Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos

Debido al incremento en los costos de mantenimiento, rehabilitación y construcción de vías, estudiar las estructuras de pavimento para determinar su comportamiento y sus características mecánicas propias analizando la distribución y posición de sus partículas, se ha vuelto un campo de gran importanc...

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Autores:: Reyes Ortiz, Oscar Javier
Mejia, Marcela
Useche Castelblanco, Juan Sebastian

Tipo de recurso:: Article of journal

Fecha de publicación:: 2019

Institución:: Universidad EIA .

Repositorio:: Repositorio EIA .

Idioma:: spa

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dc.title.spa.fl_str_mv	Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos
dc.title.translated.eng.fl_str_mv	Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos
title	Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos
spellingShingle	Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos Pavimentos Inteligencia Artificial Procesamiento Digital de Imágenes
title_short	Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos
title_full	Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos
title_fullStr	Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos
title_full_unstemmed	Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos
title_sort	Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos
dc.creator.fl_str_mv	Reyes Ortiz, Oscar Javier Mejia, Marcela Useche Castelblanco, Juan Sebastian
dc.contributor.author.spa.fl_str_mv	Reyes Ortiz, Oscar Javier Mejia, Marcela Useche Castelblanco, Juan Sebastian
dc.subject.spa.fl_str_mv	Pavimentos Inteligencia Artificial Procesamiento Digital de Imágenes
topic	Pavimentos Inteligencia Artificial Procesamiento Digital de Imágenes
description	Debido al incremento en los costos de mantenimiento, rehabilitación y construcción de vías, estudiar las estructuras de pavimento para determinar su comportamiento y sus características mecánicas propias analizando la distribución y posición de sus partículas, se ha vuelto un campo de gran importancia en la ingeniería. Las nuevas herramientas de análisis buscan hacer este estudio más eficiente reduciendo su costo y tiempo de ejecución mediante el procesamiento digital de imágenes. El procesamiento digital tradicional está limitado en su sensibilidad ante perturbaciones externas que puedan modificar la imagen, por eso se han implementado diferentes técnicas de inteligencia artificial (IA) para optimizar los algoritmos. Este trabajo presenta una revisión de las diferentes aplicaciones de técnicas de IA recientes en el procesamiento de imágenes. Después se revisan los trabajos realizados específicamente con imágenes de pavimentos y se proponen posibles aplicaciones para implementar en este campo con inteligencia artificial
publishDate	2019
dc.date.accessioned.none.fl_str_mv	2019-01-20 00:00:00 2022-06-17T20:19:48Z
dc.date.available.none.fl_str_mv	2019-01-20 00:00:00 2022-06-17T20:19:48Z
dc.date.issued.none.fl_str_mv	2019-01-20
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.eng.fl_str_mv	Journal article
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dc.relation.references.spa.fl_str_mv	Abdullah, S. & Abdolrazzagh-nezhad, M., 2014. Fuzzy job-shop scheduling problems : A review. Information Sciences, 278, pp.380–407. Available at: http://dx.doi.org/10.1016/j.ins.2014.03.060. Ali, M. et al., 2015. An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony. Information Sciences, 301, pp.44–60. Available at: http://dx.doi.org/10.1016/j.ins.2014.12.042. Apostolidis, G.K. & Hadjileontiadis, L.J., 2017. Swarm decomposition : A novel signal analysis using swarm intelligence. Signal Processing journal, 132, pp.40–50. Arjmandzadeh, Z., Safi, M. & Nazemi, A., 2017. A new neural network model for solving random interval linear programming problems. Neural Networks, 89, pp.11–18. Athertya, J.S. & Saravana Kumar, G., 2016. Automatic segmentation of vertebral contours from CT images using fuzzy corners. Computers in Biology and Medicine, 72, pp.75–89. Babashamsi, P. et al., 2016. Evaluation of pavement life cycle cost analysis : Review and analysis. International Journal of Pavement Research and Technology, 9, pp.241–254. Available at: http://dx.doi.org/10.1016/j.ijprt.2016.08.004. Banitalebi, A. et al., 2015. Enhanced compact artificial bee colony. INFORMATION SCIENCES, 298, pp.491–511. Available at: http://dx.doi.org/10.1016/j.ins.2014.12.015. Bayar, N. et al., 2015. Fault detection , diagnosis and recovery using Arti fi cial Immune Systems : A review. Engineering Applications of Artificial Intelligence, 46, pp.43–57. Berrocal, C.G.. et al., 2016. Characterisation of bending cracks in R/FRC using image analysis. submitted to: Materials and Structures, 90, pp.104–116. Bessa, I.S., Castelo Branco, V.T.F. & Soares, J.B., 2012. Evaluation of different digital image processing software for aggregates and hot mix asphalt characterizations. Construction and Building Materials, 37, pp.370–378. Bianconi, F. et al., 2015. Grain-size assessment of fine and coarse aggregates through bipolar area morphology. Machine Vision and Applications, 26(6), pp.775–789. Available at: http://link.springer.com/10.1007/s00138-015-0692-z. Bose, A. & Mali, K., 2016. Fuzzy-based artificial bee colony optimization for gray image segmentation. Signal, Image and Video Processing, 10, pp.1089–1096. Bouchet, A. et al., 2016. Fuzzy mathematical morphology for color images defined by fuzzy preference relations. Pattern Recognition, 60, pp.720–733. Cao, W. et al., 2017. A review on neural networks with random weights. Neurocomputing, 0, pp.1–10. Casti, P. et al., 2015. Analysis of structural similarity in mammograms for detection of bilateral asymmetry. IEEE Trans. Med. Imaging, 34(2), pp.662–671. Cordeiro, F.R., Santos, W.P. & Silva-Filho, A.G., 2016. A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images. Expert Systems with Applications, 65, pp.116–126. Costa, G., Matos, W. & Martinez, R., 2017. Artificial immune systems applied to fault detection and isolation : A brief review of immune response-based approaches and a case study. Applied Soft Computing Journal, 57, pp.118–131. Available at: http://dx.doi.org/10.1016/j.asoc.2017.03.031. Cristea, V., Leblebici, Y. & Almási, A., 2016. Neurocomputing Review of advances in neural networks : Neural design technology stack. Neurocomputing, 174, pp.31–41. Cui, L. et al., 2017. A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization. Information Sciences, 414, pp.53–67. Espinoza, K. et al., 2016. Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture. Computers and Electronics in Agriculture, 127, pp.495–505. Ferrari, A., Lombardi, S. & Signoroni, A., 2017. Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging. Pattern Recognition journal, 61, pp.629–640. Galdámez, P.L., Raveane, W. & Arrieta, A.G., 2017. A brief review of the ear recognition process using deep neural networks. Journal of Applied Logic, 24, pp.62–70. Available at: http://dx.doi.org/10.1016/j.jal.2016.11.014. Gong, T. et al., 2016. Magnetic resonance imaging-clonal selection algorithm : An intelligent adaptive enhancement of brain image with an improved immune algorithm. Engineering Applications of Artificial Intelligence, (October). Han, J. et al., 2016. 2D image analysis method for evaluating coarse aggregate characteristic and distribution in concrete. Construction and Building Materials, 127, pp.30–42. Hatata, A.Y. & Sedhom, B.E., 2017. Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system. Alexandria Engineering Journal. Hekayati, J. & Rahimpour, M.R., 2017. Estimation of the saturation pressure of pure ionic liquids using MLP arti fi cial neural networks and the revised isofugacity criterion. Journal of Molecular Liquids journal, 230, pp.85–95. Helena, G., Miranda, B. & Cezar, J., 2015. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. , 64, pp.334–346. Hu, L. et al., 2016. Effect of three-dimensional macrotexture characteristics on dynamic frictional coefficient of asphalt pavement surface. Construction and Building Materials, 126, pp.720–729. Huang, C., Li, H. & Li, W., 2017. Store classification using Text-Exemplar-Similarity and Hypotheses- Weighted-CNN. J. Vis. Commun. Image R. journal, 44, pp.21–28. Hugo, V. et al., 2009. Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images. NDT & E International, 42, pp.644–651. Islam, S. et al., 2014. Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier. Waste Management, 34, pp.281–290. Jani, D.B., Mishra, M. & Sahoo, P.K., 2017. Application of arti fi cial neural network for predicting performance of solid desiccant cooling systems – A review. Renewable and Sustainable Energy Reviews, 80(November 2016), pp.352–366. Available at: http://dx.doi.org/10.1016/j.rser.2017.05.169. Jiang, J. et al., 2017. Effect of the contact structure characteristics on rutting performance in asphalt mixtures using 2D imaging analysis. Construction and Building Materials, 136, pp.426–435. Knabben, R.M. et al., 2016. Evaluation of sound absorption capacity of asphalt mixtures. Applied Acoustics, 114, pp.266–274. Available at: http://dx.doi.org/10.1016/j.apacoust.2016.08.008. Kumar, D. & Mishra, K.K., 2017. Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm. Swarm and Evolutionary Computation journal, 33(April 2016), pp.119–130. Kuo, R.., Tseng, Y.. & Yao Chen, Z., 2016. Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data. Journal of Intelligent Manufacturing, 27, pp.1191–1207. Li, B. et al., 2016. Optik Image retrieval via balance-evolution artificial bee colony algorithm and lateral inhibition. , 127, pp.11775–11785. Li, Q. et al., 2011. FoSA: F* Seed-growing Approach for crack- line detection from pavement images. Image and Vision Computing, 29(12), pp.861–872. Lim, C.H., Vats, E. & Seng, C., 2015. Fuzzy human motion analysis : A review. Pattern Recognition journal, 48, pp.1773–1796. Magna, G. et al., 2015. Adaptive classification model based on artificial immune system for breast cancer detection. Proceedings of the Eighteenth AISEM Annual Conference. Magna, G. et al., 2016. Identification of mammography anomalies for breast cancer detection by an ensemble of classification models based on artificial immune system. Knowle dge-Based Systems, 101, pp.60–70. Mashaly, A.F. & Alazba, A.A., 2016. MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment. Computers and Electronics in Agriculture, 122, pp.146–155. Mavrovouniotis, M., Li, C. & Yang, S., 2017. A survey of swarm intelligence for dynamic optimization : Algorithms and applications. Swarm and Evolutionary Computation journal, 33(January), pp.1–17. Mejía, M. & Alzate, M., 2015. Clasificación automática de formas patológicas de eritrocitos humanos Automatic classification of pathological shapes in human erythrocytes. Revista Ingeniería, 21(1), pp.31–48. Michalska-Po??oga, I. et al., 2016. Towards the usage of image analysis technique to measure particles size and composition in wood-polymer composites. Industrial Crops and Products, 92, pp.149–156. 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spelling	Reyes Ortiz, Oscar Javierd8e830a17d718597df75c6d3e8601ed9300Mejia, Marcela2363cd6c386c4c0b89604f26d77fe213300Useche Castelblanco, Juan Sebastian89a25bf9c71fda446cabd57a476017cd3002019-01-20 00:00:002022-06-17T20:19:48Z2019-01-20 00:00:002022-06-17T20:19:48Z2019-01-201794-1237https://repository.eia.edu.co/handle/11190/503410.24050/reia.v16i31.12152463-0950https://doi.org/10.24050/reia.v16i31.1215Debido al incremento en los costos de mantenimiento, rehabilitación y construcción de vías, estudiar las estructuras de pavimento para determinar su comportamiento y sus características mecánicas propias analizando la distribución y posición de sus partículas, se ha vuelto un campo de gran importancia en la ingeniería. Las nuevas herramientas de análisis buscan hacer este estudio más eficiente reduciendo su costo y tiempo de ejecución mediante el procesamiento digital de imágenes. El procesamiento digital tradicional está limitado en su sensibilidad ante perturbaciones externas que puedan modificar la imagen, por eso se han implementado diferentes técnicas de inteligencia artificial (IA) para optimizar los algoritmos. Este trabajo presenta una revisión de las diferentes aplicaciones de técnicas de IA recientes en el procesamiento de imágenes. Después se revisan los trabajos realizados específicamente con imágenes de pavimentos y se proponen posibles aplicaciones para implementar en este campo con inteligencia artificialDebido al incremento en los costos de mantenimiento, rehabilitación y construcción de vías, estudiar las estructuras de pavimento para determinar su comportamiento y sus características mecánicas propias analizando la distribución y posición de sus partículas, se ha vuelto un campo de gran importancia en la ingeniería. Las nuevas herramientas de análisis buscan hacer este estudio más eficiente reduciendo su costo y tiempo de ejecución mediante el procesamiento digital de imágenes. El procesamiento digital tradicional está limitado en su sensibilidad ante perturbaciones externas que puedan modificar la imagen, por eso se han implementado diferentes técnicas de inteligencia artificial (IA) para optimizar los algoritmos. Este trabajo presenta una revisión de las diferentes aplicaciones de técnicas de IA recientes en el procesamiento de imágenes. Después se revisan los trabajos realizados específicamente con imágenes de pavimentos y se proponen posibles aplicaciones para implementar en este campo con inteligencia artificialapplication/pdfspaFondo Editorial EIA - Universidad EIARevista EIA - 2019https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://revistas.eia.edu.co/index.php/reveia/article/view/1215PavimentosInteligencia ArtificialProcesamiento Digital de ImágenesTécnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentosTécnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentosArtículo de revistaJournal articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARTREFhttp://purl.org/coar/version/c_970fb48d4fbd8a85Abdullah, S. & Abdolrazzagh-nezhad, M., 2014. Fuzzy job-shop scheduling problems : A review. Information Sciences, 278, pp.380–407. Available at: http://dx.doi.org/10.1016/j.ins.2014.03.060.Ali, M. et al., 2015. An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony. Information Sciences, 301, pp.44–60. Available at: http://dx.doi.org/10.1016/j.ins.2014.12.042.Apostolidis, G.K. & Hadjileontiadis, L.J., 2017. Swarm decomposition : A novel signal analysis using swarm intelligence. Signal Processing journal, 132, pp.40–50.Arjmandzadeh, Z., Safi, M. & Nazemi, A., 2017. A new neural network model for solving random interval linear programming problems. Neural Networks, 89, pp.11–18.Athertya, J.S. & Saravana Kumar, G., 2016. Automatic segmentation of vertebral contours from CT images using fuzzy corners. Computers in Biology and Medicine, 72, pp.75–89.Babashamsi, P. et al., 2016. Evaluation of pavement life cycle cost analysis : Review and analysis. International Journal of Pavement Research and Technology, 9, pp.241–254. Available at: http://dx.doi.org/10.1016/j.ijprt.2016.08.004.Banitalebi, A. et al., 2015. Enhanced compact artificial bee colony. INFORMATION SCIENCES, 298, pp.491–511. Available at: http://dx.doi.org/10.1016/j.ins.2014.12.015.Bayar, N. et al., 2015. Fault detection , diagnosis and recovery using Arti fi cial Immune Systems : A review. Engineering Applications of Artificial Intelligence, 46, pp.43–57.Berrocal, C.G.. et al., 2016. Characterisation of bending cracks in R/FRC using image analysis. submitted to: Materials and Structures, 90, pp.104–116.Bessa, I.S., Castelo Branco, V.T.F. & Soares, J.B., 2012. Evaluation of different digital image processing software for aggregates and hot mix asphalt characterizations. Construction and Building Materials, 37, pp.370–378.Bianconi, F. et al., 2015. Grain-size assessment of fine and coarse aggregates through bipolar area morphology. Machine Vision and Applications, 26(6), pp.775–789. Available at: http://link.springer.com/10.1007/s00138-015-0692-z.Bose, A. & Mali, K., 2016. Fuzzy-based artificial bee colony optimization for gray image segmentation. Signal, Image and Video Processing, 10, pp.1089–1096.Bouchet, A. et al., 2016. Fuzzy mathematical morphology for color images defined by fuzzy preference relations. Pattern Recognition, 60, pp.720–733.Cao, W. et al., 2017. A review on neural networks with random weights. Neurocomputing, 0, pp.1–10.Casti, P. et al., 2015. Analysis of structural similarity in mammograms for detection of bilateral asymmetry. IEEE Trans. Med. Imaging, 34(2), pp.662–671.Cordeiro, F.R., Santos, W.P. & Silva-Filho, A.G., 2016. A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images. Expert Systems with Applications, 65, pp.116–126.Costa, G., Matos, W. & Martinez, R., 2017. Artificial immune systems applied to fault detection and isolation : A brief review of immune response-based approaches and a case study. Applied Soft Computing Journal, 57, pp.118–131. Available at: http://dx.doi.org/10.1016/j.asoc.2017.03.031.Cristea, V., Leblebici, Y. & Almási, A., 2016. Neurocomputing Review of advances in neural networks : Neural design technology stack. Neurocomputing, 174, pp.31–41.Cui, L. et al., 2017. A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization. Information Sciences, 414, pp.53–67.Espinoza, K. et al., 2016. Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture. Computers and Electronics in Agriculture, 127, pp.495–505.Ferrari, A., Lombardi, S. & Signoroni, A., 2017. Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging. Pattern Recognition journal, 61, pp.629–640.Galdámez, P.L., Raveane, W. & Arrieta, A.G., 2017. A brief review of the ear recognition process using deep neural networks. Journal of Applied Logic, 24, pp.62–70. Available at: http://dx.doi.org/10.1016/j.jal.2016.11.014.Gong, T. et al., 2016. Magnetic resonance imaging-clonal selection algorithm : An intelligent adaptive enhancement of brain image with an improved immune algorithm. Engineering Applications of Artificial Intelligence, (October).Han, J. et al., 2016. 2D image analysis method for evaluating coarse aggregate characteristic and distribution in concrete. Construction and Building Materials, 127, pp.30–42.Hatata, A.Y. & Sedhom, B.E., 2017. Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system. Alexandria Engineering Journal.Hekayati, J. & Rahimpour, M.R., 2017. 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Pattern Recognition journal, 59, pp.176–187.https://revistas.eia.edu.co/index.php/reveia/article/download/1215/1229Núm. 31 , Año 20192073118916Revista EIAPublicationOREORE.xmltext/xml2731https://repository.eia.edu.co/bitstreams/7653075f-089f-4c82-94d9-24aac6ca03c2/downloadee89bbb117c5b87fd6f3b6007eac757eMD5111190/5034oai:repository.eia.edu.co:11190/50342023-07-25 16:50:29.491https://creativecommons.org/licenses/by-nc-sa/4.0/Revista EIA - 2019metadata.onlyhttps://repository.eia.edu.coRepositorio Institucional Universidad EIAbdigital@metabiblioteca.com

Técnicas de inteligencia artificial utilizadas en el procesamiento de imágenes y su aplicación en el análisis de pavimentos

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