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

共 16347 条记录，以下是531-540 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...

引用

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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A Cloud-Edge Integrated Water Extraction Using Superpixel Segmentation 18th

A Cloud-Edge Integrated Water Extraction Using Superpixel Se...

引用

18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zhang, Qingmin Chen, Chen Zhou, Yang Wang, Zhiyi Jin, Fan Lu, Haitao Wan, Shaohua School of Communication Engineering Xidian University Xi’an China Xidian Hangzhou Institute of Technology Hangzhou311231 China The State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China Ningbo Joynext Technology Corporation Ningbo361000 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

ISBN: (纸本)9783031714665

With the development of the Industrial Internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the Internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Study on the Iterative Estimation and Compensation Algorithm for IQ Imbalance in Coherent Optical Communication systems 20

Study on the Iterative Estimation and Compensation Algorithm...

引用

20th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Huang, Changjun He, Fengtao Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798350356328

Coherent optical communication technology is a key technology for building high-speed optical fibre communication networks, while the amplitude and phase imbalance between the in-phase and quadrature (IQ) signals at the receiving end will lead to the degradation of the received signal quality and affect the performance of the communication system. In this thesis, a narrowband zero-intermediate frequency (ZIF) receiver IQ imbalance estimation and compensation algorithm is proposed with the research objective of designing IQ imbalance estimation and compensation algorithms for the 16 quadrature amplitude modulation (16QAM) signal in coherent optical communication systems that can be implemented in hardware circuits. The algorithm is based on the Gram-Schmidt orthogonalization procedure (GSOP) for approximate estimation of the IQ imbalance parameters and improves the estimation accuracy of the IQ imbalance parameters by means of iterative optimization. The IQ imbalance signal is effectively compensated by the inverse matrix of the IQ mismatch parameter matrix. Based on the estimation error of the IQ imbalance parameters, the bit error rate (BER) of the signal transmission at different signal to noise ratio (SNR), and the simulation results of the 16QAM constellation diagram before and after compensation, it is concluded that the iterative optimized algorithm based on GSOP reduces the complexity of hardware implementation and improves the efficiency and accuracy of the IQ imbalance parameters estimation effectively. The findings of this study hold practical engineering significance for estimating and calibrating IQ imbalance in coherent optical communication systems using high-order modulated signals through digital signal processing techniques. © 2024 IEEE.

关键词： Bit error rate

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The Evaluation of Camouflage Based on image Edge Contour Similarity 7

The Evaluation of Camouflage Based on Image Edge Contour Sim...

引用

7th International conference on Communication, image and Signal processing, CCISP 2022

作者： Zhou, xian Zhu, Wenwen Liu, Fuyong Yang, Wei Chu, Miao Xi'An Technological University School of Art and Media Xi'an China Xi'An Technological University School of Mechatronic Engineering Xi'an China Northwest Institute of Mechanical and Electrical Engineering 3rd Research Department Xianyang China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

As the target moves in the background, the profile dislocation will be caused and the camouflage effect will be reduced following that. Aimed at the problem, the author in the research introduces a camouflage evaluation method that based on the similarity of the edge contour of the camouflage target. The research combines Sobel and Canny algorithm to extract the edge contour of the target, uses the Euclidean distance to calculate the similarity of target's edge contour to its background after camouflage, so as to judge the quality of the camouflage effect. Finally, the rationality of the method is verified by ranking and comparing six camouflage plans with different contours, which were calculated by edge contour similarity method and image discrimination method respectively. © 2022 IEEE.

关键词： Visualization Shape image edge detection Signal processing algorithms Euclidean distance Signal processing Feature extraction

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EFormer-VPR: Fusing Events and Frames with Transformer for Visual Place Recognition 27

EFormer-VPR: Fusing Events and Frames with Transformer for V...

引用

27th IEEE International conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Yangjing Shen, Yanqing Zhu, Ziyu Hai, Renwei Chen, Shitao Zheng, Nanning National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Shaanxi Xi'an710049 China

ISBN: (纸本)9798331505929

Visual place recognition (VPR) is a challenging task faced by mobile robots and autonomous driving systems. In scenarios with glare or high-speed motion, image blurring makes it difficult for traditional cameras to perform reliable and accurate place recognition. In contrast, event cameras can capture target motion information without blur in high-speed motion scenes, but lack texture information in low-speed motion scenes. To leverage the complementary characteristics of these two sensors and improve the performance and robustness of VPR algorithms, we propose EFormer- VPR, which fuses target motion events and frames with transformer. This method firstly preprocesses the events stream within an adaptive time window using a clustering method, then uses transformer-based networks to extract features from motion frames and events separately and fuse them through a scoring module. Finally, features are aggregated using a VLAD layer and the whole pipeline is supervised by a triplet ranking loss. To verify the effectiveness of the proposed algorithm, we compare it with other VPR methods on the event-based driving datasets (Brisbane-Event-VPR, NeuroGPR) with challenging scenarios. Experimental results show that on the Brisbane- Event- Vprand NeuroGPR datasets, our method achieves state-of-the-art performance. © 2024 IEEE.

关键词： Mobile robots

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A Novel Track Initiation Method based on Rule Knowledge and Deep Detection Network 16

A Novel Track Initiation Method based on Rule Knowledge and ...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Wang, Zengke Zhang, Weijie Pan, Meiyan Liu, Dai Xi'an Electronic Engineering Research Institute China

ISBN: (纸本)9781510689251

Radar target tracking plays an important role in reconnaissance, monitoring and defense. Clutter caused by complex electronic and natural environments poses many challenges to radar target tracking. The current track initiation method, which heavily relies on empirical knowledge, struggles with the increasing problem of false tracks and missed detections. To address this, a track initiation method based on rule knowledge and a deep detection network is proposed. In this method, radar echoes are converted into images that comprehensively reflect the different frames and target shapes, facilitating processing. With the help of the excellent detection, positioning and recognition ability of the deep learning network Faster-RCNN, the candidate boxes where might exist target track in the echo image are extracted candidate boxes where targets might exist in the echo image are extracted. Additionally, the corresponding angle and wave gate are set as initiation rules. The combination of radar data learning and partial experiential rules reduces the need for accurate prior parameters in traditional methods. Furthermore, by considering the spatial and temporal information of the target comprehensively, it achieves a good balance between the correct initiation rate and false initiation rate. Simulation environments under different observation trajectories are constructed, and Monte Carlo simulation is used to compare the proposed method with the traditional track initiation method and other track initiation methods combined with machine learning and prior rules. Results show that our method can ensure the correct initiation rate of the target track and effectively suppress the false initiation rate. © 2025 SPIE.

关键词： Deep learning

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FPGA-based Real-time Video Quality Enhancement for Unmanned Aerial Vehicle 20

FPGA-based Real-time Video Quality Enhancement for Unmanned ...

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20th IEEE International conference on Mechatronics and Automation, ICMA 2023

作者： Lu, Yue Li, Jinglei Yang, Qinghai Xidian University School of Telecom. Engineering Xi'an China

ISBN: (纸本)9798350320831

With the rapid improvement of UAV imaging equipment in terms of image resolution and video frame rate, relying solely on software to enhance images with extremely high data volume is no longer sufficient to meet real-time requirements. Therefore, a new method of processing image signals utilizing field programmable gate array (FPGA) hardware is proposed. This method involves analyzing traditional image enhancement algorithms to solve problems such as incorrect frame mapping, low contrast image enhancement, and incomplete comparison image enhancement mechanism resulting in noise during the engineering process. A limited contrast enhancement mechanism is also proposed to suppress the appearance of enhancement noise. Using the FPGA system, a hardware optimization design method is employed, including parallel processing, pipeline operation, and equivalent conversion, to achieve hardware image enhancement with extremely low resource consumption. This allows for the realization of video image enhancement with a resolution of 1920∗1080 and a frame rate of 100Hz. The system has improved the overall brightness of images, target contrast, and image information restoration, and has achieved good results. This algorithm can be widely used in engineering design. © 2023 IEEE.

关键词： image enhancement

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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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