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检索条件"机构=Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System"

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Sparse learning for robust background subtraction of video sequences 11th

Sparse learning for robust background subtraction of video s...

引用

11th International Conference on intelligent Computing, ICIC 2015

作者： Luo, Yuhan Zhang, Hong College 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: (纸本)9783319221793

Sparse representation has been applied to background detecting by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based methods only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the foreground and the background. Learning overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. To take these challenges, this paper proposes a new method for robust background detecting via sparse representation. Our method explores both the strength of the well-patch adaptive dictionary learning technique to video frame structure analysis and the robustness background detection by the l1-norm data-fidelity term. By using linear sparse combinations of dictionary atom, the proposed method learns the sparse representations of video frame regions corresponding to candidate particles. The experiments show that the proposed method is able to tolerate the background clutter and video frame deterioration, and improves the existing detecting performance. © Springer International Publishing Switzerland 2015.

关键词： Image representation

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industrial Air Pollution Prediction Using Deep Neural Network 1

引用

13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Pengfei, Yu Juanjuan, He Xiaoming, Liu Kai, Zhang School of Computer Science Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

In this paper, a deep neural network model is proposed to predict industrial air pollution, such as PM2.5 and PM10. The deep neural network model contains 9 hidden layers, each layer contains 45 neurons. The output of the hidden layer neurons is calculated using the ReLU activation function, which can effectively reduce the gradient elimination effect of the deep neural network. Twelve air pollutant indicators from industrial factories are collected as the input data, such as CO, NO2, O3, and SO2. About 180,000 real industrial air pollution data from Wuhan City are used to train and test the DNN model. Furthermore, the performance of our approach is compared with the SVM and Artificial neural network methods, and the comparison result shows that our algorithm is accurate and competitive with higher prediction accuracy and generalization ability. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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A Novel Image Recognition Method Based on CNN Algorithm 5th

A Novel Image Recognition Method Based on CNN Algorithm

引用

5th International Conference on Smart Computing and Communication, SmartCom 2020

作者： Guo, Yuwen Zhang, Xin Yang, Qi Guo, Hong College 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 Beijing China

ISBN: (纸本)9783030747169

Handwritten digital image recognition is a special problem in digital recognition, which is much more difficult due to uncertainties such as writer and writing environment. But the accuracy of handwritten digital recognition is particularly important in some critical areas such as public security and finance. In this paper, we used machine learning and deep learning technology, to identify MNIST handwritten digital data sets. And the support vector machine and convolutional neural network were used to image recognition, among them, the support vector machine compares the accuracy of the identification of the two kernel functions. Convolutional neural networks are improved based on the classic LeNet-5 model. The Experiments result shown that the identification accuracy of Gaussian kernel function model and linear kernel function model is 94.03% and 90.47% for support vector machines, respectively. In contrast, Gaussian kernel function performs better, but the overall accuracy is low. The best performance is based on the classic convolutional neural network LeNet-5 improved model, the test set recognition rate can reach 99.05%. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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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

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Luo, Yang Liu, Jun Ding, Mengqian 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: (纸本)9781665410205

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. © 2024 IEEE.

关键词： Contrastive Learning

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Pedestrian detection and counting based on ellipse fitting and object motion continuity for video data analysis 11th

Pedestrian detection and counting based on ellipse fitting a...

引用

11th International Conference on intelligent Computing, ICIC 2015

作者： Wang, Yaning Zhang, Hong College 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: (纸本)9783319221793

In order to detect and count pedestrians in different kinds of scenes, this paper put forward a method of solving the problem on video sequences captured from a fixed camera. After preprocessing operations on the original video sequences (Gaussian mixture modeling, three-frame-differencing, image binaryzation, Gaussian filtering, dilation and erosion) we extract the relatively complete pedestrian contours. Then we use the least square ellipse fitting method on those contours that has been extracted, the center of the ellipse is undoubtedly regarded as the tracking point of a pedestrian. With those points, a pedestrian matching pursuit and counting algorithm based on object motion continuity is used for tracking and counting pedestrians, this method can be better used in those scenes which are sparse and rarely obscured. Experiments validate that our pedestrian matching pursuit and counting algorithm has obvious superiorities: good real-time performance and high accuracy. © Springer International Publishing Switzerland 2015.

关键词： Geometry

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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...

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

ISBN: (纸本)9781665410205

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. © 2024 IEEE.

关键词： Image segmentation

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Metric Learning with Distillation for Overcoming Catastrophic Forgetting 16th

Metric Learning with Distillation for Overcoming Catastrop...

引用

16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Yu, Piaoyao He, Juanjuan Min, Qilang Zhu, Qi College 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: (纸本)9789811912528

In incremental learning, reducing catastrophic forgetting always adds additional weights to the classification layer when a new task comes. Moreover, it focuses on prediction accuracy, ignores mislabeling differences, and causes the learned features to be scattered. This paper removes the softmax and integrates depth metric learning to compute image embeddings, which doesn’t need to add extra weights or additional space for new classes. The distances of the data mapped in the old and new task spaces are calculated separately. Then, the distillation of depth metric learning is used to make the two distances as similar as possible to improve the performance of the old knowledge. The experimental results on Cifar10 and Tiny-ImageNet show that the proposed method can effectively alleviate catastrophic forgetting and enhance the effectiveness of incremental learning. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Distillation

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A Study on Semi-Supervised Semantic Segmentation of Ship Flame Images 29

A Study on Semi-Supervised Semantic Segmentation of Ship Fla...

引用

29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Wan, Junwei Chen, Yaojie Zheng, Xuecong Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350330717

In addressing the challenges of limited semantic labeling data and imprecise segmentation regions prevalent in the semantic segmentation of ship flame images, this study introduces a semi-supervised semantic segmentation method tailored for ship flame images. This approach is grounded in the principles of pairwise similarity with strong-weak perturbations. Adopting a pseudo-label self-learning strategy, the method employs weak-strong perturbation pairwise similarity, ensuring the propagation of high-confidence predictions during label generation, and thereby enhancing performance. Additionally, to tackle the inherent difficulties presented by smoke enveloping and obscuring flame regions in ship images, we have conceived a Vertical Continuity Feature Enhancement module. This module empowers the model to discern more explicit inter-class boundaries. Empirical evaluations on our proprietary dataset substantiate the superiority of our method, registering an improvement of 4.12 percentage points in mIoU over other semi-supervised approaches. Such results attest to the efficacy of the proposed pairwise similarity approach and the Vertical Continuity Feature Enhancement module in flame semantic segmentation. © 2023 IEEE.

关键词： Feature enhancemen Pairwise similarity Semantic segmentation Semi-Supervised learning

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Improved dendritic cell algorithm with false positives and false negatives adjustable 1

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11th International Conference on intelligent Computing, ICIC 2015

作者： Yuan, Song Xu, Xin College 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)9783319221809

ISBN: (纸本)9783319221793

In order to overcome the blindness of the evaluation on contexts in the classical Dendritic Cell Algorithm (DCA), how weight matrixes influence detection results is analyzed, and two kinds of DCA which can adjust false positives and false negatives are proposed. The first one is the improved voting DCA, the Tendency Factor (TF) is involved in the Dendritic Cell (DC) state transition to assess contexts fairly, and through the fine adjustment of TF false positives and false negatives of detection results are controlled;the other one is the scoring DCA, in the DC state transition phase the evaluation of contexts is ignored, instead, the antigen is directly given a score, then according to the distribution of average scores of antigens the anomaly threshold value can be adjusted to control false positives and false negatives. Experiments show that the two algorithms can both effectively realize results controlled, comparatively the scoring DCA is more intuitive. © Springer International Publishing Switzerland 2015.

关键词： Data fusion

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A Lightweight and key Escrow-Free Access Control Scheme on Encrypted Data in Edge Computing 12

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

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： He, Heng Chen, Yuejia Lu, Jianfeng Zheng, Jiacheng 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: (纸本)9798331511074

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. © 2024 IEEE.

关键词： Ciphertext

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