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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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Adversarial Reconstruction for Outdoors Insulator Anomaly Detection and Recognition in High-Speed Railway Traction Substation

Adversarial Reconstruction for Outdoors Insulator Anomaly De...

引用

6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yuchen Peng Xuemin Lu Wei Quan Ning Zhou Dong Zou Jim X. Chen School of Electrical Engineering Southwest Jiaotong University Chengdu China State Key Laboratory of Traction Power Southwest Jiaotong University Chengdu China George Mason University Fairfax Virginia USA

As an indispensable part of the high-speed railway traction substation,the outdoors insulator ensures the normal operation of transmission network and defends the stability of transmission line. In order to avoid the serious transmission fault and decrease the economic loss, finding a effective way to find the anomaly regions of insulator is a meaningful issue. Therefore, a method is presented for outdoors insulator anomaly detection and recognition in high-speed railway traction substation based on adversarial reconstruction in this paper. First, the R3Det is employed to fix the position of insulator from the input image of traction substation. At the second stage, the insulator image which located from the first stage is entered into our designed generative adversarial networks to reconstruct input image. In the end, the structural similarity algorithm is adopted to compare the difference between the input image and the generated image to find the anomalous region, then template matching is used for recognition of the anomalous area. Test results on Heishan traction substation show the effective performance of our designed method.

关键词： Visualization Substations image recognition signal processing algorithms Insulators Generative adversarial networks Rail transportation

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Quality Assessment оf Defocused image Recovery algorithms 2020

Quality Assessment оf Defocused Image Recovery Algorithms

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2020 3rd international conference on Sensors, signal and image processing

作者： Magdalena Garvanova Vladimir Ivanov University of Library Studies and Information Technologies Bulgaria Institute of Information and Communication Technologies-BAS Bulgaria

ISBN: (纸本)9781450388283

Camera shake during video recording is a major source of image defocus. Restoring these defocuses is one of the most important tasks in image processing. This article examines and evaluates the most commonly used algorithms in practice, namely: Wiener filtration, Richardson-Lucy algorithm, Tikhonov regularization, and Blind deconvolution. The comparisons were made using structural similarity and root mean square error.

关键词： camera shake image processing defocuses

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Lossy Data Compression using K-Means Clustering on Retinal images using RStudio

Lossy Data Compression using K-Means Clustering on Retinal I...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： S. Sivaarunagirinathan B. Ajith Bala Shaik Fairooz G. Sasi Har Narayan Upadhyay V. Elamaran School of EEE SASTRA Deemed University Thanjavur India Malla Reddy Engineering College Secunderabad India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (纸本)9781665438124

The massive volume of data limits the use of data on portable devices like mobile phones, as well as memory-limited devices like video game consoles. As a result, the demand for data compression methods in the field of signal and image processing is skyrocketing. Data compression required advanced ways to serve better, with a greater compression ratio, due to the rising demands, which expected more sophisticated algorithms. This article primarily focuses on image compression (retinal images) utilizing the k-means clustering technique, which is a popular unsupervised classification strategy in machine learning. The compression challenge in this study uses two retinal pictures. The most useful open-source statistical calculation tool is R programming, which is employed here with RStudio, an Integrated Development Environment (IDE). The use of R (RStudio) has had an impact across all domains of research and engineering. The test retinal images had a compression ratio of 33.3 percent, according to simulation data.

关键词： image coding Machine learning algorithms Data compression Clustering algorithms Process control Programming Retina

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SV-Loop:Semantic-Visual Loop Closure Detection with Panoptic Segmentation

SV-Loop:Semantic-Visual Loop Closure Detection with Panoptic...

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IEEE international conference on signal and image processing (ICSIP)

作者： Zhian Yuan Ke Xu Bin Deng Xiaoyu Zhou Peibo Chen Yanxin Ma College of Electronic Science National University of Defense Technology Changsha China College of Meteorology and Oceanography National University of Defense Technology Changsha China Hunan Key Laboratory for Marine Detection Technology Changsha China

ISBN: (纸本)9781665446006

Loop closure detection plays an important role in simultaneous localization and mapping (SLAM) system. Traditional visual-based methods of loop closure detection are hard to relieve the issue of dynamic objects and change of light in complex environments. To improve robustness of loop closure detection, a loop closure detection algorithm named SV-Loop combines semantic and visual information is proposed in this paper. This work leverages the latest panoptic segmentation network to extract semantic information. Firstly, similarity of details is calculated through visual module in micro perspective. Then similarity of objects is computed by semantic module from a macro perspective. Finally, visual and semantic loop candidates are integrated to determine loop closure. Experiments have been conducted on open source datasets and practical scene. Experimental results have shown higher accuracy and better robustness of proposed method in complex environmental changes.

关键词： Location awareness Visualization image segmentation Simultaneous localization and mapping Heuristic algorithms Semantics Robustness

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Self-Adaptive Visual Background Extraction with Ghost Regions Elimination

Self-Adaptive Visual Background Extraction with Ghost Region...

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IEEE international conference on signal and image processing (ICSIP)

作者： Ling Liu Shuai Liu Zhong Qu Dongyang Zhou School of Big Data and Software Engineering College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665446006

The ViBe (Visual Background Extraction) algorithm uses the first frame to initialize the pixel background model for each pixel, which may introduce the ghost regions in the following video frames. Although ViBe can use its background model update mechanism to eliminate the ghost regions, it is time-consuming. Firstly, a ghost elimination algorithm is proposed in this paper in order to reduce the elimination time for the ghost regions based on its properties. Secondly, a fixed global segmentation decision threshold is used to segment pixels in ViBe algorithm. However, because it cannot adapt to the dynamic background, a self-adaptive segmentation decision threshold based on single pixel is used to adjust to the different backgrounds. Finally, a self-adaptive update factor is presented, which makes the background model more reliable and adjusts to the background changes through the self-adaptive processing. The experimental result shows that this improved method can quickly eliminate the ghost regions, and adapt to the different kinds of backgrounds, ensuring more accurate results.

关键词： Visualization Adaptation models Heuristic algorithms image processing conferences Lighting Reliability

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Structure-Enhanced Attentive Learning For Spine Segmentation From Ultrasound Volume Projection images

Structure-Enhanced Attentive Learning For Spine Segmentation...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Rui Zhao Zixun Huang Tianshan Liu Frank H. F. Leung Sai Ho Ling De Yang Timothy Tin-Yan Lee Daniel P.K. Lun Yong-Ping Zheng Kin-Man Lam The Hong Kong Polytechnic University Hong Kong China School of Biomedical Engineering University of Technology Sydney NSW Australia

ISBN: (纸本)9781728176055;9781728176062

Automatic spine segmentation, based on ultrasound volume projection imaging (VPI), is of great value in clinical applications to diagnose scoliosis in teenagers. In this paper, we propose a novel framework to improve the segmentation accuracy on spine images via structure-enhanced attentive learning. Since the spine bones contain strong prior knowledge of their shapes and positions in ultrasound VPI images, we propose to encode this information into the semantic representations in an attentive manner. We first revisit the self-attention mechanism in representation learning, and then present a strategy to introduce the structural knowledge into the key representation in self-attention. By this means, the network explores both the contextual and structural information in the learned features, and consequently improves the segmentation accuracy. We conduct various experiments to demonstrate that our proposed method achieves promising performance on spine image segmentation, which shows great potential in clinical diagnosis.

关键词： image segmentation Ultrasonic imaging Shape conferences Semantics Imaging signal processing algorithms

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Multi-modal Medical image Fusion Technique to Improve Glioma Classification Accuracy

Multi-modal Medical Image Fusion Technique to Improve Glioma...

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IEEE international conference on signal and image processing (ICSIP)

作者： Hikmat Ullah Yaqin Zhao Longwen Wu Alam Noor Liang Zhao School of Electronics and Information Engineering Harbin Institute of Technology Harbin P.R. China Institute of Industrial and Electrical Testing China Railway Harbin Group Co. Ltd Harbin P.R. China

ISBN: (纸本)9781665446006

Usually, the low grade (LGG) and high-grade glioma (HGG) classification algorithms proposed in the literature directly concatenate the magnetic resonance image (MRI) modalities that directly affect the accuracy and precision results. Therefore, here this problem is highlighted at the initial level by applying a multi-modality fusion scheme. First, multi-level edge-preserving filtering (MLEPF) is applied to decompose the source images into fine-structure (FS), coarse-structure (CS), and base (BS) layers. Then, the novel sum-modified Laplacian (NSML) and parameter Adaptive PCNN based fusion strategy is adopted for the fusion FS and CS layers. Where region energy (RE) and information entropy (IE) based fuzzy pixel rules are implemented for the fusion of BS layers. The final fused image is achieved by integrating all the three fused layers. Visual and quantitative analysis prove that the proposed scheme results are satisfactory compare to the state-of-art. The results were also evaluated by using the Google Inception V3 convolutional neural network (CNN).

关键词： Visualization Laplace equations Statistical analysis Magnetic resonance imaging image edge detection Magnetic resonance Internet

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PolSAR image Classification Based on Resblock Combined with Attention Model

PolSAR Image Classification Based on Resblock Combined with ...

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IEEE international conference on signal and image processing (ICSIP)

作者： Zhongao Yang Qingyuan Zhang Weidong Chen Chang Chen School of Microelectronics University of Science and Technology of China Hefei University of Science and Technology of China Hefei

ISBN: (纸本)9781665446006

Deep learning technology can effectively extract the rich polarimetric information contained in polarimetric Synthetic aperture radar (PolSAR) data. However, the lack of labeled samples limits its performance. The majority of methods use iterative algorithms or need preprocessing on dataset to expand the labeled sample are time consuming. In this paper, an end-to-end method which can better utilize mid-level features and the input polarimetric features of CNN network is proposed. First, high-dimensional polarimetric features are extracted from raw data and sent into the CNN network to produce sufficient polarimetric information, then the Convolutional Block Attention Module (CBAM) is not only inserted into the network layer but also after the input data to achieve better classification performance and accelerate network convergence under tiny amounts of labeled samples. Meanwhile, a feature selection method is proposed based on the channel attention module to reduce the memory consumption and computational complexity while maintaining performance. The experiments tested on Flevoland dataset prove the effectiveness of proposed method compared with traditional method.

关键词： Deep learning Computational modeling Semantics Memory management Feature extraction Information filters Polarimetric synthetic aperture radar

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Distributed Analysis Dictionary Learning Based on Consensus Constraints

Distributed Analysis Dictionary Learning Based on Consensus ...

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IEEE international conference on signal and image processing (ICSIP)

作者： Liu Yang Jing Dong Jie Zhang Xiaoqing Luo Jian Guan College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China College of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9781665446006

Distributed dictionary learning is to learn a global dictionary so that all data distributed in a network has sparse representation in the domain of the dictionary. Existing works are based on sparse synthesis model. We consider this problem based on the sparse analysis model, and propose a distributed analysis dictionary learning (ADL) algorithm using consensus constraints. In particular, local dictionaries corresponding to local data at each node are introduced, and the distributed ADL problem is formulated as a minimization problem with consensus constraints on local dictionaries and the global dictionary. An optimization method consisting of a sparse coding stage and a dictionary update stage is then developed. Experimental results have shown that the performance of the proposed algorithm is comparable to centralized ADL algorithms.

关键词： Analytical models Dictionaries image coding conferences Optimization methods Distributed databases Machine learning

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Fast: Feature Aggregation for Detecting Salient Object in Real-Time

Fast: Feature Aggregation for Detecting Salient Object in Re...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Lv Tang Bo Li Yanliang Wu Bo Xiao Shouhong Ding Nanjing University China Tencent Shanghai China School of Information Science and Technology Southwest Jiaotong University China

This paper introduces a method named FAST for real-time salient object detection with an extremely efficient CNN architecture. Our proposed network starts from a single lightweight backbone and aggregates discriminative features through network-level and phase-level respectively. Based on the multi-scale feature propagation, FAST substantially reduces the number of parameters, but still obtains sufficient receptive field and enhances the model learning ability, which strikes a balance between the speed and performance. To better preserve object boundaries, we also explore the complementary between salient object information and edge information within our lightweight architecture. Extensive evaluations and analysis demonstrate that the proposed algorithm achieves the leading accuracy performance with real-time speed (186fps) which is significantly faster than the existing state-of-the-art methods.

关键词： Convolution image edge detection conferences Aggregates signal processing algorithms Object detection Feature extraction

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