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

共 16440 条记录，以下是1731-1740 订阅

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SWT-UNet: Medical image Segmentation Based on Multi-modality UNet with Sliding Window Transformer Block

SWT-UNet: Medical Image Segmentation Based on Multi-modality...

引用

2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yangqianhui Han, Dong Gang, xiaoming Ma, Jiajun Yuan, Xu Zhejiang University School of Mechanical Engineering Hangzhou China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou China Anshan Municipal Central Hospital Anshan China Shenhua Hollysys Information Technology Co. Ltd Beijing China Dalian University of Technology School of Software Technology Dalian China

ISBN: (纸本)9798350337488

Medical images are information carriers that visually reflect and record the anatomical structure of the human body, and play an important role in clinical diagnosis, teaching and research, etc. Modern medicine has become increasingly inseparable from the intelligent processing of medical images. In recent years, there have been more and more attempts to apply deep learning theory to medical image segmentation tasks, and it is imperative to explore a simple and efficient deep learning algorithm for medical image segmentation. In this paper, we conduct research on multi-modal medical image segmentation algorithms with a hybrid architecture of Convolutional Neural Networks and Vision Transformer. This paper proposes a multi-modal medical image segmentation model SWT-UNet based on the CNN-ViT hybrid framework. The self-attention mechanism and sliding window design of the visual Transformer are used to capture the global feature association and break the limitation of the receptive field caused by the inductive bias of the convolution operation. At the same time, a widened self-attentive vector is used to streamline the number of modules and compress the model size, so as to fit the characteristics of a small amount of medical data, which makes the model easy to be overfitted. Experiments on two multi-modal medical image datasets validate that the algorithm can achieve efficient medical image segmentation at a lightweight scale. © 2023 IEEE.

关键词： attention mechanism medical image segmentation multi-modality Vision Transformer

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image steganography algorithm based on image colorization

Image steganography algorithm based on image colorization

引用

2021 International conference on Signal image processing and Communication, ICSIPC 2021

作者： Bi, xinliang Yao, Wujun Zhang, Zhuo Huang, Siyuan Liu, Jia Chen, Bowei Engineering University of PAP College of Cryptographic Engineering Xi'an710086 China Network and Information Security Key Laboratory of PAP Xi'an710086 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

Aiming at the problems of stego images with modification traces and unnatural image generation in the image steganography algorithm based on deep learning, this paper proposes an image steganography algorithm based on image coloring. It first embeds secret information in a gray image using traditional image steganography algorithms, and then uses image coloring algorithms to color the gray image, disguising the modification of the gray image as the result of image embedding, highlighting the coloring features of the algorithm, reducing the steganographic characteristics of the algorithm, and guide the enemy to mistake the algorithm for the image coloring algorithm, thereby reducing the probability of steganography being discovered. Experimental results show that this algorithm has good results in steganographic capacity and generated images. © 2021 SPIE.

关键词： Steganography

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An Improved Genetic Algorithm for Bi-objective Dynamic Orienteering Problem with Time Windows

An Improved Genetic Algorithm for Bi-objective Dynamic Orien...

引用

2024 8th International conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Qing Wang xiang Lin Yan Zhang College of Systems Engineering National University of Defense Technology Changsha Hunan Province China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

This paper proposes an Improved Genetic Algorithm combined with Path-Reconstruction operators (IGAPR) to solve the Dynamic Orienteering Problem with Time Windows (DOPTW). In this problem, task nodes appear dynamically, challenging the solution strategies of traditional orienteering problems. We divide the entire planning horizon into multiple static time periods and apply a heuristic genetic algorithm within each period to generate high-quality solutions. Experimental results on modified Solomon and Cordeau benchmark instances demonstrate that our approach outperforms traditional greedy algorithms and conventional genetic algorithms in terms of path rewards and fitness value. Additionally, the paths generated by our method adapt to dynamic changes and exhibit good scalability as the number of nodes increases.

关键词： Greedy algorithms Satellites Heuristic algorithms Scalability Imaging Dynamic scheduling Real-time systems Planning image reconstruction Genetic algorithms

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Background Augmentation with Transformer-Based Autoencoder for Hyperspectral Anomaly Detection 5th

Background Augmentation with Transformer-Based Autoencoder ...

引用

5th IFIP TC 12 International conference on Intelligence Science, ICIS 2022

作者： Wang, Jianing Liu, Yichen Li, Linhao Xidian University Xi’an China

ISBN: (纸本)9783031149023

Aiming at handling the problem caused by the lack of prior spectral knowledge of anomalous pixels for hyperspectral anomaly detection (HAD). In this paper, we propose a background augmentation with transformer-based autoencoder for hyperspectral remote sensing image anomaly detection. The representative background pixels are selected based on sparse representation for obtaining typical background pixels as training samples of the transformer-based autoencoder. The selected typical background pixels can be used for training the transformer-based autoencoder to realize background pixel reconstruction. Thereafter, the pseudo background samples can be reconstructed from the transformer-based autoencoder, which is used to subtract the original image to obtain the residual image. Finally, Reed-xiaoli (RX) is used to detect the anomalous pixels from residual image. Experiments results demonstrate that the proposed transformer-based autoencoder which can present competitive hyperspectral image anomaly detection results than other traditional algorithms. © 2022, IFIP International Federation for Information processing.

关键词： Anomaly detection

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Convolutional Neural Networks (CNN) for Industrial Parts Recognition: Advancing Manufacturing Digitalization 2nd

Convolutional Neural Networks (CNN) for Industrial Parts Rec...

引用

2nd International conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Nardviriyaku, Pasavich Hajjaj, Sami Salama Hussen School of Robotics Xi’an Jiaotong-Liverpool University 111 Taicang Avenue Jiangsu Province Suzhou Taicang215400 China School of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819639489

In the current era of manufacturing digitalization, computer vision is an emerging field of artificial intelligence (AI) that replicates human visual system functions by enabling computers and systems to identify and interpret visual information. Convolutional Neural Networks (CNNs) have demonstrated remarkable potential in image recognition and tasks related to pixel data processing. Industrial parts recognition and classification systems play a crucial role in automating manufacturing processes, streamlining workflow, and enhancing object recognition capabilities on production lines. This research presents a CNN-based approach to industrial parts recognition and classification, offering new possibilities for efficient and accurate prediction performance. The Inception-v3 network architecture was selected for its state-of-the-art design, serving as the pre-trained model to achieve optimal performance. The TensorFlow library provided a comprehensive and flexible framework, while dropouts were employed to prevent overfitting. The trained CNN model was evaluated using key metrics such as accuracy, precision, recall, F1-score, and model loss, with a confusion matrix adopted for binary classification to assist decision-making and summarize the model’s predictions. The study results demonstrated that the model achieved an impressive accuracy of 98.83% with a model loss of 0.023084, proving highly effective in identifying industrial parts, specifically distinguishing between BJ15–860 and BJ15–870 screws. This research underscores the capabilities of artificial intelligence, particularly in CNNs, and highlights the application of industrial parts recognition and classification, contributing significantly to the digitalization of manufacturing processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Automated Optic Disk Detection in Fundus images using a Combination of Deep Learning and Local Histogram Matching

Automated Optic Disk Detection in Fundus Images using a Comb...

引用

conference on Medical Imaging - Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Tavakoli, Meysam Univ Pittsburgh Med Ctr Dept Radiat Oncol Pittsburgh PA 15260 USA UPMC Hillman Canc Ctr Pittsburgh PA 15232 USA

ISBN: (数字)9781510649484

ISBN: (纸本)9781510649484;9781510649477

Retinal images have been used in the diagnosis of many ocular diseases such as glaucoma and diabetic retinopathy. Here, automatic detection of optic disk (OD) is essential in deriving clinical parameters to assist clinical diagnosis. In fact, detecting OD center and its boundary is the essential step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. In this study, we proposed a new approach for localizing OD by combining local histogram matching and the concept of deep learning. The algorithm is composed of 4 steps, image partitioning, Local histogram matching and validation, Convolutional Neural Network (CNN) classification, and OD detection. Here, we used OD of the five reference retinal images in each dataset to extract the histograms of each color channel. Then, we calculated the mean of histograms for each channel as template for creating some OD candidates. An AlexNet-like CNN was applied to classify candidates as ODs or nonODs. The candidates used as an input to feed the CNN for final classification. In this study, we worked on three databases (one rural, MUMS-DB, and two publicly available databases, DRIVE, STARE) including 520 retinal images to evaluate the proposed method. The accuracy of our algorithm was 100%, 90%, and 95% for the DRIVE, STARE, and MUMS-DB respectively. It is shown that this method provides higher detection rates than the existing methods that have reported.

关键词： image segmentation image processing image processing algorithms and systems Optical discs image resolution MATLAB Radon transform

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Survey of Approaches to Non-Realistic Neural Style Transfer 31

Survey of Approaches to Non-Realistic Neural Style Transfer

引用

31st conference of Open Innovations Association FRUCT, FRUCT 2022

作者： Varlamova, Arina Kitov, Victor Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9789526924472

Despite researchers interest toward style transfer problem, there is still no foremost method available. Difficulties in problem formalization make a comparison of methods especially complicated. This paper covers twelve widely used algorithms in order to provide their comprehensive description and advantages one over another. © 2022 FRUCT Oy.

关键词： Technological innovation image processing Real-time systems Convolutional neural networks

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Exploration of Motion Increase Rate Based on Infrared Feature Capture and CFD Numerical Simulation

Exploration of Motion Increase Rate Based on Infrared Featur...

引用

Electronics, Energy systems and Power Engineering (EESPE), IEEE International conference on

作者： Minghan Wang Beijing University of Technology Beijing China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The purpose of this study is to explore the motion incremental speed technology based on infrared feature capture and CFD numerical simulation, through the sensor, image processing and analysis algorithms, etc. On the basis of exploring the target's motion characteristics, combined with the Chancellor's theory of ballistic trajectory, and real-time capture of the target's thermal feature information is to achieve accurate target tracking and motion trajectory adjustment. Firstly, this paper adopts the image processing and analysis algorithm, and uses the normal distribution model to process the infrared data, so as to construct the target motion model. Secondly, CFD numerical simulation is used to analyse mechanisms such as motion boost guidance in order to provide more accurate target tracking and trajectory correction schemes. Finally, the motion region of the updraft is predicted by spherical averaging of the fluctuation equation. The results show that the infrared feature capture technique is able to maintain efficient motion track correction and motion speed-up capability based on real-time data processing in dynamic environments.

关键词： Target tracking Accuracy image processing Gaussian distribution Numerical simulation Mathematical models Real-time systems Data models Numerical models Trajectory

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Semi-Supervised 3D Medical image Segmentation Using Shape-Guided Dual Consistency Learning

Semi-Supervised 3D Medical Image Segmentation Using Shape-Gu...

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Lei, Tao Liu, Hulin Chen, Qi Wang, Zexuan Zhang, Dong Wang, xingwu Du, xiaogang Lu, Bo Shaanxi University of Science and Technology Shaanxi Joint Laboratory of Artificial Intelligence Xi'an710021 China School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi'an710021 China CO. Ltd Xi'an712000 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Popular semi-supervised image segmentation networks suf-fer from two problems: firstly, supervision is only performed on the last layer of the decoder, resulting in the network's weak generalization ability;secondly, the geometry shape constraints of targets are frequently disregarded in these net-works, leading to poor segmentation results. To address these issues, we propose a novel shape-guided dual consistency semi-supervised learning framework for 3D medical image segmentation. The proposed framework makes two contri-butions. Initially, we introduce a shape constraint to learn the shape representation, which converts the difference be-tween two networks into an unsupervised loss and lets the model learn the boundary information of targets. Addition-ally, we develop a deep-supervised knowledge transfer strat-egy that improves the generalization ability of the network without increasing extra computation costs. Experiments demonstrate that the proposed framework outperforms state-of-the-art semi-supervised methods due to the strong ability of knowledge mining on unlabeled data. © 2022 IEEE.

关键词： Deep learning

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Low-Rank Matrix and Tensor Decomposition Using Randomized Two-Sided Subspace Iteration With Application to Video Reconstruction

Low-Rank Matrix and Tensor Decomposition Using Randomized Tw...

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IEEE International conference on image processing

作者： Maboud F. Kaloorazi Salman Ahmadi-Asl Susanto Rahardja School of Electronic Engineering Xi’an Shiyou University China Lab of Machine Learning and Knowledge Representation Innopolis University Russia Engineering Cluster Singapore Institute of Technology Singapore

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The low-rank approximation of big data matrices and tensors plays a pivotal role in many modern applications. Recently, the randomized subspace iteration has shown to be a powerful tool in approximating large matrices. In this paper we present a rank-revealing, two-sided variant of the randomized subspace iteration. Novelty of our work lies in the utilization of the unpivoted QR factorization, rather than the singular value decomposition (SVD), for factorizing the compressed matrix. We provide bounds on the rank-revealingness of our algorithm as well as bounds on the error of the low-rank approximations, in both 2- and Frobenius norm. In addition, we employ the proposed algorithm to efficiently compute the low rank tensor decomposition using the truncated higher-order SVD. We conduct tests on (i) two classes of matrices, and (ii) synthetic data tensor and real dataset to demonstrate the efficacy of the proposed algorithms.

关键词： Tensors image coding Supervised learning Approximation algorithms Matrix decomposition Reliability Task analysis

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