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

共 15618 条记录，以下是831-840 订阅

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A microfluidic system, utilising image processing methods, for the detection of blood coagulation and erythrocyte aggregation 5

A microfluidic system, utilising image processing methods, f...

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5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Louka, Marinos Passos, Andreas Inglezakis, Antonis Loizou, Constantinos Kaliviotis, Efstathios Cyprus Univ Technol Dept Mech Engn & Mat Sci & Engn Limassol Cyprus EMBIO Diagnost Nicosia Cyprus

ISBN: (纸本)9781665462198

Hemostasis is a defence mechanism that prevents blood losses in cases of vessel injuries, and other related disorders. In many cases, patients need to frequently monitor their blood coagulation tendency in order to regulate their medication. In addition, red blood cell aggregation (RBCA) is related to blood inflammation, and it appears elevated in many pathological conditions. Blood coagulation and RBCA can be studied by analysing the dynamic changes of light transmittance though a clotting/aggregating sample, and indeed various works in the literature exploit this approach. In this work, blood coagulation and RBCA are examined by utilising single drops of blood in an inexpensive camera-based microfluidic system, designed for low computational and production cost. Results are compared with a microscopy-camera system, with both setups utilizing the same custom made microchannel. Three image processing algorithms are developed to analyze the averaged light intensity, and the local structural chracteristics of blood, through a binarization and region classification method, using logical operations. The results illustrate the repeatability of the technique and the donorto-donor variation within the proposed approach. Based on the image processing analysis, the developed coagulation and aggregation indices show great potential of utilisation in an inexpensive and robust point of care device.

关键词： Blood coagulation red blood cell aggregation image processing medical diagnostics POC

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An Evaluation of Medical image Analysis using image Segmentation and Deep Learning Techniques for Brain-Tumour 2

An Evaluation of Medical Image Analysis using Image Segmenta...

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2nd International conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Sinha, Himanshu Kelley School of Business Indiana University Bloomington NapervilleIL United States

ISBN: (纸本)9798331541217

An aberrant mass of rapidly proliferating brain cells, known as a brain tumour, may develop into various forms of cancer. The segmentation of brain tumours by MRI scans is a challenging but crucial process with several medical analytic applications. Radiologists or clinical specialists are responsible for performing a tedious and time-consuming process that involves identifying, segmenting, and extracting contaminated tumour regions from MRI images. The different anatomical structures of human organs may be imagined via image-processing ideas. The human brain is notoriously difficult for basic imaging techniques to identify abnormal structures. A large number of researchers looked at several rapid and accurate algorithms for brain tumour (BT) detection and classification. Recent advances in DL-based automated systems have allowed for faster and more accurate segmentation of DT. This research makes use of a DenseNet50 model for medical picture classification of brain malignancies that is built on a pre-trained CNN model made possible by DL. The model is available on Kaggle. An optimiser like Adam and a categorical cross-entropy loss function were utilised to train a model. A suggested DenseNet50 model outperformed other comparative advanced DL models and attained an experimental accuracy of 95%. The visual results also highlight the impressive outcomes of the MRI brain tumour segmentation system, which will help doctors automatically detect and treat brain tumours. The results acquired demonstrate the effective identification of tumorous images, which is extremely beneficial for the diagnosis process and future applications. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Research on Carrier Phase Recovery in Coherent Optical Communication systems 24

Research on Carrier Phase Recovery in Coherent Optical Commu...

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2024 International conference on image processing, Intelligent Control and Computer Engineering, IPICE 2024

作者： Liu, Hao Zheng, Jiangpeng He, Qiuting Key&Core Technology Innovation Institute of the Greater Bay Area China

ISBN: (纸本)9798400710285

Coherent optical communication system (COCS) can achieve high-speed and long-distance transmission, but their structure is complex and costly. In coherent optical communication, problems such as link damage caused by the transmission medium and noise in transmission affect the transmission of data information. At the same time, the recovery of original data is also the key to optical communication. In response to these problems, this paper studies the carrier phase recovery in COCS. The article analyzes the advantages and disadvantages of existing phase recovery algorithms (PRA) and proposes a PRA based on the minimum first-order distance (MFOD). The algorithm first uses a blind phase search algorithm to find a rough estimate interval of the phase noise, and then uses MFOD to quickly find the phase noise and reduce the complexity of the algorithm. The simulation results show that the line width tolerance of the PRA based on the MFOD and the blind phase search algorithm are basically the same, but the complexity is reduced by 68% compared with the blind phase search algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Optical fiber communication

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An Adaptive BSCO Algorithm of Solid Color Optimization for 3D Reconstruction System with PIFuHD 17th

An Adaptive BSCO Algorithm of Solid Color Optimization for 3...

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17th International conference on Wireless algorithms, systems, and Applications (WASA)

作者： Hsieh, Chao-Hsien Song, Yubo Wang, Zhen Li, Changfeng Qufu Normal Univ Jining 273165 Shandong Peoples R China

ISBN: (纸本)9783031192135;9783031192142

PIFuHD can generate high-resolution model in the process of human 3D reconstruction. However, PIFuHD will produce debris outside the human body when the image background is more complex. In order to solve this problem, this paper develops an adaptive BSCO algorithm for the background of human body and image of human body in 3D reconstruction system. The BSCO algorithm is divided into four steps in processing. First, BSCO algorithm uses Go-selfies to separate the background. Second, BSCO algorithm converges the RGB of all pixels of the character into a set. Third, BSCO algorithm finds the greatest difference from the set through HSV conversion. Fourth, BSCO algorithm weighs the set and then calculates the RGB score. The highest score of RGB is used as the RGB of the background after solid color optimization. The experimental results show that the proposed method improves the reconstruction effect of PIFuHD.

关键词： PIFuHD 3D reconstruction image processing Background adaptive

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Color Terms and Stable Diffusion 32

Color Terms and Stable Diffusion

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32st Color and Imaging conference - Color Science and Engineering systems, Technologies, and Applications, CIC 2024

作者： Moroney, Nathan Numantic Solutions United States

ISBN: (纸本)9780892083688

Stable diffusion is a generative algorithm for creating images from text prompts. This paper explores prompts with color terms and proposes a process to generate, visualize and assess these results. Automated prompts are used to generate and render a color term, an object and a context. The results are then evaluated using two dashboard views of the underlying images. First is a sampling based on a collection of frequently used color terms. Second is a sampling by object prompts, such as apples and boxes. This paper considers the following questions: how effectively are the colored objects generated? how do the colors generated by stable diffusion compare to human color naming? How might color terms be useful in visualizing properties and features of generative algorithms? The dashboard view of color terms suggests that less frequently used color terms may be generated less consistently. In addition, even the most common color terms can fail to be correctly generated. Likewise, objects with more frequent color associations, such as apples or pumpkins, will result in less accurate color generation. ©2024 Society for Imaging Science and Technology.

关键词： Color image processing

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Wasserstein distributional robustness of neural networks 37

Wasserstein distributional robustness of neural networks

引用

37th conference on Neural Information processing systems, NeurIPS 2023

作者： Bai, Xingjian Jiang, Yifan He, Guangyi Oblój, Jan Department of Computer Science University of Oxford United Kingdom Mathematical Institute University of Oxford United Kingdom

ISBN: (纸本)9781713899921

Deep neural networks are known to be vulnerable to adversarial attacks (AA).For an image recognition task, this means that a small perturbation of the original can result in the image being *** of such attacks as well as methods of adversarial training against them are subject of intense *** re-cast the problem using techniques of Wasserstein distributionally robust optimization (DRO) and obtain novel contributions leveraging recent insights from DRO sensitivity *** consider a set of distributional threat *** the traditional pointwise attacks, which assume a uniform bound on perturbation of each input data point, distributional threat models allow attackers to perturb inputs in a nonuniform *** link these more general attacks with questions of out-of-sample performance and Knightian *** evaluate the distributional robustness of neural networks, we propose a first-order AA algorithm and its multistep *** attack algorithms include Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) as special ***, we provide a new asymptotic estimate of the adversarial accuracy against distributional threat *** bound is fast to compute and first-order accurate, offering new insights even for the pointwise *** also naturally yields out-of-sample performance *** conduct numerical experiments on CIFAR-10, CIFAR-100, imageNet datasets using DNNs on RobustBench to illustrate our theoretical *** code is available at https://***/JanObloj/W-DRO-Adversarial-Methods. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： image recognition

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A generic model of semiconductor-based photon-counting detectors for spectral CT

A generic model of semiconductor-based photon-counting detec...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Bhattarai, Mridul Panta, Raj Kumar Clark, Darin Segars, W. Paul Abadi, Ehsan Samei, Ehsan Duke Univ Med Phys Grad Program Durham NC 27708 USA Duke Univ Sch Med Dept Radiol Ctr Virtual Imaging Trials CVIT Durham NC 27708 USA Duke Univ Sch Med Dept Radiol Quantitat Imaging & Anal Lab Durham NC 27708 USA

ISBN: (纸本)9781510671553;9781510671546

Photon-counting CT (PCCT) is an emerging x-ray spectral imaging technology. There are several PCCT prototypes available for clinical and experimental applications. Despite many published results, objective quantitative evaluation of PCCT systems remains challenging due to varied and unknown influences. In addition, CT acquisition geometry and image processing mechanisms are often proprietary, complicating the correlations between the detector's physical characteristics and the corresponding CT image quality, hindering fair comparisons between different technologies. This study aimed to provide a resource to address these challenges by developing a versatile model that accurately replicates the physics of any semiconductor-based photon-counting detector (PCD) and integrating it into a virtual imaging framework to generate detector-specific CT images. The methodology involved a Monte Carlo simulation to model x-ray photon interactions with PCDs and an analytical Gaussian charge sharing model to model charge-diffusion and -repulsion in the detector. Finally, the energy deposited post-crosstalk by source photons (1-120 keV) in the 3x3-pixel-neighborhood was translated into photon counts across two energy thresholds to compute detector-specific spatio-energetic covariance correlation matrices. This framework was validated against experimental measurements of a clinical CdTe-based PCCT, showcasing its accuracy in reproducing real-world scenarios. Additionally, to demonstrate the model's efficacy, we simulated key PCD materials (Si, CdTe, CZT) and designed standardized scanner components, developing virtual systems (DukeCounter scanners). These systems were used to "scan" a physics phantom (ACR) and anthropomorphic-computational models (XCAT). In low-energy (i.e., 5 keV for Si, 20 keV for CdTe and CZT) threshold images, for 125 mAs acquisitions, (noise magnitudes, HU for bone insert) measured in ACR phantom were (76.0 HU, 889.4 HU), (76.9 HU, 932.9 HU), (81.7 HU, 939.4 HU)

关键词： Sensors Silicon Computed tomography image quality Monte Carlo methods X-ray computed tomography Scanners systems modeling Reconstruction algorithms Semiconductors Cadmium telluride Photon counting

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Novel Civil Engineering Construction Site Safety Monitoring and Warning System based on Machine vision

Novel Civil Engineering Construction Site Safety Monitoring ...

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Electronics and Renewable systems (ICEARS), International conference on

作者： Qiankun Zhang Qibin Cheng Zhejiang Construction Investment Innovation Technology Co. Ltd. Hangzhou China Zhejiang Construction Engineering Group Co. Ltd. Hangzhou China

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Machine vision is widely used in the engineering field, especially in the intelligent monitoring, safety warning and information recognition scenarios. In this study, the novel civil engineering construction site safety monitoring and warning system based on machine vision is proposed. As a pioneering research, this system realizes all-weather real-time security monitoring by combining the daytime and nighttime image processing technology. In daytime image processing scenarios, this study proposes a new image de-hazing algorithm. This algorithm models the imaging process by improving the atmospheric scattering model. At the same time, the guided filtering algorithm is used for optimization, which significantly improves the image clarity in low-visibility environments. In the night image processing scenario, this study uses infrared imaging technology and filtering algorithms for image enhancement. This operation can effectively solve the challenges of image noise and detail loss in low-light environments. As the core of the model, this study proposes a safety warning framework based on image anomaly detection to meet the special needs of construction sites. This innovative architecture includes human posture rotation detection and image color deviation anomaly recognition. Through such operations, automatic recognition and warning of dangerous behaviors are achieved. Experimental results prove the effectiveness of the recognition algorithm.

关键词： Renewable energy sources Machine vision Atmospheric modeling Scattering Alarm systems Filtering algorithms Real-time systems Monitoring Civil engineering Optimization

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Application of computer vision methods for automated wooden planks length measurement 18

Application of computer vision methods for automated wooden ...

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18th Baltic Electronics conference

作者： Zabulis, Lukas Lipnickas, Arunas Augustauskas, Rytis Kaunas Univ Technol Dept Automat Kaunas Lithuania

ISBN: (数字)9781665462952

ISBN: (纸本)9781665462952

Automated inspection is a principal component in modern manufacturing aiming for Industry 4.0. With the fast development of digital image processing and machine learning algorithms, quality control is often performed by computer vision systems (CV). Following the presented topic, this paper applies the CV system for non-direct length measuring of objects moving through a conveyor. The approach involves image segmentation by a convolutional neural network and segmented object processing principles. Since high-resolution images were used, segmentation was performed in a patch manner. The algorithm also involves a custom contour analysis and the application of the least-squares method for the midline of wooden plank extraction. The proposed approach is robust to the object's non-alignment and merging factors as well as shape and wooden texture variance. The presented algorithm was tested on a custom wooden planks database gathered in a real EURO pallets production environment and confirmed to work as expected ensuring +/- 1mm length measuring accuracy and 0.97s processing timing meeting the real-time requirement of the specified system.

关键词： object segmentation deep learning computer vision digital image processing non-direct measurement

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VGG-AM: Towards a New Hybrid Medical Imaging Analysis Based on VGG Classification Model and Deep DATA Preparation 1

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International conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Millimono, Sory Bellarbi, Larbi Maghzaz, Sarah Alidrissi, Najib Hamdi, Salsabil Kharmoum, Nassim Benahou, Ahmed Jidane, Said Belyamani, Lahcen Ghazal, Hassan Ezziyani, Mostafa Rhalem, Wajih E2SN Research Team ENSAM Mohammed V University in Rabat Rabat Morocco Moroccan Society of Digital Health Rabat Rabat Morocco Department of Surgery School of Medicine Mohammed VI University of Health Sciences Casablanca Morocco Environmental Health Laboratory Department of Research Pasteur Institute Casablanca Morocco National Center for Scientific and Technical Research Rabat Rabat Morocco Department of Mathematics and ApplicationsFaculty of Sciences Kenitra Ibn Tofail University Kenitra Morocco Emergency Department Mohammed the Vth Military Training Hospital Mohammed V University in Rabat Rabat Morocco Computer Sciences Department Faculty of Sciences and Techniques of Tangier University of Abdelmalek Essaadi Tangier Morocco Mohammed VI Center for Research and Innovation Rabat Morocco

ISBN: (数字)9783031523885

ISBN: (纸本)9783031523878

During the COviD-19 pandemic, the conventional RT-PCR method for diagnosing SARS-CoV-2 was limited by its processing time and accuracy rate. Consequently, thoracic CT images have been explored as a reliable and accurate alternative solution. However, analyzing a large volume of CT images by radiologists remains a major challenge. Deep learning solutions, specifically convolutional neural networks (CNN), have proven useful in radiology for image classification, object detection, semantic segmentation, and instance segmentation. In this study, DL approaches were evaluated for the automatic detection of COviD-19 on thoracic CT images using advanced deep networks such as XCEPTION, VGG, RESNET, and INCEPTION. A new advanced deep architecture called "VGG-advanced-Model" was also proposed, which improved upon the original VGG architecture that had achieved the best results. Various data preparation and preprocessing techniques were combined to enhance performance. The results demonstrate the effectiveness and efficiency of our method, achieving 100% precision, 99.52% accuracy, and 99% recall. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Coronavirus

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