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

共 13657 条记录，以下是321-330 订阅

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Utilising Machine Learning for Modulation and Signal processing 2

Utilising Machine Learning for Modulation and Signal Process...

引用

2nd IEEE World conference on Communication and Computing (WCONF)

作者： Singh, Daljeet Pal Solanki, Madhav Singh Jyothi, Renuka S. Maharishi Univ Informat Technol Maharishi Sch Engn & Technol Uttar Pradesh India Sanskriti Univ Dept Comp Sci & Engn Uttar Pradesh India Jain Deemed Be Univ Dept Biotechnol & Genet Sch Sci Bangalore Karnataka India

ISBN: (纸本)9798350395334;9798350395327

Device studying (ML) is an effective device that has been used in many packages, consisting of virtual sign processing, communications structures, and modulation reputation. In particular, ML has been extensively studied for modulation recognition, which is a hard hassle because of the always-changing and unpredictable nature of the sign. With the aid of leveraging ML strategies, which can be used to quantify and check specific houses of a signal, reliable modulation popularity and signal processing strategies can be evolved. For instance, ML can be used to automatically discover modulation class when the image shape of the signal is unknown. It is achieved via reading the sign with device learning techniques consisting of Fourier transform;waveletbased totally function extraction, and convolutional neural networks. Different applications encompass channel estimation, signal detection, and optimization of verbal exchange networks. Further to recognizing modulation codecs, ML-based algorithms may be used to enhance signal processing and communique systems. It could be done with the aid of developing smarter systems that are capable of studying global data. ML can be used to robotically apprehend anomalous alerts and allow networks to quickly adapt to converting situations. ML techniques can also be used to optimize community parameters, which include power management, coding schemes, and modulation codecs, so as to maximize their performance.

关键词： Consisting Communications Modulation Techniques Parameters Management

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Feature identification for parameter extraction and defect detection using machine learning 38

Feature identification for parameter extraction and defect d...

引用

conference on Metrology, Inspection, and Process Control XXXVIII

作者： Guo, Y. Pahlavani, H. Khachaturiants, A. Elsayed, K. van de Laar, J. Simons, E. Saikumar, N. Sadeghian, H. Nearfield Instruments BV Rotterdam Netherlands

ISBN: (纸本)9781510672178;9781510672161

Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput but also in terms of the richness of data to be extracted to enable engineers to fine-tune the process steps for increased yield. The move towards 3D structures requires extraction of critical dimension parameters from structures which can vary largely from layer to layer. For in-line process control, the necessary automation forces the development of layer and equipment-specific dedicated image processing algorithms. Similarly, with the increase in stochastic defects in the EUV era, detection of defects at the nm scale requires the identification of features captured in low resolution to meet the throughput requirements of HVM fabs, which can again lead to custom algorithm development. With the emergence of ML-based image processing methods, this process of algorithm development for both cases can be accelerated. In this work, we provide the general framework under which the images obtained from high-speed scanning probe microscopy-based systems can be used to train a network for either feature detection for parameter extraction or defect identification.

关键词： Scanning Probe Microscopy image processing Parameter extraction Defect detection Machine learning

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A Sequential Method for Enhancing the image Quality of Rice Leaf Diseases by Employing Various Preprocessing Techniques 9

A Sequential Method for Enhancing the Image Quality of Rice ...

引用

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

作者： Govindarajan, S. Mary Vennila, S. Presidency College Pg and Research Department of Computer Science Chennai India

ISBN: (纸本)9798350384208

Rice is a fundamental staple crop worldwide, and monitoring the health and growth of rice plants is crucial for ensuring optimal yield and quality. Automated rice leaf segmentation from images plays a vital role in plant health assessment and disease detection, enabling early intervention and management. This study presents an approach for rice leaf segmentation using image processing techniques, aiming to automate the analysis of rice plant health. The proposed methodology involves several key steps. First, pre-processing techniques such as grayscale conversion, noise reduction, and contrast enhancement are applied to prepare the input images. Subsequently, various segmentation algorithms, including thresholding, edge detection, and region-based methods, are employed to isolate the rice leaves from the background. Feature extraction is then performed to capture relevant characteristics of the leaves, facilitating a detailed analysis. © 2023 IEEE.

关键词： image segmentation

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Content-Based Retinal image Analysis: Exploring the Impact of Similarity Measures and Machine Learning on LTP Features

Content-Based Retinal Image Analysis: Exploring the Impact o...

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2024 IEEE Students conference on Engineering and systems, SCES 2024

作者： Santra, Arpita Singh, Vibhav Prakash Dewang, Rupesh Kumar MNNIT Allahabad Computer Science Engineering Prayagraj India

ISBN: (纸本)9798350374711

Diabetic Retinopathy (DR) is a retinal condition resulting in damage to blood vessels within the eye, serving as a leading cause of vision impairment or blindness when not addressed. Manual identification of diabetic retinopathy is labor-intensive and susceptible to human error due to the intricate nature of the eye's structure. This work offers a comprehensive method that uses image pre-processing techniques, local ternary pattern (LTP) features, machine learning and different similarity measures to improve content based retinal image analysis and retrieval from fundus images. With an emphasis on feature extraction, subtle texture details were extracted from retinal fundus images using Local Ternary Patterns (LTP) with different radius values, specifically concentrating on radius 1, 2, and 3. This study includes two parts: first, it assesses machine learning algorithms for the classification of diabetic retinopathy. Of these, Random Forest performed the best, with an accuracy of 92.77%. Second, the research works on retinal image retrieval and compares various LTP variants' abilities to different retrieval tasks. To evaluate model performance for particular disease classes, class-wise image retrieval was carried out, providing information on individual class precision. Several similarity measures were investigated for retinal image retrieval, demonstrating the effectiveness of Cosine Similarity in achieving better retrieval results. © 2024 IEEE.

关键词： Decision trees

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When is Multicalibration Post-processing Necessary? 38

When is Multicalibration Post-Processing Necessary?

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Hansen, Dutch Devic, Siddartha Nakkiran, Preetum Sharan, Vatsal University of Southern California United States Apple United States

Calibration is a well-studied property of predictors which guarantees meaningful uncertainty estimates. Multicalibration is a related notion - originating in algorithmic fairness - which requires predictors to be simultaneously calibrated over a potentially complex and overlapping collection of protected subpopulations (such as groups defined by ethnicity, race, or income). We conduct the first comprehensive study evaluating the usefulness of multicalibration post-processing across a broad set of tabular, image, and language datasets for models spanning from simple decision trees to 90 million parameter fine-tuned LLMs. Our findings can be summarized as follows: (1) models which are calibrated out of the box tend to be relatively multicalibrated without any additional post-processing;(2) multicalibration post-processing can help inherently uncalibrated models and also large vision and language models;and (3) traditional calibration measures may sometimes provide multicalibration implicitly. More generally, we distill many independent observations which may be useful for practical and effective applications of multicalibration post-processing in real-world contexts. We also release a python package implementing multicalibration algorithms, available via 'pip install multicalibration'. © 2024 Neural information processing systems foundation. All rights reserved.

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Applications of Superpixels on Phase Contrast Microscopy 5

Applications of Superpixels on Phase Contrast Microscopy

引用

5th International conference on Transdisciplinary AI, TransAI 2023

作者： On, Vincent Zahedi, Atena Phandthong, Rattapol California State University Bakersfield Department of Computer and Electrical Engineering and Computer Science BakersfieldCA United States Stem Cell Research Center University of California Irvine IrvineCA United States University of California Riverside Department of Molecular Cell and Systems Biology RiversideCA United States

ISBN: (纸本)9798350358018

Cellular microscopy is enhanced by computational paradigms such as image processing, computer vision, and machine learning. image segmentation is vital for quantifying cell images, enabling tracking and subsequent applications. Superpixel algorithms offer a promising solution to challenges in phase contrast image segmentation, as they group pixels with similar characteristics into meaningful regions. By preserving essential structural information and reducing complexity, superpixels improve accuracy in cell-related measurements and facilitate the study of dynamic cellular processes. This paper presents a comparison of popular superpixel algorithms applied to cellular microscopy, highlighting their contributions to image analysis and interpretation in cell biology research. © 2023 IEEE.

关键词： image segmentation

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Preliminary Study on the Detection of Subtle Variations in image Sequences for Identifying False Starts in Speedway Racing 1

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19th International conference on Dependability of Computer systems (DepCoS-RELCOMEX)

作者： Krakowian, Jacek Jelen, Lukasz Fdn Support Innovat Res & Dev Grobli 12 Lok 0-211 PL-50421 Wroclaw Poland Wroclaw Univ Sci & Technol Dept Comp Engn Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (数字)9783031618574

ISBN: (纸本)9783031618567;9783031618574

Computer Vision algorithms have gained significant popularity and prove to be highly beneficial in detecting motion. These methods have proven to be profitable in motor sports especially. This paper presents a preliminary study on image processing, computer vision, and Long Short-Term Memory (LSTM) networks to detect subtle variations in image sequences for identifying false starts in speedway racing. Traditional methods often rely on manual observation, which lacks the precision to capture delicate movements at the start line. The presented methodology utilizes mentioned methods to enhance image quality, computer vision to detect and track racers' positions, and LSTM networks to analyze temporal sequences for movements indicating a false start. Results indicate that the adopted approach outperforms manual false start detection in accuracy and reliability. This advancement not only ensures fairness in speedway racing by providing a more objective detection of false starts but also demonstrates the potential of these technologies for applications requiring the identification of subtle changes in sequences. The efficiency and precision of our method offer promising implications for sports regulations and beyond, advocating for the broader application of technology in similar contexts.

关键词： motion detection speedway false start Gaussian mixture model frame difference Long Short-Term Memory LSTM

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PAIR : Perception Aided image Restoration for Natural Driving Conditions

PAIR : Perception Aided Image Restoration for Natural Drivin...

引用

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

作者： Shyam, Pranjay Yoo, HyunJin Faurecia IRYStec Inc Montreal PQ Canada

ISBN: (纸本)9798350318920;9798350318937

We present a two-stage mechanism for generic image restoration in natural driving conditions, where multiple non-linear degradations simultaneously impact perception for humans and driving assistance systems. Our approach overcomes the limitations of utilizing a single neural network that incurs excessive computational overhead and yields sub-optimal recovery. The proposed first stage comprises computationally inexpensive image processing operations applied at a patch level using a lightweight convolutional neural network (CNN) that determines their intensity of operation. This patch size is guided by the receptive field of the CNN, allowing for dynamic restoration of non-linear and non-homogeneous degradation profiles. The second stage leverages a lightweight end-to-end neural network functioning as an inpainting network. It identifies inadequately restored regions and leverages global semantic and structural information to fill the affected areas. This approach enhances the restoration process by considering the entire image and addresses the remainder of localized deficiencies. In addition, we integrate dense perception tasks such as semantic and depth estimation during the optimization cycle to ensure restored images that are perceptually pleasing and conducive for downstream perception tasks. Since datasets covering diverse degradation scenarios for high- and low-level perception tasks are lacking, we utilize a synthetic data augmentation technique to generate non-homogeneous non-linear degradation profiles. Experiments on images captured in adverse weather conditions demonstrate the efficacy of our approach, yielding higher perceptual quality in restored images and improved performance in downstream perception tasks under adverse driving conditions. Importantly, our method offers computational efficiency compared to end-to-end image restoration algorithms, making it suitable for real-time applications.

关键词： Applications Autonomous Driving

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A Multi-Modal image Understanding and Audio Description System for the Visually Impaired People 10

A Multi-Modal Image Understanding and Audio Description Syst...

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10th International conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Preetham, Monisha Krishnan, Meena Monta Vista High School CA United States Cisco United States

ISBN: (纸本)9798350384369

A significant challenge in the field of computer vision is to develop algorithms that are capable of producing captions for images. Ensuring image accessibility is crucial for individuals who have visual impairments. Audio descriptions provide detailed explanations of displayed events, such as bodily movements and expressive gestures in the images. Producing top-notch audio reports necessitates a substantial amount of personal effort in generating descriptions. To overcome this barrier to accessibility, this paper developed a novel multi-modal system that examines the graphical elements of an image and produces audio. The proposed technique extracts the features from the images by applying the transfer learning strategy. The captions undergo pre-processing to generate the tokens. The attention mechanism-encoder-decoder model receives as inputs the tokens and the image features that have been extracted. The model undergoes testing to create captions for the images, and its performance is assessed using the Bilingual Evaluation Understudy (BLEU) score. The average score on the test dataset is 73.864. The produced captions are transformed into audio format to cater to individuals with visual impairments. The real-time implementation of this technology can provide significant benefits to visually impaired individuals, enabling them to comprehend and analyze visual information in real time. © 2024 IEEE.

关键词： Multicarrier modulation

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Analysis of Ectopic Pregnancy in First Trimester Fetal Ultrasound images using Machine Learning Technique 2

Analysis of Ectopic Pregnancy in First Trimester Fetal Ultra...

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2nd International conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Maheswari, S. Bhuvaneshwaran, A. Rajalakshmi, J. Nivetha, S.B. Kishore, R. Deepanjali, S. Department of Biomedical Engineering Velalar College of Engineering and Technology Erode India

ISBN: (纸本)9798350340235

This study focuses on using medical imaging and machine learning to address Ectopic pregnancies (EPs) during the first trimester. The variability in ultrasound image quality and the presence of noise can hinder accurate image analysis in case of manual review by physicians, thus, this study aims to develop an automated system for fetal ultrasound image analysis. The research employs advanced image processing methods, including Region of Interest (ROI) selection and image enhancement techniques. Three classification algorithms CNN, SVM, and KNN are compared to select the most suitable one based on performance metrics. This suggests that CNN is a promising tool with 97% accuracy for the early detection of EP. The proposed system has the potential to transform the diagnosis and management of EP, potentially improving maternal outcomes. The goal is to enhance the precision and effectiveness diagnosis, potentially transforming early-stage pregnancy diagnostics. © 2023 IEEE.

关键词： Diagnostic accuracy Early diagnosis Ectopic pregnancy image enhancement image processing Machine learning Maternal-fetal health Medical imaging Ultrasound images

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