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Extraction of Sparse Features of Color Images in Recognizing Objects
Bui, T. T. Q.,Vu, T. T.,Hong, K.-S. Korean Institute of Electrical Engineers 2016 International Journal of Control, Automation, and Vol.14 No.2
<P>In this paper, we propose a new object recognition framework that combines Gabor energy filters, a visual cortex model in which single units alternate with complex units, and color information. Each color image is first converted to the CIELAB color space. Rather using Gabor filters in the first layer of the cortex model, to each color component, a set of Gabor energy filters is applied. Thereafter, the superposition responses of the Gabor energy filter outputs over the color components are normalized by divisive normalization. In the fourth layer, sparse features are calculated using a localized pooling method that allows retention of some geometric information from the prototype patches' positions. Finally, a set of sparse features are exploited by a linear SVM classifier for object recognition and classification. In the learning stage, a set of prototypes is selected randomly over spatial position, spatial size, and several scales simultaneously, and is extracted by the local maximum over scales and orientations, ignoring weaker training scales and orientations. The results of experiments performed on several datasets show that the use of color information in our framework improves object recognition significantly.</P>
An efficient hybrid TLBO-PSO-ANN for fast damage identification in steel beam structures using IGA
S. Khatir,T. Khatir,D. Boutchicha,C. Le Thanh,H. Tran-Ngoc,T.Q. Bui,R. Capozucca,M. Abdel Wahab 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.25 No.5
The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (<i>nMSEDI</i>) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using <i>nMSEDI</i> to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from <i>nMSEDI</i> are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.
Comparison of various image fusion methods for impervious surface classification from VNREDSat-1
Luu, Hung V.,Pham, Manh V.,Man, Chuc D.,Bui, Hung Q.,Nguyen, Thanh T.N. The International Promotion Agency of Culture Tech 2016 International Journal of Advanced Culture Technolo Vol.4 No.2
Impervious surfaces are important indicators for urban development monitoring. Accurate mapping of urban impervious surfaces with observational satellites, such as VNREDSat-1, remains challenging due to the spectral diversity not captured by an individual PAN image. In this article, five multi-resolution image fusion techniques were compared for the task of classifting urban impervious surfaces. The result shows that for VNREDSat-1 dataset, UNB and Wavelet tranformation methods are the best techniques in reserving spatial and spectral information of original MS image, respectively. However, the UNB technique gives the best results when it comes to impervious surface classification, especially in the case of shadow areas included in non-impervious surface group.
Comparison of various image fusion methods for impervious surface classification from VNREDSat-1
Hung V. Luu,Manh V. Pham,Chuc D. Man,Hung Q. Bui,Thanh T.N. Nguyen 국제문화기술진흥원 2016 International Journal of Advanced Culture Technolo Vol.4 No.2
Impervious surfaces are important indicators for urban development monitoring. Accurate mapping of urban impervious surfaces with observational satellites, such as VNREDSat-1, remains challenging due to the spectral diversity not captured by an individual PAN image. In this article, five multi-resolution image fusion techniques were compared for the task of classifting urban impervious surfaces. The result shows that for VNREDSat-1 dataset, UNB and Wavelet tranformation methods are the best techniques in reserving spatial and spectral information of original MS image, respectively. However, the UNB technique gives the best results when it comes to impervious surface classification, especially in the case of shadow areas included in non-impervious surface group.