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

共 16444 条记录，以下是631-640 订阅

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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...

引用

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?

引用

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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Intelligent temperature and humidity detection device for watering flowers 3

Intelligent temperature and humidity detection device for wa...

引用

3rd International conference on Advanced Manufacturing Technology and Manufacturing systems, ICAMTMS 2024

作者： Wu, Yahang Sun, Kangning Chang'an University Xi'an710000 China State Grid Jiangsu Electric Power Company Zhenjiang Power Supply Branch Jiangsu212002 China

ISBN: (纸本)9781510681804

Through research in the field of plant maintenance device equipment maintenance and operation, particularly pertaining to intelligent water spray detection equipment, we have developed an intelligent water spray temperature and humidity detection device. This device addresses the limitation of traditional intelligent water spray equipment, which only monitors soil humidity and disregards the influence of environmental temperature. The device comprises a humidity detection module, a solar charging module, a temperature measurement module, an LCD display, and a motorized water pump. This setup allows for simultaneous monitoring of ambient temperature and humidity, with data transmission to the controller for processing. Utilizing data processing algorithms, the device can automatically water and adjust the watering amount and frequency. This functionality not only enhances the growth and survival rates of plant maintenance but also helps minimize water wastage. Furthermore, the device utilizes solar cells as a power source, making it more environmentally friendly and contributing to the sustainable development of the plant care industry. © 2024 SPIE.

关键词： Sustainable development

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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...

引用

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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Improved Self-Sparse Fuzzy Clustering Algorithm for image Segmentation

Improved Self-Sparse Fuzzy Clustering Algorithm for Image Se...

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2023 International conference on Communication, Circuits, and systems, IC3S 2023

作者： Nayak, Tunirani Bhoi, Nilamani Vssut Department of Electronics and Telecommunication Engg. Burla758018 India

ISBN: (纸本)9798350325904

The effectiveness of the FCM image segmentation algorithm can be increased by introducing local spatial information as FCM is very sensitive to noise. Also, during transmission, the image details are lost because noise is added. Thus, an Alpha-trimmed mean filter is utilized as a pre-processing step to maintain the image features and immunity. Then SSFCA algorithm is used to obtain a fuzzy membership with sparsity. It is used to reduce the non-homogeneous interference and got a good segmentation result in comparison to other self-optimized FCM algorithms. Again, to overcome the background noise the resultant image is applied to the morphological operators as a postprocessing step. Experiments show that this method can create good segmentations. © 2023 IEEE.

关键词： image segmentation

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Intelligent Test Paper Generation Algorithm Integrating Normal Distribution and Parallel Genetic Algorithm 5

Intelligent Test Paper Generation Algorithm Integrating Norm...

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5th International conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Liu, Chuangye Huang, Guimin School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350368284

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms. © 2024 IEEE.

关键词： Consensus algorithm

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