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

共 13626 条记录，以下是51-60 订阅

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Medical image Segmentation Techniques: Advances and Challenges 6

Medical Image Segmentation Techniques: Advances and Challeng...

引用

6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Boulehmi, Hela Filali, Rania Linda DGET ISET de Bizerte Bizerte Tunisia

ISBN: (纸本)9798331506520

Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in image processing technologies has led to the development of numerous segmentation methods, encompassing traditional algorithms, machine learning (ML)-driven approaches, and cutting-edge deep learning (DL) techniques. This study undertakes a comparative evaluation of these methods, focusing on their efficiency, accuracy, and suitability across different medical imaging modalities. It also delves into prominent segmentation techniques like thresholding, region-based methods, edge detection, graph cuts, active contour models, and convolutional neural networks (CNNs). Additionally, the paper explores ongoing challenges and prospective advancements aimed at enhancing segmentation efficacy in medical imaging. © 2025 IEEE.

关键词： Convolutional neural networks

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An Investigation on Classical Methods and Unsupervised Deep Learning algorithms for image Segmentation in High Variance images for Industrial Application 35

An Investigation on Classical Methods and Unsupervised Deep ...

引用

35th Irish systems and Signals conference, ISSC 2024

作者： Henry, Nathan Reiter, Tamas

ISBN: (纸本)9798350352986

This paper investigates classical image processing techniques and unsupervised deep learning algorithms for segmenting images with high variance for an under researched industrial problem, focusing on beam burns generated by Focused Ion Beam (FIB) technology. Classical methods (CM) include preprocessing techniques such as edge detection, while Deep Learning (DL) employs Convolutional Neural Networks (CNNs) for unsupervised image segmentation. Experimental results demonstrate the superior accuracy of DL, but with increased computational overhead, in contrast to the efficiency and controllability of CM. Combining both approaches offers a potential solution for optimizing accuracy and efficiency in image segmentation. © 2024 IEEE.

关键词： image segmentation

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Implementation of a Comparative Study of Convolutional Neural Network Architectures for image Blind Noise Elimination 4th

Implementation of a Comparative Study of Convolutional Neura...

引用

4th International conference on Intelligent systems and Pattern Recognition-ISPR

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Univ Sidi Mohamed Ben Abdellah Dept Math & Comp Sci M2PA Lab ENS Fes 30030 Morocco

ISBN: (纸本)9783031821523;9783031821530

The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of image processing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. These methods have demonstrated remarkable performance in image processing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. These findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study.

关键词： Deep Learning image processing image Denoising Convolutinal Neural Networks (CNNs) Blind Noise DnCNN DRNet RIDNET PSNR SSIM

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Detecting Multiple Mental Health Disorders with Large Language Models 28

Detecting Multiple Mental Health Disorders with Large Langua...

引用

28th International conference on Information Visualisation (iv)

作者： Nanda, Mehul Inkpen, Diana Dargel, Aroldo Univ Ottawa Ottawa ON Canada Ottawa Hosp Ottawa ON Canada

ISBN: (纸本)9798350380170;9798350380163

Artificial Intelligence systems have become useful in many sectors, including healthcare. In this paper, we focus on detecting signs of mental health disorders from social media texts. This can be used to direct patients to consult a healthcare professional, while on waiting lists, or for post-monitoring. The performance of the current algorithms for detecting multiple disorders is limited. We propose a new method to increase performance by including target-domain knowledge for each type of mental health disorder (for nine disorders). We used this information to select the best training examples to include in our carefully engineered prompts for performing few-shot learning. We show that the results improved when compared to zero-shot learning based on Large Language Models and when compared to state-of-the-art results on the same test set.

关键词： AI and Healthcare Predictions Natural Language processing Transformers BERT Large Language Models Domain Knowledge

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Classification of Red Watermelon Varieties Using Canny Edge Detection and CNN

Classification of Red Watermelon Varieties Using Canny Edge ...

引用

IEEE International conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Martinez, Milca Rejaiah B. Dayrit, Kian Matthew D. Yumang, Analyn N. Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

ISBN: (纸本)9798350372113;9798350372106

Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most sought-after fruits globally. With the watermelon fruit consisting of different varieties, consumers usually have difficulty in classifying watermelons due to their similar external appearances, especially when labeled under the same name. This study is conducted to implement a system that detects and classifies three red watermelon varieties, Red Export, Orchid Sweet, and Dixie Queen, with image processing algorithms. The researchers utilized Canny Edge Detection for the dataset's preprocessing phase and Convolutional Neural Network (CNN) for its classification. The Raspberry Pi is also applied to this study. Moreover, the researchers created and collected their datasets for testing and validation data. The model used in this study has acquired 84.71% overall accuracy.

关键词： Watermelon Classification image Classification Canny Edge Detection CNN image processing Raspberry Pi

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The application of deep learning algorithms in power system load forecasting 6

The application of deep learning algorithms in power system ...

引用

6th International conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Wang, Shilin Cui, Wei Yang, Yang Meng, Xiangliang State Grid Information & Telecommunication Group Co. Ltd. No. 10 Xuanwumen Outer Street Xicheng District 95555-0345 Beijing100029 China

ISBN: (纸本)9781510682962

This article explores the application of deep learning (DL) algorithms in power system load forecasting. With the continuous advancement of the construction of new power systems, traditional load forecasting models designed based on time series analysis are no longer in line with the actual needs of the current power system. To improve the accuracy of load forecasting, this paper attempts to transform time series data into images for analysis, and then use deep learning methods widely used in the field of image processing for power load forecasting. Convolutional neural network (CNN), as a commonly used image processing algorithm, has been applied in time series data processing. However, data is still processed as a sequence matrix, and the advantages of CNN algorithm in processing image matrices have not been fully utilized. Therefore, this article proposes a CNN algorithm based on sequence to image conversion (STI-CNN). Firstly, the load sequence is converted into a load image. Then, a dual branch deep network model is used to accurately cluster the input data. Finally, the STI-CNN model is used for load prediction. Matlab simulation experiments show that the proposed STI-CNN model has excellent performance in different prediction metrics and has higher prediction accuracy. © 2024 SPIE.

关键词： Data modeling Data conversion image processing systems modeling Convolutional neural networks Evolutionary algorithms Statistical modeling Education and training Support vector machines

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Identification of a Person Based on Block processing of a Signature image

Identification of a Person Based on Block Processing of a Si...

引用

2024 International Russian Smart Industry conference, SmartIndustryCon 2024

作者： Rizaev, Ildus Takhavova, Elza Shleimovich, Mikhail Kazan National Research Technical University Named after A.N. Tupolev-KAI Automated Systems for Information Processing and Control Department Kazan Russia

ISBN: (纸本)9798350395044

The development of computer vision systems stimulates the development of various applications in the field of image recognition. Methods and algorithms for image recognition in document processing systems play a crucial role. There is a lot of research in this direction, and one of the most difficult problems is handwritten text recognition. This includes the tasks of signature identification, which the authors of the paper consider. The article considers the main stages of signature analysis to establish authorship. They are the preprocessing of the signature, representation in a form convenient for applying author identification. A dataset containing sample signatures of various people is used. The described tools became means to develop software for preprocessing images of signatures, extracting characteristics and identifying the author of the signature. © 2024 IEEE.

关键词： Textures

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The Disease Prediction System based on Symptom Identification and image processing

The Disease Prediction System based on Symptom Identificatio...

引用

2024 International conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Swathi, M. Prithika, N.G. Sri Aishwariya, N. Jayaram, B. Dept.of.Computer Science And Business Systems Rmk Engineering College Thiruvallur India Dept.of.Computer Science And Business Systems Rmk Engineering College Thiruvallur India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366846

This paper aims to investigate a system that uses various machine-learning algorithms to predict symptoms and deep-learning techniques for image processing that leads to early disease prediction, an essential aspect of today's healthcare industry. The main objective of this system is to enable both the healthcare professionals and the patients to interpret the results at a faster rate. Existing systems provide results based on only symptoms which is not very effective in real-time but our system works well with both symptoms and image-based specifications. This investigation explores several algorithms like Random Forest, Decision tree, XGBoost, Logistic regression, Long-term short memory (LSTM), and Naïve Bayes for symptom-based prediction [14]. CNN (Convolutional Neural Network) deep learning techniques, notably DenseNet121, are used for image processing. The use of image processing bridges the gap between the user who feels symptom-based prediction to be a bit difficult. Furthermore, diverse experiments are done on multiple datasets to establish more accurate results. This system also ensures the safeguarding of sensitive information. The proposed disease prediction system helps empower healthcare professionals and individuals with proactive insights into potential health risks by giving accurate predictions for early intervention of the disease. © 2024 IEEE.

关键词： Forecasting

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Beyond RGB: A Real World Dataset for Multispectral Imaging in Mobile Devices

Beyond RGB: A Real World Dataset for Multispectral Imaging i...

引用

IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Glatt, Ortal Ater, Yotam Kim, Woo-Shik Werman, Shira Berby, Oded Zini, Yael Zelinger, Shay Lee, Sangyoon Choi, Heejin Soloveichik, Evgeny Samsung Israel R&D Ctr Tel Aviv Israel Samsung Adv Inst Technol Suwon South Korea

ISBN: (纸本)9798350318920;9798350318937

Multispectral (MS) imaging systems have a wide range of applications for computer vision and computational photography tasks, but do not yet enjoy widespread adoption due to their prohibitive costs. Recently, advances in the design and fabrication of photonic metamaterials have enabled the development of MS sensors suitable for integration into consumer grade mobile devices. Augmenting existing RGB cameras and their processing algorithms with richer spectral information has the potential to be utilized in many steps of the image processing pipeline, but diverse real world datasets suitable for conducting such research are not freely available. We introduce Beyond RGB(1), a real-world dataset comprising thousands of multispectral and RGB images in diverse real world and lab conditions that is suitable for the development and evaluation of algorithms utilizing multispectral and RGB data. All the scenes in our dataset include a colorimetric reference and a measurement of the spectrum of the scene illuminant. Additionally, we demonstrate the practical use of our dataset through the introduction of a novel illuminant spectral estimation (ISE) algorithm. Our algorithm surpasses the current state-of-the-art (SoTA) by up to 58.6% on established benchmarks and sets a baseline performance on our own dataset.

关键词： algorithms algorithms Computational photography Datasets and evaluations image and video synthesis

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Practical real-time image compression for resource-challenged devices

Practical real-time image compression for resource-challenge...

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conference on Multimodal image Exploitation and Learning

作者： Pham, Kevin Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673854;9781510673847

In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional image processing tasks. However, many of the existing solutions in this domain are burdened by computational complexity, rendering them unsuitable for real-time deployment on standard devices as they often necessitate complex systems and substantial energy consumption. This work addresses the growing paradigm of edge computing for real-time applications by introducing a novel, on-edge device solution. This innovative approach aims to strike a balance between efficiency and accuracy, adhering to the practical constraints of real-world deployment. By presenting demonstrations of the proposed solution's performance on readily available devices, we provide tangible evidence of its applicability and viability in real-world scenarios. This advance contributes to the ongoing dialogue about the need for accessible and efficient image compression algorithms that can be deployed real-time applications on edge devices, bridging the gap between the demanding computational requirements of deep learning and the practical limitations of everyday hardware. As data continues to surge, solutions like this become ever more critical in ensuring effective image compression, aligning with on-edge computing within AI. This research paves the way for improved image processing in real-time applications while conserving computational resources and energy consumption.

关键词： image compression efficient AI real time processing

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