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

共 16443 条记录，以下是421-430 订阅

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Predicting Liver Tumor: Leveraging image processing with DenseNet121 3

Predicting Liver Tumor: Leveraging Image Processing with Den...

引用

International conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Sandhiya, B. Selvan, Anbu Gowtham, R. N. Jain Deemed Be Univ Sch Comp Sci & Informat Technol Bengaluru India Easwari Engn Coll Dept Informat Technol Chennai Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Early detection and accurate prediction of liver disease play a crucial role in improving patient outcomes and reducing the burden on healthcare systems. Segmenting the liver and its tumors using computed tomography (CT) images is an essential undertaking for the diagnosis and treatment of liver illnesses. Because of the uneven distribution, hazy boundaries, varied densities, forms, and sizes of lesions, segmenting the liver and associated tumor is a challenging task. Up until this point, our primary focus has been on developing deep learning algorithms that can separate the liver and its tumor from CT scan pictures of the abdomen, saving time and effort when diagnosing liver illnesses. A deep learning-based automatic segmentation method is presented that uses the improved densenet121 model to segment the liver and its tumor. In this model image processing is used for the accurate automated segmentation of tumors, the proposed method demonstrates the ability to accurately segment the liver as well, as indicated by the confusion matrix obtained when comparing to the previous work on liver and tumor segmentation. The Densenet121 architecture serves as the foundation for the algorithm employed here, we introduced an autonomous technique to segment the liver from CT scans and lesions from the segmented liver region, based on semantic segmentation convolutional neural networks ( CNNs). For liver and tumor segmentations, respectively, the suggested system achieves an accuracy of 95.31% to 95.39%.

关键词： Convolutional neural network Deep learning image processing Densenet121 Confusion matrix

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A Comparative Study of Generative Adversarial Networks for image Recognition algorithms Based on Deep Learning and Traditional Methods 6

A Comparative Study of Generative Adversarial Networks for I...

引用

6th IEEE International conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Zhong, Yihao Wei, Yijing Liang, Yingbin Liu, xiqing Ji, Rongwei Cang, Yiru New York University New York United States Northwestern University Evanston United States Northeastern University Seattle United States Columbia University New York United States Southern Methodist University Dallas United States Northeastern University Boston United States

ISBN: (纸本)9798350374315

This paper delves into an innovative image recognition algorithm that merges deep learning techniques with Generative Adversarial Networks (GANs) and offers a comparative analysis against traditional image recognition methods. The primary objective of this study is to evaluate the benefits and future prospects of deep learning, with a particular focus on GANs, within the realm of image recognition. The paper begins by reviewing conventional image recognition approaches, highlighting classical algorithms such as SIFT and HOG, which are based on feature extraction, and their combination with classifiers like Support Vector Machines (SVM) and Random Forests. Following this, the paper outlines the fundamental principles, network architecture, and unique advantages of GANs in the context of image generation and recognition. To verify the effectiveness of GANs in image recognition tasks, a series of experiments were performed using several publicly available image datasets for both training and testing purposes. The findings reveal that GANs significantly outperform traditional methods, especially in the context of processing complex images, enhancing accuracy, and improving resistance to noise. GANs are particularly adept at capturing intricate, high-dimensional features and subtle details in images, leading to substantial improvements in recognition performance. Additionally, GANs demonstrate exceptional capabilities in addressing issues related to image noise, managing incomplete data, and generating high-quality visual content. © 2024 IEEE.

关键词： Generative adversarial networks

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A N-Dimensional Discrete Chaotic Map and Its Performance

A N-Dimensional Discrete Chaotic Map and Its Performance

引用

Natural Language processing (ICNLP), International conference on

作者： xin Gao xiumei Cai Chengmao Wu School of Automation Xi’an University of Post and Telecommunications Xi’an China School of Electronic Engineering Xi’an University of Post and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to solve the problems of small key space and simple chaotic behavior of low-dimensional chaotic systems in discrete domain, an N-dimensional discrete chaotic mapping system is proposed. An N-dimensional discrete chaotic system is obtained by coupling Chebyshev mapping with ICMIC mapping. Taking two-dimensional chaotic mapping as an example, the Lyapunov index, bifurcation graph, correlation and other properties of the discrete chaotic system are analyzed and applied to more classical image encryption algorithms. Experimental simulation results show that the N-dimensional discrete chaotic mapping system has larger key space, better chaotic behavior, and better security performance for image encryption algorithms.

关键词： Performance evaluation Correlation Sensitivity Simulation Information security Bifurcation Natural language processing Encryption Trajectory Indexes

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GAN-based image deblurring: A comparison 2

GAN-based image deblurring: A comparison

引用

2nd IEEE International conference on Electrical Engineering, Big Data and algorithms, EEBDA 2023

作者： Feng, Qi Jiang, Liyan Ji, Yuyang Zhang, Ruihan University of Wisconsin-Madison Madison United States Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai China New York University New York United States Xi'An Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781665462532

In recent years, image deblurring algorithms based on Generative Adversarial Network (GAN) have been proposed. The motion deblurring has been studied for some time. The multi-scale architecture of convolutional neural networks (CNN) can clearly recover blurred images, but the processing time is long. Generative Adversarial Networks (GAN) performs well in style conversion and can clearly recover blurred images under a non-multi-scale architecture. The authors reviewed the popular papers related to DeblurGAN, compared the algorithm structure of different papers, related data sets and the accuracy of experimental results, which helped GAN based image deblur field to summarize and summarize, and enabled researchers to better promote the development of related fields. The authors review six papers, introduce their research motivation, research methods and experimental results, and show the differences of different deblur algorithms. Some excellent algorithms can work flexibly with a wide backbone, balancing performance and efficiency. They run faster than simple DeblurGAN while maintaining near-state of the art results. © 2023 IEEE.

关键词： Generative adversarial networks

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Augmented Reality System Based on Real-Time Object 6D Pose Estimation 2

Augmented Reality System Based on Real-Time Object 6D Pose E...

引用

2nd International conference on image processing and Media Computing, ICIPMC 2023

作者： Zhao, Yan Zhang, Shaobo Zhao, Wanqing Wei, Ying Peng, Jinye Northwest University School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350326611

Augmented Reality (AR) refers to the perfect integration of virtual information with the real environment through computer technology, enhancing people's understanding of the real environment. Due to its excellent interactivity and realistic visual effects, it has received extensive attention from various fields. The effectiveness of augmented reality largely depends on whether virtual information can be accurately rendered in the real scene, with the most critical aspect being the 6D pose estimation of the target object in the real scene. Deep learning-based pose estimation methods have become indispensable technology in AR systems due to their high accuracy and wide applicability. Among existing pose estimation algorithms, YOLO6D has the advantages of good stability and fast speed. However, for AR applications that require interaction with the human eye, due to its complex network structure and large computational complexity, it still faces challenges in real-time performance and deployment on mobile devices. Therefore, this paper aims to achieve a mobile AR system based on real-time pose estimation, using the YOLO-6D pose estimation network as the foundation, and conducting research from the aspects of model compression and system design. By using model compression algorithms include channel pruning and knowledge distillation (KD), the computational and parameter complexity of the pose estimation model is reduced, and the network inference speed is improved. At the same time, the AR tasks are decomposed in the system design to alleviate the performance pressure during deployment on mobile devices, thus building a high-quality, low-latency mobile AR system. © 2023 IEEE.

关键词： Augmented reality

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Multi-Exposure Fusion Light Field image Quality Assessment Based on Object Detection 14

Multi-Exposure Fusion Light Field Image Quality Assessment B...

引用

14th IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2024

作者： Yao, Keke Liao, Guanglong Jiang, Gangyi Chen, Yeyao Yu, Mei Ningbo University Faculty of Information Science and Engineering Ningbo China

ISBN: (纸本)9798350366556

The multi-exposure fusion algorithms can effectively improve the dynamic range of light field imaging, but distortion is inevitably introduced during the process. Therefore, it is crucial to construct an effective quality assessment method for multi-exposure fusion light field images (MEF-LFIs). Aiming at the significant artifact distortion phenomenon of MEF-LFIs and the limitations of the traditional dynamic region extraction methods, the MEF-LFI quality assessment method combined with object detection is proposed. Firstly, an object detection model is utilized to extract dynamic region masks of the original LFI sequences, and the artifact feature fusion module is constructed for artifact distortion. Subsequently, the MEF-LFIs are converted from the RGB color space to the HSV color space to measure color distortion. Then, the multi-stream feature extraction networks with attention mechanism are constructed to extract artifact, spatial and angular distortion features, respectively. Finally, a regression model is constructed to measure the visual quality of MEF-LFIs. Experimental results show that the proposed method outperforms many representative image quality assessment methods and maintains well consistency with the human visual perception. © 2024 IEEE.

关键词： image quality

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A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion 6

A Novel Approach to Malicious Code Detection Using CNN-BiLST...

引用

6th International conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Zhang, Lixia Liu, Tianxu Shen, Kaihui Chen, Cheng Xi’an XD Power Systems Co. Ltd. Xi'an China Hangzhou Research Institute Xidian University Hangzhou China Xidian Digital Technology Xi'an China Harbin Engineerning University Harbin China

ISBN: (纸本)9798331541699

With the rapid advancement of Internet technology, the threat of malware to computer systems and network security has intensified. Malware affects individual privacy and security and poses risks to critical infrastructures of enterprises and nations. The increasing quantity and complexity of malware, along with its concealment and diversity, challenge traditional detection techniques. Static detection methods struggle against variants and packed malware, while dynamic methods face high costs and risks that limit their application. Consequently, there is an urgent need for novel and efficient malware detection techniques to improve accuracy and robustness. This study first employs the minhash algorithm to convert binary files of malware into grayscale images, followed by the extraction of global and local texture features using GIST and LBP algorithms. Additionally, the study utilizes IDA Pro to decompile and extract opcode sequences, applying N-gram and tf-idf algorithms for feature vectorization. The fusion of these features enables the model to comprehensively capture the behavioral characteristics of malware. In terms of model construction, a CNN-BiLSTM fusion model is designed to simultaneously process image features and opcode sequences, enhancing classification performance. Experimental validation on multiple public datasets demonstrates that the proposed method significantly outperforms traditional detection techniques in terms of accuracy, recall, and F1 score, particularly in detecting variants and obfuscated malware with greater stability. The research presented in this paper offers new insights into the development of malware detection technologies, validating the effectiveness of feature and model fusion, and holds promising application prospects. © 2024 IEEE.

关键词： Critical infrastructures

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A review of intelligent vehicle motion planning algorithms 3

A review of intelligent vehicle motion planning algorithms

引用

3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Kong, Lingming Wang, Zhiyu xi, Ruixuan Shen, Liangyi Ji, Zhao Li, Kai Co. Ltd. Tianjin300300 China

ISBN: (纸本)9781510668522

With vehicles becoming a necessity for people's life and work, the vigorous development of intelligent vehicles is of great significance in today's society in order to reduce traffic accidents, lower operating costs and achieve environmentally friendly travel. Motion planning, as the functional core of intelligent driving systems, is crucial to the driving safety of intelligent vehicles. This paper introduces the development status of intelligent vehicles at home and abroad, studies the current research status of mainstream motion planning techniques, analyses the technical challenges encountered in motion planning for intelligent vehicles in urban roads, and illustrates the development goal of establishing efficient and reliable motion planning algorithms in complex scenarios. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Motion planning

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Automatic conversion system of digital music score image based on image recognition algorithm 3

Automatic conversion system of digital music score image bas...

引用

3rd International conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Yao, Yi Xi'an Conservatory of Music Shaanxi Xi'an710000 China

ISBN: (纸本)9781510661240

The development of target detection and recognition algorithms in the field of image processing has promoted the development of automatic image conversion systems for digital musical scores and related algorithms. This was crucial for the development of note-recognition coding and the conversion of musical scores into readable digital formats. The purpose of this paper is to study the automatic conversion system of digital music score image based on image recognition algorithm. The deep learning technology based on computer vision is used for musical score image recognition, and a digital musical score image automatic conversion system is designed and built. Harmonic peak method and confidence method are introduced, and their shortcomings are analyzed. Combined with the advantages of these two algorithms, an improved normal harmonic method is proposed, and the detailed ideas and steps of the algorithm are given. And the extracted fundamental frequency and time information are converted into pitch and time value combined with the basic knowledge of music theory. The recognition accuracy rate of the normal harmonic method proposed in this paper reaches more than 92%, which not only greatly improves the recognition accuracy, but also retains the strong anti-noise characteristics of the traditional harmonic peak method. © 2022 SPIE.

关键词： image recognition

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An adaptive fusion of RGB channel features U-Net for retinal vessel segmentation

An adaptive fusion of RGB channel features U-Net for retinal...

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2024 International conference on image processing and Artificial Intelligence, ICIPAl 2024

作者： xi, Mingyuan Gong, Zikang Shanghai Maritime University 1550 Haigang Ave. Pudong New Area Shanghai200135 China

ISBN: (纸本)9781510681514

Accurate segmentation of retinal vessels in fundus color photos holds significant importance in diagnosing ophthalmic conditions. This process plays a crucial role in identifying and analyzing various eye diseases. However, the retinal vascular structure is highly complex, with imbalances in multiscale and foreground-background proportions, making automatic segmentation challenging. Studies have indicated that valuable information is contained in the three RGB channels of color retinal vessel images. Nevertheless, there has been no research to date on integrating the information from the RGB channels with U-shaped segmentation networks for training. Therefore, in this paper, we propose an Attention U-Net with adaptive fusion of RGB three-channel features. The selective channel attention fusion module can adaptively integrate RGB three-channel features, and the fused features are input into the U-shaped network for training. In addition, an Attention Gate module is added before each upsampling concatenation operation in the decoder stage of the U-Net network. This effectively highlights specific target areas while suppressing irrelevant regions of the input image. Experimental results demonstrate that our method outperforms the segmentation results of other algorithms in three datasets in recent years. © 2024 SPIE.

关键词： Diagnosis

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