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检索条件"主题词=convolutional autoencoder"

共 405 条记录，以下是91-100 订阅

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HYPERSPECTRAL ANOMALY DETECTION OF HIDDEN CAMOUFLAGE OBJECTS BASED ON convolutional autoencoder 3

HYPERSPECTRAL ANOMALY DETECTION OF HIDDEN CAMOUFLAGE OBJECTS...

引用

Conference on Artificial Intelligence and Machine Learning in Defense Applications III Held as Part of SPIE Security+Defence Conference

作者： Kuester, Jannick Gross, Wolfgang Middelmann, Wolfgang Fraunhofer IOSB Fraunhofer Ctr Machine Learning Ettlingen Germany

ISBN: (数字)9781510645851

ISBN: (纸本)9781510645851;9781510645844

The rising availability of hyperspectral data has increased the attention of anomaly detection for various applications. Anomaly detection aims to find a small number of pixels in the hyperspectral data for which the spectral signatures differ significantly from the background. However, for anomalies like camouflage objects in a rural area, the spectral signatures distinguish only by small features. For this purpose, we use a 1D-convolutional autoencoder, which extracts the background spectra's most specific features to reconstruct the spectral signature by minimizing the loss function's error. The difference between the original and the reconstructed data can be exploited for anomaly detection. Since the loss function is minimized based on predominant background spectra, areas with anomalies exhibit higher error values. The proposed anomaly detection method's performance is tested on hyperspectral data in the range of 1000 to 2500 nm. The data was recorded with a drone-based Headwall sensor at approximately 80 m over a rural area near Greding, Germany. The anomalies consist mainly of camouflage materials and vehicles. We compare the performance of a 1D-convolutional autoencoder trained on a data set without the target anomalies for different models. This is done to quantify the number of anomalies in the data set before they inhibit the detection process. Additionally, the detection results are compared to the state-of-the-art Reed-Xiaoli anomaly detector. We present the results by counting the correct detections in relation to the false positives with the receiver operating characteristic and discuss more suitable evaluation approaches for small targets. We show that the 1D-CAE outperforms the Reed-Xiaoli anomaly detector for a false alarm rate of 0.1% by reconstructing the background with a low error and the anomalies with a higher error. The 1D-CAE is suitable for camouflage anomaly detection.

关键词： hyperspectral anomaly detection compression error convolutional autoencoder target detection camouflage

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A convolutional autoencoder Approach for Weakly Supervised Anomaly Video Detection 15th

A Convolutional Autoencoder Approach for Weakly Supervised A...

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15th International Conference on Computational Collective Intelligence (ICCCI)

作者： Phan Nguyen Duc Hieu Phan Duy Hung FPT Univ Hanoi Vietnam

ISBN: (纸本)9783031414558;9783031414565

Weakly-supervised video anomaly detection uses video-level labels to avoid annotating all frames or segments in the training video. This problem is typically considered as a multiple instance learning problem, the training process aims to learn how to score both abnormal segments and normal segments, and the score of abnormal segments is higher than the score of normal segments. The features are extracted from videos before the training or testing process. Although many models have been proposed and obtained good results, improving the performance of the problem remains a challenge. This study proposes a convolutional autoencoder based approach to reconstruct features, with an assumption that the reconstructed features contain important information early bound to the objective of normal or abnormal video classification (minimizing the cost function). The work is validated on the ShanghaiTech Campus dataset and has produced results that outperform state-of-the-art methods.

关键词： Anomaly Video Detection convolutional autoencoder Multiple Instance Learning

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A Feature Extraction Method Based on convolutional autoencoder for Plant Leaves Classification 2nd

A Feature Extraction Method Based on Convolutional Autoencod...

引用

2nd IEEE Colombian Conference on Computational Intelligence (ColCACI)

作者： Paco Ramos, Mery M. Paco Ramos, Vanessa M. Loaiza Fabian, Arnold Osco Mamani, Erbert F. Univ Nacl Jorge Basadre Grohmann Tacna Peru

ISBN: (纸本)9783030362119;9783030362102

In this research, we present an approach based on convolutional autoencoder (CAE) and Support Vector Machine (SVM) for leaves classification of different trees. While previous approaches relied on image processing and manual feature extraction, the proposed approach operates directly on the image pixels, without any preprocessing. Firstly, we use multiple layers of CAE to learn the features of leaf image dataset. Secondly, the extracted features were used to train a linear classifier based on SVM. Experimental results show that the classifiers using these features can improve their predictive value, reaching an accuracy rate of 94.74%.

关键词： Feature extraction Image processing Plant classification convolutional autoencoder Deep learning Computer vision

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SDCAE: Stack Denoising convolutional autoencoder Model for Accident Risk Prediction via Traffic Big Data 6

SDCAE: Stack Denoising Convolutional Autoencoder Model for A...

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6th International Conference on Advanced Cloud and BigData (CBD)

作者： Chen, Chao Fan, Xiaoliang Zheng, Chuanpan Xiao, Lujing Cheng, Ming Wang, Cheng Xiamen Univ Fujian Key Lab Sensing & Comp Smart City Xiamen Peoples R China Xiamen Univ Digital Fujian Inst Urban Traff Big Data Res Xiamen Peoples R China

ISBN: (纸本)9781538680346

Traffic accident is considered as one of main causes for traffic congestion in cities. There are many causal factors that may give rise to traffic accidents, e.g. driver characteristics, road conditions, traffic flows and weather conditions, etc. Due to uncertain factors as well as the contingency of accident occurrences, it is very difficult to predict traffic accidents. Many existing works have utilized classical prediction models to predict the risk of accidents on highways or road segments. However, predicting the risk of citywide accidents remains an open issue. To address this problem, we propose SDCAE, a novel Stack Denoise convolutional Auto-Encoder algorithm to predict the risk of traffic accident in the city-level. First, we divided the city into regions by counting the number of accidents and traffic flows in each region. Second, we employed a deep model of stack denoise convolutional autoencoder which considers spatial dependencies to learn the hidden factors in accidents. Third, we conducted extensive experiments on two real-world cross-domain traffic big datasets from a major city of China for accident risk prediction. Experimental results demonstrate that SDCAE could outperforms five baseline methods.

关键词： traffic accident convolutional autoencoder risk prediction

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3D intracranial artery segmentation using a convolutional autoencoder

3D intracranial artery segmentation using a convolutional au...

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Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Chen, Li Xie, Yanjun Sun, Jie Balu, Niranjan Mossa-Basha, Mahmud Pimentel, Kristi Hatsukami, Thomas S. Hwang, Jenq-Neng Yuan, Chun Univ Washington Dept Elect Engn Seattle WA 98195 USA Univ Washington Dept Mech Engn Seattle WA 98195 USA Univ Washington Dept Radiol Seattle WA 98195 USA Univ Washington Dept Surg Seattle WA 98195 USA

ISBN: (纸本)9781509030507

Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction and feature extraction by representing high dimensional information with low dimensional latent variables. In this paper, we trained an 8-layer CAE to learn a 3D segmentation model of intracranial arteries from 49 cases of MRA data. After parameter optimization and prediction refinement, our trained model was shown to perform better than the three traditional segmentation methods in both binary classification and visual evaluation.

关键词： convolutional autoencoder deep neural network machine learning artery segmentation Magnetic Resonance Angiography

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Anomaly Detection for Industrial Control Systems Using K-Means and convolutional autoencoder 27

Anomaly Detection for Industrial Control Systems Using K-Mea...

引用

27th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

作者： Chang, Chun-Pi Hsu, Wen-Chiao Liao, I-En Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung Taiwan Natl Taichung Univ Sci & Technol Dept Informat Management Taichung Taiwan

ISBN: (纸本)9789532900880

With the rise of Internet of Things technology and smart manufacturing, the issues of cyber security in industrial control systems (ICS) have become of great concern. Critical infrastructures, such as power plants and water treatment facilities, which heavily rely on ICS, have increasingly come under cyberattack in recent years. These cyberattacks pose severe threats to public safety as well as national security. Therefore, protecting ICS from cyberattack is an important research issue in the era of information technology and operation technology convergence. In this study, we propose an anomaly detection method based on semi-supervised techniques for industrial control systems, using the k-means and convolutional autoencoder methods. Experimental results show that the proposed method outperforms the other methods in almost all performance metrics for gas pipeline and water storage tank datasets.

关键词： anomaly detection intrusion detection system industrial control system k-means convolutional autoencoder

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A Multi-Temporal convolutional autoencoder Neural Network for Cloud Removal in Remote Sensing Images 15

A Multi-Temporal Convolutional Autoencoder Neural Network fo...

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15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Sintarasirikulchai, Wassana Kasetkasem, Teerasit Isshiki, Tsuyoshi Chanwimaluang, Thitiporn Rakwatin, Preesan Kasetsart Univ Dept Elect Engn Bangkok Thailand Tokyo Inst Technol Dept Commun & Comp Engn Tokyo Japan Natl Elect & Comp Technol Ctr NECTEC Pathum Thani Thailand Digital Econ Agcy Bangkok Thailand

ISBN: (纸本)9781538635551

The uncontrollable weather conditions can cause a serious problem to remote sensing imaginary. One of the weather conditions is a resulting from cloud contamination. As a result, this paper proposed the use of the convolutional autoencoder neural networks to remove clouds from cloud-contaminated images by training on a multi-temporal remote sensing dataset. Here, the observations from different spectral bands are assumed to be independent since their spectral responses are usually non-overlapped. From this assumption, each convolutional autoencoder neural networks are trained with the observation from only one spectral band. In our method, we have three convolutional autoencoder neural networks for red, green and blue spectral bands. The experiments were conducted on both synthesis and real dataset derived from the actual LANDSAT 8 images from the central part of Thailand where our algorithm has shown to have a superb performance.

关键词： Cloud Removal Multi-Temporal convolutional autoencoder Deep Learning Remote Sensing Image Restoration

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ABCD: Trust enhanced Attention based convolutional autoencoder for Risk Assessment 24

ABCD: Trust enhanced Attention based Convolutional Autoencod...

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9th International Conference on Machine Learning Technologies (ICMLT)

作者： Naidu, Sarala M. Xiong, Ning Malardalen Univ Sch Innovat Design & Engn Vasteras Sweden Hitachi Energy Grid Integerat Power Grids Vasteras Sweden

ISBN: (纸本)9798400716379

Anomaly detection in industrial systems is crucial for preventing equipment failures, ensuring risk identification, and maintaining overall system efficiency. Traditional monitoring methods often rely on fixed thresholds and empirical rules, which may not be sensitive enough to detect subtle changes in system health and predict impending failures. To address this limitation, this paper proposes, a novel self-supervised learning technique using autoencoder based hybrid models called Attention-based convolutional autoencoder (ABCD) for risk detection and mapping risk value derive to the maintenance planning. ABCD learns the normal behavior of conductivity from historical data of a real-world industrial cooling system and reconstructs the input data, identifying anomalies that deviate from the expected patterns. The framework also employs calibration techniques to ensure the reliability of its predictions. Evaluation of results demonstrate that with the attention mechanism in ABCD a 57.4% increase in performance and a reduction of false alarms by 9.37% is seen compared to without attentions. This approach can effectively detect risks, the risk priority rank mapped to maintenance, providing valuable insights for cooling system designers and service personnel. Calibration error of 0.03% indicates that the model is well-calibrated and enhances model's trustworthiness, enabling informed decisions about maintenance strategies.

关键词： Attentions convolutional autoencoder cooling liquid conductivity Anomaly detection Calibration error

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Anomaly Detection of Logo Images in the Mobile Phone Using convolutional autoencoder 4

Anomaly Detection of Logo Images in the Mobile Phone Using C...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Ke, Muyuan Lin, Chunyi Huang, Qinghua South China Univ Technol Dept Elect & Informat Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538611074

There are Logo images printed in the mobile phone. Anomaly detection of Logo image is an important quality control task in the intelligent manufacture. In real applications, there are not enough negative samples in the production line for us to study their difference from normal samples. In this paper, we propose an unsupervised learning method based on convolutional autoencoder (CAE) to generate the template of sample and detect the abnormal information through comparing test images with the adaptive template. Firstly, several methods of data augmentation are introduced to expand the scale of positive samples, aiming to improve the performance of CAE. Secondly, the topology of proposed CAE model is introduced. Thirdly, we introduce the image processing methods to detect and locate the abnormal information in the Logo image. A series of experiments on three group of different Logo image have shown that the method we proposed can effectively detect most of the anomalies in the image and achieve the average accuracy of 98.9%.

关键词： Data Augmentation convolutional autoencoder Anomaly Detection Image Processing

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Power Doppler Imaging with a Novel convolutional autoencoder Based Clutter Filtering Technique: Feasibility Study

Power Doppler Imaging with a Novel Convolutional Autoencoder...

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IEEE International Ultrasonics Symposium (IUS)

作者： Shih, Chih-Hsiang Li, Meng-Lin Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan Natl Tsing Hua Univ Inst Photon Technol Hsinchu Taiwan

ISBN: (纸本)9781728145969

Singular value decomposition (SVD)-based clutter filters have shown its outstanding ability in clutter rejection for ultrafast plane-wave Doppler imaging. However, automatic decision on order selection still remains a big problem in vivo and the computational complexity of the SVD-based clutter filter is also high for clinical translation. In this study, we propose a novel convolutional autoencoder based clutter filtering technique for power Doppler, which retains the characteristic principle component analysis (PCA) done by the SVD filtering while being with no need for order selection and potentially lower computational load. The autoencoder is a counterpart for PCA in deep learning field. The simulation results show that the proposed convolutional autoencoder clutter filtering technique can robustly reject tissue clutter whose spectrum overlaps that of the blood signal, which is impossible to accomplish with conventional highpass filtering, and outperforms the SVD filtering. Our simulation results show that the contrast-to-noise ratio (i.e., blood-to-clutter ratio) of our proposed method is better than those of the SVD-based filter and the highpass filter by 23.97 dB and 64.17 dB, respectively.

关键词： power Doppler clutter filter singular value decomposition convolutional autoencoder neural network

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