancing the classification system's robustness and providing additional data for training.

关键词： image segmentation Radar Radar signal processing Education and training Matrices Doppler effect Sensors Motion detection image classification image processing algorithms and systems

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