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

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Research on image Enhancement Technology and Signal processing Methods in Teaching Hydraulic systems

Research on Image Enhancement Technology and Signal Processi...

引用

Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Shijie Guan Pengpeng Liu Zhengyi Zhang Weiwei Dong Xiangtong Kong High-tech Institute QingZhou Shandong China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Hydraulic system as an important part of industrial automation, its teaching effect directly affects the quality of related technical personnel training. Hydraulic transmission course is a theoretical and practical combination of the post course offered by our non-commissioned officers' vocational and technical education in mechanical manufacturing technology, because the hydraulic system involves complex images and signal data, students often face difficulties in understanding and analyzing in the learning process. Through the course, students can systematically master the basic theoretical knowledge of the hydraulic transmission system, familiar with the structural composition and working principle of commonly used hydraulic components, and master the working principle and characteristics of common basic circuits. This paper proposes to combine image enhancement technology with signal processing methods to optimize the teaching of hydraulic system. By analyzing the application of image enhancement technology in image denoising, edge detection and brightness adjustment, as well as the practical effect of signal processing methods in frequency domain analysis, time-frequency analysis and intelligent algorithms, we explore how to enhance students' theoretical understanding and practical ability of hydraulic system. The study shows that the integration of image and signal processing technology can not only significantly improve the teaching efficiency, but also provide technical support for the construction of intelligent teaching mode in the future.

关键词： Training Time-frequency analysis image edge detection Education Brightness Hydraulic systems Virtual reality Signal processing image enhancement image denoising

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Tatsukichi Shibuya Nakamasa Inoue Rei Kawakami Ikuro Sato Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in fine-tuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO

关键词： Training Speech coding Memory management Signal processing algorithms Signal processing Classification algorithms Servers Speech processing Optimization image classification

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A Study on Comparative Analysis of Machine Learning and image processing Techniques for Identifying Plant Leaf Disease 7th

A Study on Comparative Analysis of Machine Learning and Imag...

引用

7th International conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Badami, Vittal Kanakaraddi, Suvarna Kalwad, Priyadarshini K L E Technological University Karnataka Hubballi India

ISBN: (纸本)9789819908370

This research investigates the approaches for identifying and classifying plant leaf diseases from digital images using deep neural networks. While diseases can affect any part of a plant and occasionally go undetected, there are some physical characteristics or symptoms that the plants exhibit that can be quickly detected using vision. These diseases can be identified and classified by machine learning algorithms using a wide range of techniques like deep neural networks, regression analysis, colour analysis and so on but not all techniques are appropriate for identifying all types of plant leaf diseases. It might be difficult to determine which approach is best for particular type of disease detection. This paper mainly focuses on image classification based on deep neural networks, object detection based on thresholding and image transformation and severity quantification with different methods like colour analysis which include Histogram of Oriented Gradients (HOG) and spot analysis and leaf area measurement using open source tool and segmentation-based calculation of affected area. This detailed analysis will be helpful to researchers working on algorithm optimization for image classification, object detection and severity quantification in identifying plant leaf diseases. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Adversarial Encoder-Driven Filter for Targeted image Tagging: A Novel Approach to Visual Content Manipulation

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

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International image processing, Applications and systems conference (IPAS)

作者： Cole Mckee Dominic Flowers Jesse Wood Ethan Shafer Department of Electrical Engineering and Computer Science United States Military Academy West Point New York

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. This paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon’s Rekognition image tagging algorithm using a black box approach.

关键词： Training Resistance Computer vision Visualization Law enforcement Military computing Computational modeling Autoencoders image annotation Closed box

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Pre-processing Techniques and Model Aggregation for Plant Disease Prevention

Pre-processing Techniques and Model Aggregation for Plant ...

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International conference on Practical Applications of Agents and Multi-Agent systems, PAAMS 2023

作者： Marco-Detchart, C. Rincon, J.A. Julian, V. Carrascosa, C. Camino de Vera s/n Valencia46022 Spain Camí de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031375927

image processing techniques have become increasingly popular in plant disease classification. However, one of the major challenges in this field is accurately identifying and classifying different diseases based on plant images. Pre-processing techniques such as smoothing and sharpening can play a crucial role in improving the accuracy of disease classification. These techniques can enhance the quality of the images and reduce noise, making it easier for machine learning algorithms to extract meaningful features from the images. In this context, these techniques can significantly improve the overall accuracy of plant disease classification systems. This paper aims to explore the potential of preprocessing techniques such as smoothing and sharpening in enhancing the quality of plant images for disease classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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VisNow-Medical—A Visual Analysis Platform for Medical Data processing 10th

VisNow-Medical—A Visual Analysis Platform for Medical Data ...

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10th KES International conference on Innovation in Medicine and Healthcare, KES-InMed 2022

作者： Regulski, Piotr Szopinski, Kazimierz Department of Dental and Maxillofacial Radiology Faculty of Medicine and Dentistry Medical University of Warsaw Binieckiego 6 St Warsaw Poland

ISBN: (纸本)9789811934391

The VisNow Medical platform is a set of integrated algorithms for visual analysis of medical data and is an extension of the VisNow platform used for image processing and visualization. VisNow Medical platform emphasizes on the ease of development of case-specific, multi-platform medical applications, with the possibility of processing one-, two-, and three-dimensional data in terms of filtering, denoising, segmentation, mapping, classification, quantitative and qualitative visual processing. VisNow Medical facilitates the research and development process in medical field. This research presents several applications of the platform for magnetic resonance, computed tomography and image processing applications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical applications

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CyberSaver - A Machine Learning Approach to Detection of Cyber Bullying 16

CyberSaver - A Machine Learning Approach to Detection of Cyb...

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16th International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Jia, Hii Lee Hameed, Vazeerudeen Abdul School, Muhammad Ehsan Rana Asia Pacific Univ Sch Comp Technol Pk Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781665426787

In this modern era of extensive use of online resources there has been reports of numerous cases of cyberbullying. Although awareness through medical health support systems such as counselling and psychological assistance is available, a system to combat threats is needed to handle the increasing rate of cyber bullying. This paper presents a model that can be used to detect and report cyberbullying with the use of machine learning techniques. A careful selection of the machine learning algorithms has been identified that could enable better accurate detection. The model was transformed into a prototype in python to evaluate the effectiveness of the model in detecting cyber bullying. The proposed model primarily focusses on test based and image-based threats as they are more common than other forms of cyber bullying.

关键词： Convolutional Neural Network Cyber Bullying Machine Learning Support Vector Machine Text processing image processing

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Compression and Denoising of Medical images Using Autoencoders 1

Compression and Denoising of Medical Images Using Autoencode...

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1st International conference on Recent Trends in Microelectronics, Automation, Computing and Communications systems, ICMACC 2022

作者： Senapati, Ranjan K. Badri, Rohit Kota, Anudeep Merugu, Nithin Sadhul, Suraj Vnr Vjiet Bachupally Dept.of Ece Nizampet Hyderabad India

ISBN: (纸本)9781665496049

In medical image analysis image compression and denoising is an important processing steps for remote analytics. A number of algorithms are proposed in the literature with varying degrees of denoising performances. In this paper, a 3-layer autoencoder model to compress and denoise grayscale medical images is proposed. Gaussian noise with a noise factor of 0.5 is added and passes through 3 convolution and 3 max pool layers. The reverse process is made for image denoising. The proposed model learns to denoise data with a high PSNR and a high compression ratio after training. Most of the denoised images show over 30 dB PSNR with a 4:1 compression ratio. images can be combined to increase sample size and overall denoising performance. © 2022 IEEE.

关键词： image denoising

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Multi-modality Medical image Fusion Employing Various Contrast Enhancement Techniques 2nd

Multi-modality Medical Image Fusion Employing Various Contra...

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Emergent Converging Technologies and Biomedical systems Select Proceedings of the 2nd International conference, ETBS 2022

作者： Jain, Shruti Pal, Bandana Sood, Rittwik Jaypee University of Information Technology Himachal Pradesh Solan Waknaghat India Indira Gandhi Delhi Technical University for Women Delhi India NIT Hamirpur Himachal Pradesh Hamirpur India

ISBN: (纸本)9789819922703

Clinical imaging has a major role in healthcare applications. The blur and the noise of the picture are eliminated, which improves the contrast and provides information about the image. But to increase the precision of the image, it is first improved. The tasks of improving medical pictures are to intensify an area of concern. Because of such issues, image processing techniques like enhancement and fusion algorithms aid human visuals in making unbiased judgments of brain images using CT and MRI. The main aim of this research paper is to exploit the benefits of spatial enhancement methods, namely binarization, median filter, contrast stretching, and contrast limited adaptive histogram equalization (CLAHE). This helps in attaining greater quality images. The enhanced images are followed by the fusion of images using principal component analysis, discrete wavelet transform, and Laplacian re-decomposition (LRU) fusion methodology. The comparative analysis is done based on qualitative and quantitative parameters and found that the results using CLAHE and LRU outperform other techniques. Later, the model is validated on the available datasets and the results are in consensus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Median filters

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Key Technologies on High-capacity Satellite imagery Compression 11

Key Technologies on High-capacity Satellite Imagery Compress...

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2023 IEEE 11th International conference on Information, Communication and Networks, ICICN 2023

作者： Fang, Huoneng Han, Pengfei Xiao, Huachao Xi'An Institute of Space Radio Technology Information Processing Technology Research Lab Xi'an China Xi'an China

ISBN: (纸本)9798350314014

With the continuous progress of science and technology, the accuracy of satellite remote sensing, detection and reconnaissance technologies is getting higher and higher, and the amount of data generated by them is also getting larger and larger. The amount of image data generated by high-resolution satellite imaging systems often reaches hundreds of Gb/s, which, coupled with the limited transmission bandwidth and storage space constraints, puts great pressure on data caching and transmission. The upper limit of transmission rate provided by traditional satellite data transmission methods has been difficult to meet the demand of inter-satellite and satellite-ground high-speed communication. To address this, a high-capacity satellite image data compression algorithm is proposed. Firstly, for the problem of redundancy of handover satellite image data, image fusion or stitching technology is used to extract valuable information from different images and synthesize high-quality images to reduce data redundancy. Secondly, to address the problem of repeated transmission of re-entry satellite data, multi-temporal data is analyzed to determine the change process of re-entry satellite image features and segment the change region in the image. In the re-entry process, only the information of the change region needs to be transmitted, avoiding multiple transmissions of duplicate information. Finally, to solve the problem of low compression efficiency of existing on-satellite compression algorithms, a new 'image-information-image' data compression strategy is explored to achieve high compression ratio and high fidelity of satellite image compression. In order to achieve high capacity compression and efficient transmission of satellite images with limited bandwidth and storage space, and to lay the foundation for fast and efficient intelligent services for all-day, all-weather observation of the Earth. © 2023 IEEE.

关键词： image compression

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