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Image-based fall detection and classification of a user with a walking support system

Sajjad TAGHVAEI, Kazuhiro KOSUGE

Frontiers of Mechanical Engineering 2018, Volume 13, Issue 3,   Pages 427-441 doi: 10.1007/s11465-017-0465-7

Abstract:

The classification of visual human action is important in the development of systems that interactThis study investigates an image-based classification of the human state while using a walking supportThe visual feature for the state classification is the centroid position of the upper body, which isThe classification results are employed to control the motion of a passive-type walker (called &ldquoemploy depth image-sensing devices.

Keywords： fall detection     walking support     hidden Markov model     multivariate analysis    

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Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 470-487 doi: 10.1007/s11684-020-0782-9

Abstract: In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology,including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation

Keywords： pathology     deep learning     segmentation     detection     classification    

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Astatistical distribution texton feature for synthetic aperture radar image classification Article

Chu HE, Ya-ping YE, Ling TIAN, Guo-peng YANG, Dong CHEN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1614-1623 doi: 10.1631/FITEE.1601051

Abstract: propose a novel statistical distribution texton (s-texton) feature for synthetic aperture radar (SAR) imageclassification.

Keywords： Synthetic aperture radar     Statistical distribution     Parameter estimation     Image classification    

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Automatic malware classification and new malwaredetection using machine learning Article

Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9,   Pages 1336-1347 doi: 10.1631/FITEE.1601325

Abstract: The explosive growth of malware variants poses a major threatto information security. Traditional anti-virus systems based on signaturesfail to classify unknown malware into their corresponding familiesand to detect new kinds of malware programs. Therefore, we proposea machine learning based malware analysis system, which is composedof three modules: data processing, decision making, and new malwaredetection. The data processing module deals with gray-scale images,Opcode n-gram, and import functions, which are employed to extractthe features of the malware. The decision-making module uses the featuresto classify the malware and to identify suspicious malware. Finally,the detection module uses the shared nearest neighbor (SNN) clusteringalgorithm to discover new malware families. Our approach is evaluatedon more than 20 000 malware instances, which were collected by Kingsoft,ESET NOD32, and Anubis. The results show that our system can effectivelyclassify the unknown malware with a best accuracy of 98.9%, and successfullydetects 86.7% of the new malware.

Keywords： Malware classification     Machine learning     n-gram     Gray-scale image     Feature extraction     Malware detection    

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Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial Special Feature on Intelligent Design

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 12,   Pages 1632-1643 doi: 10.1631/FITEE.1900399

Abstract: We propose a hybrid system with porcelain style identification and image recreation modules.Case studies on image creation indicate that the proposed system has the potential to engage the community

Keywords： Cantonese porcelain     Classification     Generative adversarial network     Creative arts    

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Automatic Visual Leakage Detection and Localization from Pipelines in Chemical Process Plants Using Machine Vision Techniques Reiew

Mina Fahimipirehgalin, Emanuel Trunzer, Matthias Odenweller, Birgit Vogel-Heuser

Engineering 2021, Volume 7, Issue 6,   Pages 758-776 doi: 10.1016/j.eng.2020.08.026

Abstract:

Liquid leakage from pipelines is a critical issue in large-scale process plants. Damage in pipelines affects the normal operation of the plant and increases maintenance costs. Furthermore, it causes unsafe and hazardous situations for operators. Therefore, the detection and localization of leakages is a crucial task for maintenance and condition monitoring. Recently, the use of infrared (IR) cameras was found to be a promising approach for leakage detection in large-scale plants. IR cameras can capture leaking liquid if it has a higher (or lower) temperature than its surroundings. In this paper, a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant. Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid, it is applicable for any type of liquid leakage (i.e., water, oil, etc.). In this method, subsequent frames are subtracted and divided into blocks. Then, principle component analysis is performed in each block to extract features from the blocks. All subtracted frames within the blocks are individually transferred to feature vectors, which are used as a basis for classifying the blocks. The k-nearest neighbor algorithm is used to classify the blocks as normal (without leakage) or anomalous (with leakage). Finally, the positions of the leakages are determined in each anomalous block. In order to evaluate the approach, two datasets with two different formats, consisting of video footage of a laboratory demonstrator plant captured by an IR camera, are considered. The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos. The proposed method has high accuracy and a reasonable detection time for leakage detection. The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.

Keywords： Leakage detection and localization     Image analysis     Image pre-processing     Principle component analysis     k-nearest neighbor classification    

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Laplacian sparse dictionary learning for image classification based on sparse representation Article

Fang LI, Jia SHENG, San-yuan ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1795-1805 doi: 10.1631/FITEE.1600039

Abstract: The learned LSD can be easily integrated into a classification framework based on sparse representationWe compare the proposed method with other methods using three benchmark-controlled face image databases, Extended Yale B, ORL, and AR, and one uncontrolled person image dataset, i-LIDS-MA.show the advantages of the proposed LSD algorithm over state-of-the-art sparse representation based classification

Keywords： Sparse representation     Laplacian regularizer     Dictionary learning     Double sparsity     Manifold    

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Turbidity-adaptive underwater image enhancement method using image fusion

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-021-0669-8

Abstract: In this paper, we propose a turbidity-adaptive underwater image enhancement method.Based on the detection result, different image enhancement strategies are designed to deal with the problemThe proposed method is verified by an underwater image dataset captured in real underwater environmentThe result is evaluated by image metrics including structure similarity index measure, underwater colorimage quality evaluation metric, and speeded-up robust features.

Keywords： turbidity     underwater image enhancement     image fusion     underwater robots     visibility    

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Development of a new method for RMR and Q classification method to optimize support system in tunneling

Asghar RAHMATI,Lohrasb FARAMARZI,Manouchehr SANEI

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 4,   Pages 448-455 doi: 10.1007/s11709-014-0262-x

Abstract: Rock mass classification system is very suitable for various engineering design and stability analysisclassification method is confirmed by Japan Highway Public Corporation that this method can figure outThese equations as a new method were able to optimize the support system for and classification systemsFrom classification and its application in these case studies, it is pointed out that the methodfor the design of support systems in underground working is more reliable than the and classification

Keywords： JH classification     Q and RMR classification     new method    

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Molecular classification and precision therapy of cancer: immune checkpoint inhibitors

Yingyan Yu

Frontiers of Medicine 2018, Volume 12, Issue 2,   Pages 229-235 doi: 10.1007/s11684-017-0581-0

Abstract: Taking gastric cancer as an example, its molecular classification is built on genome abnormalities, butSubsequently, by using their findings, oncologists will carry out targeted therapy based on molecular classification

Keywords： molecular classification     precision medicine     pembrolizumab     PD-1/PD-L1     MSI-H    

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Gradient-based compressive image fusion

Yang CHEN,Zheng QIN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 3,   Pages 227-237 doi: 10.1631/FITEE.1400217

Abstract: We present a novel image fusion scheme based on gradient and scrambled block Hadamard ensemble (SBHE)In the fusion phase, the image gradient is calculated to reflect the abundance of its contour informationBy compositing the gradient of each image, gradient-based weights are obtained, with which compressiveFinally, inverse transformation is applied to the coefficients derived from fusion, and the fused imageIn addition, different image fusion application scenarios are applied to explore the scenario adaptability

Keywords： Compressive sensing (CS)     Image fusion     Gradient-based image fusion     CS-based image fusion    

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Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900116

Abstract: In a typical classification task, the classification accuracy is strongly related to the features that

Keywords： 自动编码器；图像分类；半监督学习；神经网络    

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Edge detection of steel plates at high temperature using image measurement

Qiong Zhou, Qi An

Frontiers of Mechanical Engineering 2009, Volume 4, Issue 1,   Pages 77-82 doi: 10.1007/s11465-009-0013-1

Abstract: An edge detection method for the measurement of steel plate’s thermal expansion is proposed in this paper, where the shrinkage of a steel plate is measured when temperature drops. First, images are picked up by an imaging system; a method of regional edge detection based on grayscales’ sudden change is then applied to detect the edges of the steel plate; finally, pixel coordinates of the edge position are transformed to physical coordinates through calibration parameters. The experiment shows that the real-time, high precision, and non-contact measurement of the steel plate’s edge position under high temperature can be realized using the imaging measurement method established in this paper.

Keywords： thermal expansion     image measurement     edge detection     image calibration    

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Two-level hierarchical feature learning for image classification Article

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9,   Pages 897-906 doi: 10.1631/FITEE.1500346

Abstract: In some image classification tasks, similarities among different categories are different and the sampleshighly similar categories, more specific features are required so that the classifier can improve the classificationOur proposed method effectively increases the classification accuracy in comparison with flat multipleclassification methods.

Keywords： Transfer learning     Feature learning     Deep convolutional neural network     Hierarchical classification    

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An approach for mechanical fault classification based on generalized discriminant analysis

LI Wei-hua, SHI Tie-lin, YANG Shu-zi

Frontiers of Mechanical Engineering 2006, Volume 1, Issue 3,   Pages 292-298 doi: 10.1007/s11465-006-0022-2

Abstract: To deal with pattern classification of complicated mechanical faults, an approach to multi-faults classificationKPCA is good at detection of machine abnormality while GDA performs well in multi-faults classificationWhen the proposed method is applied to air compressor condition classification and gear fault classification, an excellent performance in complicated multi-faults classification is presented.

Keywords： generalized discriminant     non-separable     abnormality     classification     multi-faults classification    

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Title Author Date Type Operation

Image-based fall detection and classification of a user with a walking support system

Sajjad TAGHVAEI, Kazuhiro KOSUGE

Journal Article

Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

Journal Article

Astatistical distribution texton feature for synthetic aperture radar image classification

Chu HE, Ya-ping YE, Ling TIAN, Guo-peng YANG, Dong CHEN

Journal Article

Automatic malware classification and new malwaredetection using machine learning

Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG

Journal Article

Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Journal Article

Automatic Visual Leakage Detection and Localization from Pipelines in Chemical Process Plants Using Machine Vision Techniques

Mina Fahimipirehgalin, Emanuel Trunzer, Matthias Odenweller, Birgit Vogel-Heuser

Journal Article

Laplacian sparse dictionary learning for image classification based on sparse representation

Fang LI, Jia SHENG, San-yuan ZHANG

Journal Article

Turbidity-adaptive underwater image enhancement method using image fusion

Journal Article

Development of a new method for RMR and Q classification method to optimize support system in tunneling

Asghar RAHMATI,Lohrasb FARAMARZI,Manouchehr SANEI

Journal Article

Molecular classification and precision therapy of cancer: immune checkpoint inhibitors

Yingyan Yu

Journal Article

Gradient-based compressive image fusion

Yang CHEN,Zheng QIN

Journal Article

Representation learning via a semi-supervised stacked distance autoencoder for image classification

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Journal Article

Edge detection of steel plates at high temperature using image measurement

Qiong Zhou, Qi An

Journal Article

Two-level hierarchical feature learning for image classification

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Journal Article

An approach for mechanical fault classification based on generalized discriminant analysis

LI Wei-hua, SHI Tie-lin, YANG Shu-zi

Journal Article