Desarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea.
Dendrochronology has been a tool of great importance when it comes to ecological studies and has allowed the study of climate and forests around the world. However, this technique was originally developed in temperate zones, which resulted in the bias that rings only occur in zones with seasons. For...
- Autores:
-
Sánchez Aguiar, Andrés Felipe
- Tipo de recurso:
- Work document
- Fecha de publicación:
- 2019
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/75549
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/75549
- Palabra clave:
- Ingeniería y operaciones afines
Dendrochronology, Computer vision, Deep learning, Apeiba membranacea, Segmentation.
Dendrocronología, Visión por computador, Apeiba membranácea, Aprendizaje profundo, Segmentación
- Rights
- openAccess
- License
- Atribución-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Desarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea. |
| title |
Desarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea. |
| spellingShingle |
Desarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea. Ingeniería y operaciones afines Dendrochronology, Computer vision, Deep learning, Apeiba membranacea, Segmentation. Dendrocronología, Visión por computador, Apeiba membranácea, Aprendizaje profundo, Segmentación |
| title_short |
Desarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea. |
| title_full |
Desarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea. |
| title_fullStr |
Desarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea. |
| title_full_unstemmed |
Desarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea. |
| title_sort |
Desarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea. |
| dc.creator.fl_str_mv |
Sánchez Aguiar, Andrés Felipe |
| dc.contributor.advisor.spa.fl_str_mv |
Espinosa-Bedoya, Albeiro |
| dc.contributor.author.spa.fl_str_mv |
Sánchez Aguiar, Andrés Felipe |
| dc.contributor.researchgroup.spa.fl_str_mv |
GIDIA: Grupo de Investigación y Desarrollo en Inteligencia Artificial |
| dc.subject.ddc.spa.fl_str_mv |
Ingeniería y operaciones afines |
| topic |
Ingeniería y operaciones afines Dendrochronology, Computer vision, Deep learning, Apeiba membranacea, Segmentation. Dendrocronología, Visión por computador, Apeiba membranácea, Aprendizaje profundo, Segmentación |
| dc.subject.proposal.eng.fl_str_mv |
Dendrochronology, Computer vision, Deep learning, Apeiba membranacea, Segmentation. |
| dc.subject.proposal.spa.fl_str_mv |
Dendrocronología, Visión por computador, Apeiba membranácea, Aprendizaje profundo, Segmentación |
| description |
Dendrochronology has been a tool of great importance when it comes to ecological studies and has allowed the study of climate and forests around the world. However, this technique was originally developed in temperate zones, which resulted in the bias that rings only occur in zones with seasons. For this reason, studies of growth rings in the tropics are minimal compared to studies outside of it. Due to the difference in the anatomical characteristics of the species within the tropics, it is necessary to create tools focused on these species that allow a greater development of dendrochronology in the tropics. Thus, the development of a method that allows segmenting the growth rings in the Apeiba membranacea species from computer vision techniques is proposed. The process begins by analyzing how to develop the acquisition of the images, finding that the best option for this is by scanning the images at a resolution of 1200 PPP, then the color spaces of these images were evaluated by experts criteria finding that the channels based on intensity are those that best reflect the anatomical characteristics, especially the RGB space, which presents with different levels the anatomical characteristics in each of its channels. Subsequently, different segmentation techniques were analyzed and it was found that the most appropriate is the use of a Ternausnet architecture in different batches weighing the final result. When validating the results against hand segmented images, a Jaccard index of 0.75, an accuracy of 0.85, a sensitivity of 0.85 and a specificity of 0.88 were obtained, concluding that the most appropriate way to address this problem is through the use of different models, trained based on daca one of the anatomical characteristics of the species. |
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2019 |
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2019-12-02 |
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2020-01-31T19:57:28Z |
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2020-01-31T19:57:28Z |
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Documento de trabajo |
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info:eu-repo/semantics/workingPaper |
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info:eu-repo/semantics/acceptedVersion |
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Kumar Patel, K., Kar, A., Jha, S. N., & Khan, M. A. (2012). Machine vision system: a tool for quality inspection of food and agricultural products. Journal of Food Science and Technology, 123–14. Montana, G., & Brebisson , A. (2015). Deep Neural Networks for Anatomical Brain Segmentation. The IEEE Conference on Computer Vision and Pattern Recognition Borianne, P., Pernaudat, R., & Subsol, G. (2011). Automated delineation of tree-rings in X-Ray Computed Tomography images of wood. IEEE. Cerda, M., Hitschfeld-Kahler, N., & Mery, D. (2007). Robust Tree-Ring Detection. PacificRim Symposium on Image and Video Technology. Conner, W., Schowengerdt, R., Munro, M., & Hughes, M. (1998). Design of a computer vision based tree ring dating system. 1998 IEEE Southwest Symposium on Image Analysis and Interpretation, 256-261. Dendroecological investigations on Swietenia. (2003). Trees 17, 244-250 Derganc, J., Likar, B., Tomaževič, D., & Pernuš, F. (2003). Real-time automated visual inspection of color tablets in pharmaceutical blisters. Real-Time Imaging, 113-124. Dünisch, O., Montóia, V., & Bauch, J. (2003). Dendroecological investigations on Swietenia. Trees 17, 244–250. Entacher, K., & Planitzer, D. (2007). Towards an automated generation of tree ring. Proceedings of the 5th International Symposium on, 174–179. Fabijańska, A., Barniak, J., Danek, M., & Piórkowski, A. (2017). A Comparative Study of Image Enhancement Methods in Tree-Ring Analysis. researchgate. Fichtler, E., & Worbes, M. (2010). Wood anatomy and tree-ring structure and their importance for tropical dendrochronology. Amazonian Floodplain Forests: Ecophysiology, Biodiversity and Sustainable Management, 329-346. Henke, M., & Sloboda, B. (2014). Semiautomatic tree ring segmentation using Active Contours and an optimised gradient operator. Central European Forestry Journal. Kamil, R., Malik, A. S., Thong, C.-M., & Mohd Hani, A. F. (2012). A review of SMD-PCB defects and detection algorithms. Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies. Kennel, P., Borianne, P., & Subsol, G. (2015). An automated method for tree-ring delineation based on active contours guided by DT-CWT complex coefficients in photographic images: Application to Abies alba wood slice images. Lara, W., Bravo, F., & Sierra, C. (2015). measuRing: An R package to measure tree-ring widths from scanned images. Dendrochronologia, 43-50 Locosselli, G., Krottenthaler, S., Pitsch, P., Anhuf, D., & Ceccantini, G. (2017). Age and growth rate of congeneric tree species (Hymenaea spp. - Leguminosae) inhabiting different tropical biomes. erdkunde, 45-57. Mainieri, C., & Chimelo, J. (1989). Fichas das características das principais madeiras brasileiras. Sao paolo: Instituto de Pesquisas Tecnológicas (IPT. Maioli Barbosa, A. C., Pereira, G. A., Granato-Souza, D., Santos , R. M., & Leite Fontes, M. A. (2018). Tree rings and growth trajectories of tree species from seasonally dry tropical fores. Australian journal of botany, 414-42. Mery, D., & Carrasco, M. (2006). Advances on Automated Multiple View Inspection. Pacific-Rim Symposium on Image and Video Technology, 513-522. Ngan, H., Pang, G., & Yung, N. (2011). Review article: Automated fabric defect detectionA review. Image and Vision Computing, 442-458 . Pons, T., & Helle, G. (2011). Identification of anatomically non-distinct annual rings in tropical trees using stable isotopes. Springer-Verlag. Ronneberger, O., Fischer, P, & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 234–241 Sheppard, P., & Graumlich, L. (1994). A reflected-light video imaging system for tree-ring analysis of conifers. Tree rings, environment and humanity. Radiocarbon 1996. Proceedings of the International conference, 17-21. Sioma, A., & Socha, J. (2016). Automation of annual tree increment measurement using vision system. Drewno 59, 19–30. Sundari, P. M., Brito, S., & Kumar, R. (2014). An Approach for Dendroclimatology Using Image Processing Techniques. 2014 World Congress on Computing and Communication Technologies, 234-236. Therrell, M., Stahle, D., Mukelabai, M., & Shugart, H. (2007). Age, and radial growth dynamics of Pterocarpus angolensis in southern Africa. forest ecology and managment, 24-31. Timm, F., & Barth, E. (2012). Novelty detection for the inspection of light-emitting diodes. Expert Systems with Applications. Worbes, M. (1989). Growth rings, increment and age of trees in inundation forests, savanna and a mountain forest in the neotropics. IAWA, 109–122. Xu, k., Wang, X., An, H., Sun, H., Han, w., & Li , Q. (2017). Tree-ring widths are good proxies of annual variation in forest productivity in temperate forests. Scientific Reports. Zhou, H., Feng, R., Huang, H., Lin, E., & Yu, J. (2012). Method of tree-ring image analysis for dendrochronology. Optical Engineering. |
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Atribución-SinDerivadas 4.0 InternacionalAtribución-SinDerivadas 4.0 InternacionalDerechos reservados - Universidad Nacional de ColombiaAcceso abiertohttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Espinosa-Bedoya, Albeiro13ed2b7f-0ff6-4c66-961c-a12ee48c09a4-1Sánchez Aguiar, Andrés Felipea055b075-729f-4f32-845d-2bb64d29643aGIDIA: Grupo de Investigación y Desarrollo en Inteligencia Artificial2020-01-31T19:57:28Z2020-01-31T19:57:28Z2019-12-02https://repositorio.unal.edu.co/handle/unal/75549Dendrochronology has been a tool of great importance when it comes to ecological studies and has allowed the study of climate and forests around the world. However, this technique was originally developed in temperate zones, which resulted in the bias that rings only occur in zones with seasons. For this reason, studies of growth rings in the tropics are minimal compared to studies outside of it. Due to the difference in the anatomical characteristics of the species within the tropics, it is necessary to create tools focused on these species that allow a greater development of dendrochronology in the tropics. Thus, the development of a method that allows segmenting the growth rings in the Apeiba membranacea species from computer vision techniques is proposed. The process begins by analyzing how to develop the acquisition of the images, finding that the best option for this is by scanning the images at a resolution of 1200 PPP, then the color spaces of these images were evaluated by experts criteria finding that the channels based on intensity are those that best reflect the anatomical characteristics, especially the RGB space, which presents with different levels the anatomical characteristics in each of its channels. Subsequently, different segmentation techniques were analyzed and it was found that the most appropriate is the use of a Ternausnet architecture in different batches weighing the final result. When validating the results against hand segmented images, a Jaccard index of 0.75, an accuracy of 0.85, a sensitivity of 0.85 and a specificity of 0.88 were obtained, concluding that the most appropriate way to address this problem is through the use of different models, trained based on daca one of the anatomical characteristics of the species.La dendrocronología ha sido una herramienta de gran importancia a la hora de hacer estudios ecológicos y ha permitido el estudio del clima y los bosques alrededor del mundo. Sin embargo, esta técnica se desarrolló originalmente en las zonas templadas, lo que resultó en el sesgo de que los anillos solo se presentan en las zonas con estaciones. Por tal razón, los estudios de anillos de crecimiento en el trópico son mínimos en comparación a los estudios fuera de él. Por la diferencia en las características anatómicas de las especies dentro del trópico se ve necesaria la creación de herramientas enfocadas en estas especies que permitan un mayor desarrollo de la dendrocronología en el trópico. Así, se propone el desarrollo de un método que permita segmentar los anillos de crecimiento en la especie Apeiba membranácea a partir de técnicas de visión por computador. El proceso se inicia analizando cómo desarrollar la adquisición de las imágenes, encontrando que la mejor opción para esto es escaneando las imágenes a una resolución de 1200 PPP, posteriormente se evaluó mediante el criterio de expertos los espacios de color de estas imágenes encontrando que los canales basados en intensidad son los que mejor reflejan las características anatómicas, en especial el espacio RGB, que presenta con diferentes niveles las características anatómicas en cada uno de sus canales. Posteriormente, se analizaron diferentes técnicas de segmentación y se encontró que la más adecuada es el uso de una arquitectura Ternausnet en diferentes lotes ponderando el resultado final. Al validar los resultados contra las imágenes segmentadas a mano, se obtuvo un índice de Jaccard de 0.75, una exactitud de 0.85, una sensibilidad de 0.85 y una especificidad de 0.88, concluyendo que la forma más adecuada de abordar este problema es mediante el uso de diferentes modelos, entrenados con base a daca una de las características anatómicas de la especie.application/pdfspaIngeniería y operaciones afinesDendrochronology, Computer vision, Deep learning, Apeiba membranacea, Segmentation.Dendrocronología, Visión por computador, Apeiba membranácea, Aprendizaje profundo, SegmentaciónDesarrollo de un método basado en visión por computador para segmentar imágenes de los anillos de crecimiento en la especie Apeiba membranácea.Documento de trabajoinfo:eu-repo/semantics/workingPaperinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_8042Texthttp://purl.org/redcol/resource_type/WPUniversidad Nacional de Colombia - Sede MedellínUniversidad Nacional de Colombia - Sede MedellínKumar Patel, K., Kar, A., Jha, S. N., & Khan, M. A. (2012). Machine vision system: a tool for quality inspection of food and agricultural products. Journal of Food Science and Technology, 123–14.Montana, G., & Brebisson , A. (2015). Deep Neural Networks for Anatomical Brain Segmentation. The IEEE Conference on Computer Vision and Pattern RecognitionBorianne, P., Pernaudat, R., & Subsol, G. (2011). Automated delineation of tree-rings in X-Ray Computed Tomography images of wood. IEEE.Cerda, M., Hitschfeld-Kahler, N., & Mery, D. (2007). Robust Tree-Ring Detection. PacificRim Symposium on Image and Video Technology.Conner, W., Schowengerdt, R., Munro, M., & Hughes, M. (1998). Design of a computer vision based tree ring dating system. 1998 IEEE Southwest Symposium on Image Analysis and Interpretation, 256-261.Dendroecological investigations on Swietenia. (2003). Trees 17, 244-250Derganc, J., Likar, B., Tomaževič, D., & Pernuš, F. (2003). Real-time automated visual inspection of color tablets in pharmaceutical blisters. Real-Time Imaging, 113-124.Dünisch, O., Montóia, V., & Bauch, J. (2003). Dendroecological investigations on Swietenia. Trees 17, 244–250.Entacher, K., & Planitzer, D. (2007). Towards an automated generation of tree ring. Proceedings of the 5th International Symposium on, 174–179.Fabijańska, A., Barniak, J., Danek, M., & Piórkowski, A. (2017). A Comparative Study of Image Enhancement Methods in Tree-Ring Analysis. researchgate.Fichtler, E., & Worbes, M. (2010). Wood anatomy and tree-ring structure and their importance for tropical dendrochronology. Amazonian Floodplain Forests: Ecophysiology, Biodiversity and Sustainable Management, 329-346.Henke, M., & Sloboda, B. (2014). Semiautomatic tree ring segmentation using Active Contours and an optimised gradient operator. Central European Forestry Journal.Kamil, R., Malik, A. S., Thong, C.-M., & Mohd Hani, A. F. (2012). A review of SMD-PCB defects and detection algorithms. Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies.Kennel, P., Borianne, P., & Subsol, G. (2015). An automated method for tree-ring delineation based on active contours guided by DT-CWT complex coefficients in photographic images: Application to Abies alba wood slice images.Lara, W., Bravo, F., & Sierra, C. (2015). measuRing: An R package to measure tree-ring widths from scanned images. Dendrochronologia, 43-50Locosselli, G., Krottenthaler, S., Pitsch, P., Anhuf, D., & Ceccantini, G. (2017). Age and growth rate of congeneric tree species (Hymenaea spp. - Leguminosae) inhabiting different tropical biomes. erdkunde, 45-57.Mainieri, C., & Chimelo, J. (1989). Fichas das características das principais madeiras brasileiras. Sao paolo: Instituto de Pesquisas Tecnológicas (IPT.Maioli Barbosa, A. C., Pereira, G. A., Granato-Souza, D., Santos , R. M., & Leite Fontes, M. A. (2018). Tree rings and growth trajectories of tree species from seasonally dry tropical fores. Australian journal of botany, 414-42.Mery, D., & Carrasco, M. (2006). Advances on Automated Multiple View Inspection. Pacific-Rim Symposium on Image and Video Technology, 513-522.Ngan, H., Pang, G., & Yung, N. (2011). Review article: Automated fabric defect detectionA review. Image and Vision Computing, 442-458 .Pons, T., & Helle, G. (2011). Identification of anatomically non-distinct annual rings in tropical trees using stable isotopes. Springer-Verlag.Ronneberger, O., Fischer, P, & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 234–241Sheppard, P., & Graumlich, L. (1994). A reflected-light video imaging system for tree-ring analysis of conifers. Tree rings, environment and humanity. Radiocarbon 1996. Proceedings of the International conference, 17-21.Sioma, A., & Socha, J. (2016). Automation of annual tree increment measurement using vision system. Drewno 59, 19–30.Sundari, P. M., Brito, S., & Kumar, R. (2014). An Approach for Dendroclimatology Using Image Processing Techniques. 2014 World Congress on Computing and Communication Technologies, 234-236.Therrell, M., Stahle, D., Mukelabai, M., & Shugart, H. (2007). Age, and radial growth dynamics of Pterocarpus angolensis in southern Africa. forest ecology and managment, 24-31.Timm, F., & Barth, E. (2012). Novelty detection for the inspection of light-emitting diodes. Expert Systems with Applications.Worbes, M. (1989). Growth rings, increment and age of trees in inundation forests, savanna and a mountain forest in the neotropics. IAWA, 109–122.Xu, k., Wang, X., An, H., Sun, H., Han, w., & Li , Q. (2017). Tree-ring widths are good proxies of annual variation in forest productivity in temperate forests. Scientific Reports.Zhou, H., Feng, R., Huang, H., Lin, E., & Yu, J. (2012). Method of tree-ring image analysis for dendrochronology. Optical Engineering.ORIGINAL1037629866.2019.pdf1037629866.2019.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemasapplication/pdf2419622https://repositorio.unal.edu.co/bitstream/unal/75549/4/1037629866.2019.pdf5c06d26a29b21abd621f1bb7fd10dee0MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-83991https://repositorio.unal.edu.co/bitstream/unal/75549/5/license.txt6f3f13b02594d02ad110b3ad534cd5dfMD55CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8799https://repositorio.unal.edu.co/bitstream/unal/75549/6/license_rdff7d494f61e544413a13e6ba1da2089cdMD56THUMBNAIL1037629866.2019.pdf.jpg1037629866.2019.pdf.jpgGenerated Thumbnailimage/jpeg4813https://repositorio.unal.edu.co/bitstream/unal/75549/7/1037629866.2019.pdf.jpgad2b01d3e3c597bf3ad26d2ab5475641MD57unal/75549oai:repositorio.unal.edu.co:unal/755492023-10-13 10:21:29.725Repositorio Institucional Universidad Nacional de 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