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检索条件"机构=Key Laboratory of Embedded Real-time Information Processing Technology"

共 968 条记录，以下是201-210 订阅

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Unleashing Fine-Coarse Curve Perception Via Trunk-Branch Perturbation

Unleashing Fine-Coarse Curve Perception Via Trunk-Branch Per...

引用

IEEE International Conference on Image processing

作者： Yunxiang Cao Li Chen Yubo Wang Zhida Feng Xiaoming Liu School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Segmenting intricate curve structures like retinal blood vessels, encompassing both fine and coarse details, remains a significant challenge. This work proposes a novel module that divides these complex curve structures into trunks and branches, fuses the input as auxiliary information, and optimizes the breakpoints of different curve parts through losses. In order to balance the redundant information that may lead to model overfitting, a unique feature perturbation strategy is introduced after the backbone decoding process to enhance the model’s robustness to complex curve structure segmentation tasks. Experiments show that this method can effectively distinguish different topological structures of blood vessels and maintain high segmentation accuracy even at blood vessel intersections or breakpoints, which holds immense potential for diverse future applications in image segmentation.

关键词： Measurement Image segmentation Fuses Perturbation methods Blood vessels Retina Robustness

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B-Walk: Bernoulli Principle Guided Biased Random Walk for Curve Connection

B-Walk: Bernoulli Principle Guided Biased Random Walk for Cu...

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IEEE International Conference on Image processing

作者： Zhuang Sun Li Chen Zhida Feng Xiaoming Liu School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In the segmentation of curve structures, the discontinuity may lead to an incomplete topological representation of the structure. The current methods for reconnecting curve structures lack physical explanations and are limited in their effectiveness in image processing. In order to address these constraints, a new algorithm for reconnecting curve structures in segmentation is proposed by combining fluid mechanics principles, especially Bernoulli’s principle and random walks. This algorithm calculates the similarity of fracture curves to find the fracture *** the energy calculation method during the reconnection process and calculated the probability of energy transfer. It adopts a biased random walk guided by the energy transfer probability in the graph until the walker reaches the target. This innovative approach provides a more comprehensive physical explanation and improves the effectiveness of image processing.

关键词： Image segmentation Fluids Accuracy Energy exchange Probability

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An improved ISAR imaging method under low SNR 14

An improved ISAR imaging method under low SNR

引用

14th European Conference on Synthetic Aperture Radar, EUSAR 2022

作者： Gao, Yongpeng Ding, Zegnag Zhang, Tianyi Liu, Shujiang Guo, Xin Li, Pucheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401147 China DFH Satellite Co. Ltd Beijing100094 China

ISBN: (纸本)9783800758234

Current inverse synthetic aperture radar (ISAR) imaging methods cannot perform well when the signal-To-noise ratio (SNR) is low. In this paper, an improved ISAR imaging method based on generalized Radon-Fourier transform (GRFT) and relaxation (RELAX) technique is proposed. Through the proposed method, a well-focused radar image of moving target under low SNR condition can be obtained. Further, the estimation error caused by off-grid points as well as the unnecessary side lobe and main lobe values introduced by GRFT based ISAR imaging can be solved. Simulations are conducted to verify the effectiveness of the proposed method. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Inverse synthetic aperture radar

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Raindrop Removal for In-Vehicle Camera Images with Generative Adversarial Network

Raindrop Removal for In-Vehicle Camera Images with Generativ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhao, Zihao Jiang, Min Guo, Jia Yang, Xiaoyu Hu, Yudie Zhou, Xianlong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Raindrop removal for in-vehicle camera images is useful for surveillance and analysis system such as intelligent driving or case diagnosis. As in rainy days, images taken by in-vehicle cameras such as monitor or drive recorder often suffer from noticeable image degradation, and make it difficult to identify the objects on the road. With the uncertainty of raindrop distribution and the complexity of raindrop status, the attached raindrops of in-vehicle camera images can have different effects. And the large-diameter raindrop will greatly reduce the image quality. However, most of the popular deraining algorithms have been successful in recovering images with small raindrop noise and tiny image distortion, but fails to restore those with raindrops covering large areas. We propose a single image raindrop removal network based on Generative Adversarial Network for in-vehicle camera images. We incorporate two overlapping attention layers into the deraining network, which adopt task-driven visual attention and content perception mechanism. The former obtains features by recursive neural network to guide network more interested in the raindrop regions and the surrounding structures, while the content perception schema extracts the features far from raindrops. Moreover, we collect a new benchmark for In-Vehicle Camera Image Deraining (IVCRID), it chooses in-vehicle camera images from various deraining dataset. The experimental results show that the proposed deraining network outperforms other state-of-the-art raindrop removal methods in image recovering test on the IVCRID dataset and also has the best performance on the experiments of traffic objects detection. © 2022 IEEE.

关键词： Generative adversarial networks

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DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Dual-Teacher Semi-Supervised Network for Kidney Ultrasound Image Segmentation

DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Du...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yang Luo Jun Liu Mengqian Ding School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Accurate segmentation of kidney ultrasound (KUS) images is of great significance for the diagnosis of kidney diseases. However, KUS images have challenges such as low contrast, large size variations, and acoustic shadow interference. In addition, obtaining labeled data is a major challenge in the field of medical image segmentation. This paper proposes a dual-branch hybrid CNN Transformer combined with a dual teacher semi-supervised segmentation network (DHCT-DT) based on the MT model. The reliability of guidance can be improved between two teachers by constructing consistency regularization constraints. The U-Net architecture is employed between the main teacher and student model. The auxiliary teacher model adopts a dual-branch hybrid CNN Transformer architecture. The multi-scale channel fusion attention mechanism (MSCF) is introduced to capture the feature correlation information of KUS images at different scales and channels. Two different training methods can combine their advantages to fully extract local and global information from images. To demonstrate the applicability of the proposed method, experiments were conducted on two datasets: a KUS dataset and a breast ultrasound (BUS) dataset. Both datasets demonstrated the most competitive segmentation performance. For example, in a semi-supervised experiment using only 10% labeled data in the BUS dataset, the Dice coefficient, Jaccard coefficient, HD, and ASSD reached 82.28%, 76,36%, 13.36mm, and 3.71mm.

关键词： Training Image segmentation Ultrasonic imaging Attention mechanisms Accuracy Predictive models Network architecture Transformers Reliability Kidney

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A Lightweight and key Escrow-Free Access Control Scheme on Encrypted Data in Edge Computing

A Lightweight and Key Escrow-Free Access Control Scheme on E...

引用

International Conference on Advanced Cloud and Big Data (CBD)

作者： Heng He Yuejia Chen Jianfeng Lu Jiacheng Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Attribute-based encryption provides fine-grained access control, ensuring data security in edge computing. However, existing access control schemes face issues such as high computational complexity due to bilinear pairing and key escrow with a single authority, which limit their effectiveness for lightweight terminal devices. To address these challenges, we propose a lightweight and key escrow-free access control scheme on encrypted data. This scheme replaces bilinear pairing with fast elliptic curve scalar multiplication, transferring most computational tasks to edge servers while preserving privacy, thus alleviating the computational burden on terminal devices. Additionally, we design a blockchain-based distributed key management method that allows multiple edge servers to distribute private keys collaboratively, resolving the key-escrow issue. Security analysis and performance evaluation demonstrate that our scheme effectively ensures data confidentiality, resists collusion attacks, and achieves high computational efficiency on the user side with low storage overheads, making it highly suitable for lightweight devices in edge computing.

关键词： Access control Performance evaluation Privacy Elliptic curves Process control Transforms Resists Encryption Servers Edge computing

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CC-Net: Consistency Learning Combined with Contrastive Learning for Kidney Ultrasound Segmentation

CC-Net: Consistency Learning Combined with Contrastive Learn...

引用

IEEE International Conference on Systems, Man and Cybernetics

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Due to the scarcity of medical images with labels and the fact that medical image data is not easily accessible. Kidney ultrasound (KUS) images have speckle noise, low image quality, acoustic shadows, and other reasons, which pose significant challenges to the kidney ultrasound image segmentation task. To solve the above two problems, a novel consistency learning and contrastive learning-based kidney ultrasound image segmentation method (CC-Net) has been proposed. Specifically, the student-teacher model was adopted as the semi-supervised learning framework and uncertainty estimation was introduced. The Consensus Uncertainty Map (CUM) strategy was proposed to generate segmentation results for the teacher model when calculating uncertainty. To solve the problems of acoustic shadows and low image quality in kidney ultrasound images, a kidney ultrasound image data augmentation method (KUS-AUG) and a convolutional block (KUS-CONV) tailored to the characteristics of kidney ultrasound images were proposed. In addition, contrastive learning was implemented to enhance the encoder's ability to learn more semantic information, due to its inherent advantage in learning data distribution. Finally, a series of experiments were conducted on the ultrasound image dataset and the method achieved the best performance.

关键词： Image quality Image segmentation Ultrasonic imaging Uncertainty Noise Contrastive learning Semisupervised learning Acoustics Kidney Biomedical imaging

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Code Generation Based on Quality-Aware Reordering

Code Generation Based on Quality-Aware Reordering

引用

6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： Yuan Song Luo Yu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Translation efforts from natural language to code generation have achieved great success with large pre-trained language models. However, selecting the correct code output for the problem posed by the generated set of candidate codes is still challenging. In this paper, we address this problem by proposing an outcome-based quality-aware reordering method for code selection. This method scores each code by whether the set of candidate codes passes the test cases or not as a reference to select the top k best code outputs. In addition, this method depends on the quality and number of test cases. However, it is impractical to manually create a large number of test cases for each problem, so this paper adopts an automated approach that utilizes the current model to generate test cases for each problem. The experimental results show that the approach proposed in this paper obtains a significant improvement in the pass@k benchmark for each model, and the correctness and quality of test cases generated automatically using the model are discussed experimentally. Summarizing the experimental results and discussions, the result-based quality-aware reordering method proposed in this paper achieves a significant improvement in solving the problem of selecting the correct output from natural language to code generation, effectively improves the accuracy of evaluation by automating the generation of test cases, and provides an effective solution to improving the correctness of code generation.

关键词： Codes Accuracy Computational modeling Estimation Signal processing Benchmark testing

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MAML MOT: Multiple Object Tracking Based on Meta-Learning

MAML MOT: Multiple Object Tracking Based on Meta-Learning

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jiayi Chen Chunhua Deng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the advancement of video analysis technology, the multi-object tracking (MOT) problem in complex scenes involving pedestrians is gaining increasing importance. This challenge primarily involves two key tasks: pedestrian detection and re-identification. While significant progress has been achieved in pedestrian detection tasks in recent years, enhancing the effectiveness of re-identification tasks remains a persistent challenge. This difficulty arises from the large total number of pedestrian samples in multi-object tracking datasets and the scarcity of individual instance samples. Motivated by recent rapid advancements in meta-learning techniques, we introduce MAML MOT, a meta-learning-based training approach for multi-object tracking. This approach leverages the rapid learning capability of meta-learning to tackle the issue of sample scarcity in pedestrian re-identification tasks, aiming to improve the model's generalization performance and robustness. Experimental results demonstrate that the proposed method achieves high accuracy on mainstream datasets in the MOT Challenge. This offers new perspectives and solutions for research in the field of pedestrian multi-object tracking.

关键词： Metalearning Training Pedestrians Accuracy Robustness Object tracking Optimization Cybernetics

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Transductive Multi-Prototype Network for HRRP Recognition with Missing Aspects

Transductive Multi-Prototype Network for HRRP Recognition wi...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Mingchen Yuan Jiaqi Liu Xinyang Wang Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Zhengzhou Academy Of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The High-Resolution Range Profile (HRRP) plays a pivotal role in radar automatic target recognition. However, due to its aspect sensitivity, the absence of aspects within the training data can lead to a decline in recognition accuracy. To mitigate this issue, we introduce a novel Transductive Multi-Prototype Network (TMP-Net), leveraging the intrinsic similarity within test data to propagate labels along a manifold, thereby achieving classification. TMP-Net addresses the aspect sensitivity by utilizing multiple prototypes per class. Additionally, a loss function term is proposed to ensure the existence of the manifold. Experiments on the MSATR dataset demonstrate that TMP-Net outperforms existing methods by 10.22% in recognition accuracy even with only 10% of aspects represented in the training set. Furthermore, analysis reveals the advantages of employing multiple prototypes.

关键词： Manifolds Training Sensitivity Accuracy Target recognition Prototypes Training data Radar Data processing

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