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

共 12948 条记录，以下是271-280 订阅

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Review of Artificial Intelligence methods for detecting cancer in medical image processing 3

Review of Artificial Intelligence methods for detecting canc...

引用

3rd International conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sarkar, Priya Saha, Ashim National Institute of Technology Agaratala Computer Science and Engineering Agartala India

ISBN: (纸本)9781665462167

Cancer is the unchecked spread of aberrant cells throughout the body. Cancer is a general word for a set of diseases brought on by the growth of abnormal cells in various bodily parts. Lung cancer, breast cancer, skin cancer, oral cancer, colon cancer, and prostate cancer are just a few of the more than a hundred different forms of cancer. Delays in treatment might result in major health problems and even death. This research reviews strategies for detecting liver, brain, and lung cancer using image processing. Automated and computer-aided detection systems (CAD) with artificial intelligence are the approaches utilized for detection, and these systems are good at processing big datasets to produce accurate and effective results in the detection of cancer. To be compatible with AI, these processing systems must overcome numerous obstacles, including picture capture, pre-processing, data management, segmentation, and classification algorithms. The numerous picture acquisition and segmentation approaches are reviewed in this work. These methods are now essential to meet the needs of the expanding patient population and to improve the healthcare system. © 2023 IEEE.

关键词： Diseases

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Learning-based image Analytics in User-AI Agent Interactions for Cyber-enabled Manufacturing 4

Learning-based Image Analytics in User-AI Agent Interactions...

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4th IEEE International conference on Human-Machine systems, ICHMS 2024

作者： Alarcon, Mauro Lemus Roy, Upasana Kambhampati, Anirudh Nguyen, Nguyen Attari, Minasadat Surya, Ramakrishna Bunyak, Filiz Maschmann, Matthew Palaniappan, Kannappan Calyam, Prasad University Of Missouri Columbia United States

ISBN: (纸本)9798350315790

Manual trial-and-error methods are employed for image parameter selection decisions in the processes that control cyber-enabled scientific instruments. Particularly in materials manufacturing use cases, where image analytics can be iterative, time-consuming and prone to errors, there is a need to enhance existing processes by using agents featuring learning algorithms that recommend image analytics parameters for users. In this paper, we present an analysis focused on identifying the optimal learning algorithm for an AI agent guiding real-time image processing tasks in carbon nanotube (CNT) manufacturing, which involves enhancing instrument control of cyber-enabled scanning electron microscope (SEM) instrument image scanning setup parameters such as Zoom, Focus, and Contrast. Specifically, we demonstrate the use of Reinforcement Learning (RL) and Imitation Learning (IL) based agents within a Remote Instrumentation Science Environment (RISE), a modularized system capable of utilizing multiple learning algorithms for guiding image analytics tasks. Further, we conduct a comparative performance analysis of RL and IL using 236CNT images captured in SEM material synthesis experiments. The experiments include configuring scanning parameters for CNT images generated by SEM, implementing CNT image segmentation, and assessing the effectiveness of RL and IL agents in identifying scanning parameters to enhance image quality. The objective is to predict Zoom, Focus, and Contrast parameters using limited labeled data for offline training, guiding dynamic adjustments in SEM settings. Our findings reveal that the IL agent outperforms the RL agent under dynamic conditions in characterizing CNT image parameters - specifically, Zoom, Focus, and Contrast - by evaluating image segmentation metrics. © 2024 IEEE.

关键词： Iterative methods

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Color Medical image Encryption Based on Chaotic System and DNA 2nd

Color Medical Image Encryption Based on Chaotic System and D...

引用

3rd International conference on Digital Technologies and Applications

作者： Maloufy, Ahmed E. L. Karmouni, Hicham Tahiri, Mohamed Amine Qjidaa, Hassan Sayyouri, Mhamed Jamil, Mohamed Ouazzani Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes Morocco Cadi Ayyad Univ Natl Sch Appl Marrakech Morocco Sidi Mohamed Ben Abdellah Fez Univ CED ST STIC Lab Elect Signals & Syst Informat LESSIDhar El M Fes Morocco Univ Privee Fes UPF Lab Syst & Environm Durable SED Fac Sci Ingn FSI Fes Morocco

ISBN: (纸本)9783031298561;9783031298578

The discipline of information security known as deoxyribonucleic acid (DNA) cryptography is one of the newest and most promising areas. In this context, we suggest a new color medical imaging algorithm that combines the biogenetic principle of DNA with a four-dimensional, extremely chaotic system. This program encrypts the image blocks using the hyper-chaotic system's chaotic output, then determines the encoding, decoding, and DNA calculation of each image block to increase key space and resist image processing attacks. Simulation results exhibit the strength and efficiency of the suggested algorithm against different types of image processing attacks compared to other encryption algorithms.

关键词： Color image Encryption Decryption Chaotic systems DNA

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Multiple Transform Selection in Versatile Video Coding: a Review 8

Multiple Transform Selection in Versatile Video Coding: a Re...

引用

8th International conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Chowdary, T. Anil Nalluri, Purnachand VIT-AP University School of Electronics Engineering Inavolu Andhra Pradesh 522237 India

ISBN: (纸本)9781665408165

Tools for Transform Coding in coding of video relied on DCT-II traditionally for mapping residuals of image/video signals. Residual mapping can be done to a domain where quantizing and encoding tools give better efficiency in coding than domain of spatial. Nonetheless, DCT-II cannot model available types of residual image/video signals effectively. DCT-II modeling all types of residuals is not possible because of non-stationary behaviour of contents of video. So, to improve performance in compression of 4 X 4 blocks, DST-vii was instigated into VVC. In this paper This Paper presents survey of different algorithms for selection of optimal transform for each block (4X4, 8X8, 16X16, 32X32 and 64X64) of residual image/video for VVC. © 2022 IEEE.

关键词： Video signal processing

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Automated Anemia Detection Through Fingernail image Analysis 4

Automated Anemia Detection Through Fingernail Image Analysis

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4th International conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Geetha, A. Stanley, Mini Anuradha, S. Maya, P.R. Nalini, M. Sriharipriyan, P. Sri Sairam Engineering College Department of English Chennai India Sri Sairam Engineering College Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798331508845

Millions of people worldwide, especially in low- and middle-income nations, suffer from anemia, a common health problem. The disease is characterized by a lack of hemoglobin or red blood cells, which can result in symptoms including weakness and exhaustion as well as serious consequences if ignored. Conventional techniques for identifying anemia usually entail intrusive blood tests, which can be expensive, time-consuming, and sometimes unavailable to a large number of people. In order to overcome these obstacles, this study uses MATLAB to provide an automated, non-invasive method for detecting anemia via fingernail image analysis. This project's main goal is to develop an effective and precise anemia screening tool by utilizing machine learning and image processing methods. The fingernail photos are taken by the system and pre-processed to improve quality and guarantee consistency. The technology uses sophisticated image processing techniques to extract pertinent elements from the fingernail photos, including texture and color characteristics that indicate hemoglobin levels. Machine learning algorithms use these traits to categorize the photos as either anemic or non-anemic. MATLAB is used because of its strong skills in machine learning and image processing. From picture capture and pre-processing to feature extraction and classification, the program streamlines the whole procedure. MATLAB's extensive toolkit makes it possible to manipulate photos effectively and has strong machine learning capabilities, which makes it the perfect platform for creating an automatic detection system. The system is guaranteed to be scalable, simple to install, and accurate through the use of MATLAB. Clinical data is used to thoroughly validate the system's functionality. The machine learning models are trained and tested using images from both non-anemic individuals and patients with clinically confirmed anemia. The system's sensitivity, specificity, and accuracy are assessed to guar

关键词： Noninvasive medical procedures

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Intelligent processing algorithms for Motion Capture Data Oriented to Artificial Intelligence 2

Intelligent Processing Algorithms for Motion Capture Data Or...

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2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Lu, Zhenglei Beijing Information Technology College General Ability Teaching Department Physical Education Teaching Research Office Beijing China

ISBN: (纸本)9798350317558

Traditional motion capture systems are prone to environmental interference, resulting in noise and errors in the captured data. This article proposes an artificial intelligence oriented intelligent processing algorithm for motion capture data, which improves the quality of motion capture data and reduces noise and errors. This article utilizes sensors to collect a large amount of motion timing data and motion image data, preprocesses the data, and constructs a Convolutional Neural Network (CNN) Long Short Term Memory (LSTM) model. By using the CNN model to extract features from images, this paper uses the LSTM model to analyze temporal data, fuses the output of LSTM and CNN, and finally estimates the motion attitude. The data results of the test set indicate that the average mean square error of motion attitude estimation using the CNN-LSTM model is only 0.03. The use of the CNN-LSTM model can effectively improve the performance of motion data pose analysis. © 2024 IEEE.

关键词： attitude estimation convolutional neural network intelligent processing long short term memory motion capture data

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IRT and ANN Based System Identification Schemes for Model Based Fault Detection of Nonlinear systems

IRT and ANN Based System Identification Schemes for Model Ba...

引用

IEEE International conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

作者： Alwagait, Mohammed Khan, Mohammad Obaidullah Imam Mohammad Ibn Saud Islamic Univ Coll Engn Elect Engn Dept Riyadh Saudi Arabia

ISBN: (纸本)9798350344004

Faults in electrical power transmission systems can cause system failures and even may cause explosions. It is desired to remove a faulty component immediately to prevent further damage to the system and the environment. To address this, a typical technique is devised based on thermal imaging and various artificial neural network algorithms. The faulty element or component will radiate or emit higher energy when compared to normal or healthy conditions, because of the higher current flow rates. The thermal image taken from such defective part of the power system will be more highlighted in the image in contrast with the normal cool background. This drastic change in the grey level values in contrast with the healthy power system's picture hints or predicts a fault in that region. To support this methodology, an exhaustive simulation is implemented and demonstrated using thermal image processing and self-learning neural network algorithms and the simulation results are compared. This analysis is performed through various types of ANN techniques, and comparisons are established between them to report the network with best prediction results on a typical 'Step' dataset and 'Realistic' dataset. A similar low score mean square error MSE is exhibited with these models, and the R-square values are closer to the best score of one in all algorithms discussed. A better graph is obtained in Levenberg-Marquadrdt training Algorithm where most of the predictions fall alongside the target;however, Bayesian regularization gives a better plot than LM with the best fit is being obtained at lesser number of iterations that is in less time.

关键词： AI Artificial Neural Network Training algorithms image Extraction IRT Power System Faults Thermal Imaging

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Latest Trends on Satellite image Segmentation 5th

Latest Trends on Satellite Image Segmentation

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5th International conference on Communication and Intelligent systems, ICCIS 2023

作者： Borkar, Sahil Chidrawar, Krishna Naik, Sakshi Turuk, Mousami P. Vaijapurkar, Vaibhav B. SCTRs Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819720781

For the last few decades satellite imaging technology has taken massive strides towards higher spatial resolution, larger swath coverage and almost real-time data delivery. Satellite imaging or remote sensing is extensively used in optical imaging of the globe, military surveillance, deforestation detection, etc. Multispectral imaging by satellite has enabled an exceptional understanding of the earth by imaging beyond the realm of the visible spectrum. Against this backdrop image segmentation using machine learning, deep learning models and image processing has prompted several new approaches and techniques for satellite image segmentation. This survey provides a comprehensive review of the recent literature, covering the novel approaches in image segmentation using satellite imaging as well as others. There is a broad coverage of segmentation algorithms of UNET, TSVM and Random Walker. It investigates the strengths, challenges and novel aspects and compares precisions and deliberate potential research outlooks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration

Do We Still Need Non-Maximum Suppression? Accurate Confidenc...

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

作者： Gilg, Johannes Teepe, Torben Herzog, Fabian Wolters, Philipp Rigoll, Gerhard Tech Univ Munich Munich Germany

ISBN: (纸本)9798350318920;9798350318937

Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessibility and can sometimes produce unreliable predictions. Especially concerning in this regard are the-essentially hand-crafted-non-maximum suppression algorithms that lead to an obfuscated prediction process and biased confidence estimates. We show that we can eliminate classic NMS-style post-processing by using IoU-aware calibration. IoU-aware calibration is a conditional Beta calibration;this makes it parallelizable with no hyperparameters. Instead of arbitrary cutoffs or discounts, it implicitly accounts for the likelihood of each detection being a duplicate and adjusts the confidence score accordingly, resulting in empirically based precision estimates for each detection. Our extensive experiments on diverse detection architectures show that the proposed IoU-aware calibration can successfully model duplicate detections and improve calibration. Compared to the standard sequential NMS and calibration approach, our joint modeling can deliver performance gains over the best NMS-based alternative while producing consistently better-calibrated confidence predictions with less complexity. The code for all our experiments is publicly available(1).

关键词： accountable algorithms algorithms ethical computer vision Explainable fair image recognition and understanding privacy-preserving

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Deep Learning-based Early Lung Cancer Detection using Multi-Model image Fusion Technique 2

Deep Learning-based Early Lung Cancer Detection using Multi-...

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

作者： Wahengbam, Monita Sriram, M. Singh, Tonjam Gunendra Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Lung cancer, a significant global health concern, continues to take many lives, especially as a result of late-stage diagnoses. Early detection continues to have the most significant impact on patient outcomes and mortality rates. This research examines how modern image-processing methods might transform the early diagnosis of lung cancer. To investigate the application of deep learning algorithms, computer-aided diagnosis systems, and the synergistic integration of multimodal imaging techniques to increase the accuracy and efficiency of lung cancer diagnosis. The article thoroughly examines current approaches, presents a novel framework designed for early detection, and rigorously assesses the efficacy of the suggested system using actual medical imaging data. The research has the potential to radically alter lung cancer diagnosis, encourage a new era in early intervention, and eventually help to significantly lower lung cancer mortality rates by bridging the gap between cutting-edge image processing and clinical practice. © 2023 IEEE.

关键词： Computer Aided Diagnosis Deep Learning image processing Lung Cancer Multimodal Imaging Fusion

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