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

共 14690 条记录，以下是981-990 订阅

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OCTA-Based Biomarker Characterization in nAMD

OCTA-Based Biomarker Characterization in nAMD

引用

International image processing, Applications and systems conference (IPAS)

作者： Maria Simona Tivadar Ioana Damian Adrian Petru Groza Simona Delia Nicoara Department of Computer Science Technical University of Cluj-Napoca Romania European University of Technology European Union Department of Ophthalmology "Iuliu Hatieganu" University of Medicine and Pharmacy Emergency County Hospital Cluj-Napoca Romania

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

We aim to enhance ophthalmologists’ decision-making when diagnosing the Neovascular Age-Related Macular Degeneration (nAMD). We developed three tools to analyze Optical Coherence Tomography Angiography images: (1) extracting biomarkers such as mCNV area and vessel density using image processing; (2) generating a 3D visualization of the neovascularization for a better view of the affected regions; and (3) applying an ensemble of three white box machine learning algorithms (decision tree, support vector machines and DL-Learner) for nAMD diagnosis. The learned expressions reached 100% accuracy for the training data and 68% accuracy in testing. The main advantage is that all the learned models white-box, which ensures explainability and transparency, allowing clinicians to better understand the decision-making process.

关键词： Three-dimensional displays Accuracy Optical coherence tomography image processing Angiography Decision making Medical services Machine learning Biomarkers Glass box

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Determination of Leaf Diseases Using Deep Learning 9

Determination of Leaf Diseases Using Deep Learning

引用

9th IEEE International conference on Smart Structures and systems, ICSSS 2023

作者： Babu, T.R. Ganesh Praveena, R. Sudha, G. Elango, S. Muthayammal Engineering College Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Muthayammal Engineering College Department of Electronics and Communication Engineering Namakkal India Muthayammal Engineering College Department of Biomedical Engineering Namakkal India Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India

ISBN: (纸本)9798350384208

Agriculture is the back bone of India and it is going down because of many reasons and one of the main reasons is plants getting affected by diseases. In this process the data's are collected from Kaggle data set. The network includes three classes early blights, late blights and healthy conditions of the leaves which works on TensorFlow platform. In this work Convolutional Deep neural network helps the model to learn using various layers based on looping process. It uses the multiclass classification. This paper proposes of various modules which includes (i) Pre-processing module (ii) Normalization (iii) Layers (iv) Optimizer (v) Model fit (v) E-poach. © 2023 IEEE.

关键词： image classification

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The Implementation and Optimization of High-Performance image processing algorithms Based on Artificial Intelligence

The Implementation and Optimization of High-Performance Imag...

引用

Integrated Intelligence and Communication systems (ICIICS), International conference on

作者： Caizeng Ye Peng Wang Shandong Institute of Commerce and Technology Jinan China Jinan Engineering Polytechnic Jinan China

image processing (IP) technology has emerged on the basis of AI, digital imaging technology, and multimedia technology, and people began to use computers to process images to improve image quality and improve human vision. With the advancement of computer vision and AI technology, people want to achieve high performance IP. In the field of IP, techniques such as image segmentation, image compression, and image restoration are active research topics in computer vision. In this paper, we propose IP algorithms such as Artificial Bee Colony(ABC) Optimization Algorithm and Searcher Optimization Algorithm (SOA) for this block of image compression, analyze the image compression effect of these two algorithms, and compare the image compression quality by comparing the PSNR of each algorithm, and the results get that the image compression effect based on artificial bee colony(ABC) Algorithm is better and the image compression code book quality is better with the same compression ratio.

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CHARACTERIZATION OF WETTING PROPERTIES OF MAGNETOACTIVE ELASTOMER SURFACES 16

CHARACTERIZATION OF WETTING PROPERTIES OF MAGNETOACTIVE ELAS...

引用

ASME 16th Annual conference of the Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

作者： Kriegl, Raphael Kravanja, Gaia Hribar, Luka Jezersek, Matija Drevensek-Olenik, Irena Shamonin, Mikhail Ostbayer Tech Hsch Regensburg Regensburg Germany Univ Ljubljana Fac Mech Engn Ljubljana Slovenia Univ Ljubljana Fac Math & Phys Ljubljana Slovenia J Stefan Inst Ljubljana Slovenia

ISBN: (纸本)9780791887523

Commercially available contact angle (CA) measuring devices usually do not allow for the application of magnetic fields to the sample under test. A setup for measuring the CA of liquids on magnetosensitive surfaces has been developed specifically for investigating the surfaces of magnetoactive elastomers (MAEs). The addition of a programmable linear stage, which moves a permanent magnet, allows for fine control of the magnetic field applied to the MAE without the need for large and power-consuming electromagnets. Paired with a custom control and evaluation software, this measurement setup operates semiautomatically, limiting operator error and increasing precision, speed, as well as repeatability of static and dynamic CA measurements for different magnetoactive materials. The software is equipped with robust droplet fitting algorithms to avoid experimental challenges arising with soft magnetoactive materials, such as the curling of sample edges or diffuse non-reflective surfaces. Several application examples on MAE surfaces, both processed and unprocessed, are presented.

关键词： magnetoactive elastomers image processing droplet contour fitting contact angle

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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...

引用

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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Hyperspectral image processing with Machine Learning and Mathematical Models 7th

Hyperspectral Image Processing with Machine Learning and Ma...

引用

7th International conference on Soft Computing and Signal processing, ICSCSP 2024

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

ISBN: (纸本)9789819799251

Hyperspectral imaging is a fast-growing imaging technique in many fields, like remote sensing, fruit analysis, clinical images, etc. The spectral image consists of two parts: spectral data and spatial data. The processing of spectral images includes the processing of the spatial part as well as the spectral part. This paper analyzes various methods and algorithms that are used for spectral image processing in existing systems and proposes a diagram for hyperspectral image processing. There are five important steps for processing the spectral image and analyzing the region selection and dimensionality reduction methods in detail. Comparing the performance of region selection and dimensionality reduction techniques, the Rembg algorithm and Principal Component Analysis (PCA), respectively, show very good results. A well processed image has better performance in the classification and regression steps, and it reduces the computational time and cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Principal component analysis

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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...

引用

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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