A computational model for interpretable visual category discovery of foliar shapes
ilustraciones, diagramas
- Autores:
-
Victorino Guzmán, Jorge Enrique
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/84707
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
510 - Matemáticas::514 - Topología
570 - Biología::577 - Ecología
Hojas de las plantas
Plant Leaves
Morfología (botánica)
Anatomía vegetal
Botany - morphology
Botany - anatomy
Plant anatomy
Novel category discovery
Unsupervised categorization
Leaf shape
Contour analysis
Morphological
Image processing
Topological analysis
Image classification
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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Universidad Nacional de Colombia |
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|
| dc.title.eng.fl_str_mv |
A computational model for interpretable visual category discovery of foliar shapes |
| dc.title.translated.spa.fl_str_mv |
Modelo computacional para el descubrimiento de categorías de formas foliares |
| title |
A computational model for interpretable visual category discovery of foliar shapes |
| spellingShingle |
A computational model for interpretable visual category discovery of foliar shapes 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación 510 - Matemáticas::514 - Topología 570 - Biología::577 - Ecología Hojas de las plantas Plant Leaves Morfología (botánica) Anatomía vegetal Botany - morphology Botany - anatomy Plant anatomy Novel category discovery Unsupervised categorization Leaf shape Contour analysis Morphological Image processing Topological analysis Image classification |
| title_short |
A computational model for interpretable visual category discovery of foliar shapes |
| title_full |
A computational model for interpretable visual category discovery of foliar shapes |
| title_fullStr |
A computational model for interpretable visual category discovery of foliar shapes |
| title_full_unstemmed |
A computational model for interpretable visual category discovery of foliar shapes |
| title_sort |
A computational model for interpretable visual category discovery of foliar shapes |
| dc.creator.fl_str_mv |
Victorino Guzmán, Jorge Enrique |
| dc.contributor.advisor.none.fl_str_mv |
Gómez Jaramillo, Francisco Albeiro |
| dc.contributor.author.none.fl_str_mv |
Victorino Guzmán, Jorge Enrique |
| dc.contributor.researchgroup.spa.fl_str_mv |
COMBIOS |
| dc.contributor.orcid.spa.fl_str_mv |
Victorino, Jorge [0000-0003-3331-4340] |
| dc.contributor.cvlac.spa.fl_str_mv |
Victorino, Jorge |
| dc.contributor.googlescholar.spa.fl_str_mv |
Victorino, Jorge |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación 510 - Matemáticas::514 - Topología 570 - Biología::577 - Ecología |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación 510 - Matemáticas::514 - Topología 570 - Biología::577 - Ecología Hojas de las plantas Plant Leaves Morfología (botánica) Anatomía vegetal Botany - morphology Botany - anatomy Plant anatomy Novel category discovery Unsupervised categorization Leaf shape Contour analysis Morphological Image processing Topological analysis Image classification |
| dc.subject.decs.spa.fl_str_mv |
Hojas de las plantas |
| dc.subject.decs.eng.fl_str_mv |
Plant Leaves |
| dc.subject.lemb.spa.fl_str_mv |
Morfología (botánica) Anatomía vegetal |
| dc.subject.lemb.eng.fl_str_mv |
Botany - morphology Botany - anatomy Plant anatomy |
| dc.subject.proposal.eng.fl_str_mv |
Novel category discovery Unsupervised categorization Leaf shape Contour analysis Morphological Image processing Topological analysis Image classification |
| description |
ilustraciones, diagramas |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-09-13T22:05:41Z |
| dc.date.available.none.fl_str_mv |
2023-09-13T22:05:41Z |
| dc.date.issued.none.fl_str_mv |
2023-08-22 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
| format |
http://purl.org/coar/resource_type/c_db06 |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/84707 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/84707 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Adams, D. C., Rohlf, F. J., & Slice, D. E. (2004). Geometric morphometrics: ten years of progress following the ‘revolution’. Italian Journal of Zoology, 71(1), 5–16. Alpaydin, E. (2010). Introduction to Machine Learning (2nd ed.). Cambridge, Massachusetts, USA: The MIT Press. Amézquita, E. J., Quigley, M. Y., Ophelders, T., Munch, E., & Chitwood, D. H. (2020). The shape of things to come: Topological data analysis and biology, from molecules to organisms. Developmental Dynamics, 249(7), 816–833. Amlekar, M. M., & Gaikwad, A. T. (2019). Plant classification using image processing and neural network. In Data Management, Analytics and Innovation (pp. 375–384). New York, NY: Springer. Anderson, D. R. (1976). Guidelines for line transect sampling of biological populations. USA: The Unit. Aurenhammer, F., Klein, R., & Lee, D.-T. (2013). Voronoi diagrams and Delaunay triangulations. Singapore: World Scientific Publishing Company. Ball, H., Exell, A., Harding, J., L’eonard, J., Lewis, J., Melderis, A., … Van der Veken, P. (1962). Systematics association committee for descriptive biological terminology. II. Terminology of simple symmetrical plane shapes (Chart 1). Taxon, 41(11), 145–156. Barite, M. G. (2000). The notion of category: its implications in subject analysis and in the construction and evaluation of indexing languages. KO KNOWLEDGE ORGANIZATION, 27(1–2), 4–10. Batut, B., Hiltemann, S., Bagnacani, A., Baker, D., Bhardwaj, V., Blank, C., … Others. (2018). Community-driven data analysis training for biology. Cell Systems, 6(6), 752–758. Baumgartner, A., Donahoo, M., Chitwood, D. H., & Peppe, D. J. (2020). The influences of environmental change and development on leaf shape in Vitis. American Journal of Botany, 107(4), 676–688. Beentje, H. (2010). The Kew Plant Glossary: An Illustrated Dictionary of Plant Terms. Richmond, London, UK: Kew Publishing, Royal Botanical Gardens, Kew. Bender, A. L. D., Chitwood, D. H., & Bradley, A. S. (2017). Heritability of the structures and 13C fractionation in tomato leaf wax alkanes: a genetic model system to inform paleoenvironmental reconstructions. Frontiers in Earth Science, 5, 47. Berdugo-Lattke, M. L., Gónzalez, F., Rangel-Ch, J. O., & Gómez, F. (2016). P-type based dimensionality reduction for open contours of Colombian Páramo plant species. Ecological Informatics, 36, 1–7. Biot, E., Cortizo, M., Burguet, J., Kiss, A., Oughou, M., Maugarny-Calès, A., … Others. (2016). Multiscale quantification of morphodynamics: MorphoLeaf software for 2D shape analysis. Development, 143(18), 3417–3428. Boggess, A., & Narcowich, F. J. (2015). A first course in wavelets with Fourier analysis. Hoboken, NJ, USA: John Wiley & Sons. Bor, N. L. (1953). Manual of Indian forest botany. Oxford, England, UK: Oxford University Press.; Geoffrey Cumberlege. Bookstein, F. L. (1997). Morphometric tools for landmark data: geometry and biology. Cambridge, UK: Cambridge University Press. Brown, V. K., & Lawton, J. H. (1991). Herbivory and the evolution of leaf size and shape. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 333(1267), 265–272. Bryson, A. E., Wilson Brown, M., Mullins, J., Dong, W., Bahmani, K., Bornowski, N., … Others. (2020). Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves. Applications in Plant Sciences, 8(12), e11404. Bucksch, A., Atta-Boateng, A., Azihou, A. F., Battogtokh, D., Baumgartner, A., Binder, B. M., … Others. (2017). Morphological plant modeling: unleashing geometric and topological potential within the plant sciences. Frontiers in Plant Science, 8, 900. Budd, G. E. (2021). Morphospace. Current Biology, 31(19), R1181–R1185. Cal, A. J., Sanciangco, M., Rebolledo, M. C., Luquet, D., Torres, R. O., McNally, K. L., & Henry, A. (2019). Leaf morphology, rather than plant water status, underlies genetic variation of rice leaf rolling under drought. Plant, Cell and Environment, 42, 1532–1544. Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep clustering for unsupervised learning of visual features. Proceedings of the European Conference on Computer Vision (ECCV), 132–149. Chazal, F., & Michel, B. (2021). An introduction to topological data analysis: fundamental and practical aspects for data scientists. Frontiers in Artificial Intelligence, 4, 667963. Chignell, M., Wang, L., Zare, A., & Li, J. (2022). The Evolution of HCI and Human Factors: Integrating Human and Artificial Intelligence. ACM Transactions on Computer-Human Interaction. Chitwood, D. H. (2021). The shapes of wine and table grape leaves: An ampelometric study inspired by the methods of Pierre Galet. Plants, People, Planet, 3(2), 155–170. Chitwood, D. H., & Mullins, J. (2022). A predicted developmental and evolutionary morphospace for grapevine leaves. Quantitative Plant Biology, 3, e22. Chitwood, D. H., & Otoni, W. C. (2017). Morphometric analysis of Passiflora leaves: the relationship between landmarks of the vasculature and elliptical Fourier descriptors of the blade. GigaScience, 6(1), 1–13. Chitwood, D. H., & Sinha, N. R. (2016). Evolutionary and environmental forces sculpting leaf development. Current Biology, 26(7), R297–R306. Coussement, J., Steppe, K., Lootens, P., Roldán-Ruiz, I., & De Swaef, T. (2018). A flexible geometric model for leaf shape descriptions with high accuracy. Silva Fennica, 52(2). Daelli, V., van Rijsbergen, N. J., & Treves, A. (2010). How recent experience affects the perception of ambiguous objects. Brain Research, 1322, 81–91. De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50(1), 1–18. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv Preprint. Eldar, Y. C., & Oppenheim, A. V. (2003). MMSE whitening and subspace whitening. Information Theory, IEEE Transactions On, 49(7), 1846–1851. Ellis, B., Daly, D. C., Hickey, L. J., Johnson, K. R., Mitchell, J. D., Wilf, P., & Wing, S. L. (2009). Manual of leaf architecture. Ithaca, NY, USA: Cornell University Press Ithaca. Erwig, M. (2000). The graph Voronoi diagram with applications. Networks: An International Journal, 36(3), 156–163. Failmezger, H., Lempe, J., Khadem, N., Cartolano, M., Tsiantis, M., & Tresch, A. (2018). MowJoe: a method for automated-high throughput dissected leaf phenotyping. Plant Methods, 14(1), 27. Faktor, A., & Irani, M. (2013). ``Clustering by Composition’’—Unsupervised Discovery of Image Categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6), 1092–1106. Da Fona Costa, L., & Cesar, R. M., Jr. (2018). Shape classification and analysis: theory and practice. Crc Press. Franz, N. M. (2010). Biological taxonomy and ontology development: scope and limitations. Biodiversity Informatics, 7(1). Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. K. (2001). Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291(5502), 312–316. Fritz, M. A., Rosa, S., & Sicard, A. (2018). Mechanisms underlying the environmentally induced plasticity of leaf morphology. Frontiers in Genetics, 9, 478. Fu, W.-Y., Teng, J.-C., Tang, B., Wang, Q.-Q., Yang, W., Tao, L., … Deng, Y. (2022). The Lobed-Leaf Phenotype in Brassica juncea Is Associated with the BjLMI1 Locus as Evidenced Using GradedPool-Seq. Agronomy, 12(11), 2696. Fukunaga, K., & Hostetler, L. D. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. Information Theory, IEEE Transactions On, 21(1), 32–40. Gati, I., & Tversky, A. (1984). Weighting common and distinctive features in perceptual and conceptual judgments. Cognitive Psychology, 16(3), 341–370. Gehan, M. A., Fahlgren, N., Abbasi, A., Berry, J. C., Callen, S. T., Chavez, L., … Others. (2017). PlantCV v2: Image analysis software for high-throughput plant phenotyping. PeerJ, 5, e4088. Giesen, J., Cazals, F., Pauly, M., & Zomorodian, A. (2006). The conformal alpha shape filtration. The Visual Computer, 22, 531–540. Goëau, H., Joly, A., Bonnet, P., Selmi, S., Molino, J.-F., Barthélémy, D., & Boujemaa, N. (2014). Lifeclef plant identification task 2014. CLEF2014 Working Notes. Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014, 598–615. CEUR-WS. Goldstone, R. L., Kersten, A., & Carvalho, P. F. (2013). Concepts and categorization. Gomes, J., & Velho, L. (2015). From fourier analysis to wavelets (Vol. 3). New York, USA: Springer. Grauman, K., & Darrell, T. (2006). Unsupervised learning of categories from sets of partially matching image features. Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference On, 1, 19–25. IEEE. Gonzalez, R., & Woods, R. (2006). Digital Image Processing (3rd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc. Gray, A. (1867). Manual of the botany of the northern United States. USA: Ivison & Company. Gupta, S., Rosenthal, D. M., Stinchcombe, J. R., & Baucom, R. S. (2020). The remarkable morphological diversity of leaf shape in sweet potato (Ipomoea batatas): The influence of genetics, environment, and G× E. New Phytologist, 225(5), 2183–2195. Han, J., Quan, R., Zhang, D., & Nie, F. (2017). Robust object co-segmentation using background prior. IEEE Transactions on Image Processing, 27(4), 1639–1651. Han, K., Rebuffi, S.-A., Ehrhardt, S., Vedaldi, A., & Zisserman, A. (2021). Autonovel: Automatically discovering and learning novel visual categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 6767–6781. Hawkins, W. G., Leichner, P. K., & Yang, N.-C. (1988). The circular harmonic transform for SPECT reconstruction and boundary conditions on the Fourier transform of the sinogram. IEEE Transactions on Medical Imaging, 7(2), 135–138. Hawthorne, W., & Lawrence, A. (2013). Plant identification: creating user-friendly field guides for biodiversity management. Routledge. He, N., Liu, C., Tian, M., Li, M., Yang, H., Yu, G., … Hou, J. (2018). Variation in leaf anatomical traits from tropical to cold-temperate forests and linkage to ecosystem functions. Functional Ecology, 32(1), 10–19. Hickey, M., & King, C. (2000). The Cambridge illustrated glossary of botanical terms. Cambridge, UK: Cambridge University Press. Holland, I., & Davies, J. A. (2020). Automation in the life science research laboratory. Frontiers in Bioengineering and Biotechnology, 8, 571777. Huang, F., Gan, Y., Zhang, D., Deng, F., & Peng, J. (2018). Leaf shape variation and its correlation to phenotypic traits of Soybean in northeast China. Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology, 40–45. Jia, X., Han, K., Zhu, Y., & Green, B. (2021). Joint representation learning and novel category discovery on single-and multi-modal data. Proceedings of the IEEE/CVF International Conference on Computer Vision, 610–619. Kant, I. (1908). Critique of pure reason. 1781. Modern Classical Philosophers, Cambridge, MA: Houghton Mifflin, 370–456. Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). USA: Sthda. Keeney, E. (1992). The botanizers: amateur scientists in nineteenth-century America. Chapel Hill, North Carolina, USA: Univ of North Carolina Press. Kruschke, J. K. (2008). Models of categorization. The Cambridge Handbook of Computational Psychology, 267–301. Kuhl, F. P., & Giardina, C. R. (1982). Elliptic Fourier features of a closed contour. Computer Graphics and Image Processing, 18(3), 236–258. Lee, Y. J., & Grauman, K. (2009). Shape discovery from unlabeled image collections. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2254–2261. IEEE. Letouzey, R., & Others. (1986). Manual of forest botany. Tropical Africa. Vol. 2 A. Families (1st part). Vol. 2 B. Families (2nd part). Nogent-sur-Marne, France: Centre technique forestier tropical. Li, M., An, H., Angelovici, R., Bagaza, C., Batushansky, A., Clark, L., … Others. (2018). Topological data analysis as a morphometric method: using persistent homology to demarcate a leaf morphospace. Frontiers in Plant Science, 9, 553. Li, M., Frank, M. H., Coneva, V., Mio, W., Chitwood, D. H., & Topp, C. N. (2018). The persistent homology mathematical framework provides enhanced genotype-to-phenotype associations for plant morphology. Plant Physiology, 177(4), 1382–1395. Li, M., Frank, M. H., Coneva, V., Mio, W., Chitwood, D. H., & Topp, C. N. (2017). The persistent homology mathematical framework provides enhanced genotype-to-phenotype associations for plant morphology. Plant Physiology, 177(4), 1382–1395. Liantoni, F., Prakisya, N. P. T., Aristyagama, Y. H., & Hatta, P. (2021). Comparative analysis of hierarchical clustering with improve feature for herbs leaves. Journal of Physics: Conference Series, 1808, 012025. IOP Publishing. Lin, W.-C., Tsai, C.-F., Hu, Y.-H., & Jhang, J.-S. (2017). Clustering-based undersampling in class-imbalanced data. Information Sciences, 409, 17–26. Lipton, Z. C. (2016). The mythos of model interpretability. arXiv Preprint. Liu, C., Li, Y., Xu, L., Chen, Z., & He, N. (2019). Variation in leaf morphological, stomatal, and anatomical traits and their relationships in temperate and subtropical forests. Scientific Reports, 9(1), 5803. Liu, Y., & Tuytelaars, T. (2022). Residual tuning: Toward novel category discovery without labels. IEEE Transactions on Neural Networks and Learning Systems, 1–15. doi:"10.1109/TNNLS.2022.3140235" Lucas, T. C. D. (2020). A translucent box: interpretable machine learning in ecology. Ecological Monographs, 90(4), e01422. Mähler, N., Schiffthaler, B., Robinson, K. M., Terebieniec, B. K., Vučak, M., Mannapperuma, C., … Street, N. R. (2020). Leaf shape in Populus tremula is a complex, omnigenic trait. Ecology and Evolution, 10(21), 11922–11940. Marcinkevičs, R., & Vogt, J. E. (2020). Interpretability and explainability: A machine learning zoo mini-tour. arXiv Preprint arXiv:2012. 01805. Mark, de B., Otfried, C., Marc, van K., & Mark, O. (2008). Computational geometry algorithms and applications. Germany: Springer. Mazzocchi, F. (2008). Complexity in biology: exceeding the limits of reductionism and determinism using complexity theory. EMBO Reports, 9(1), 10–14. Min, E., Guo, X., Liu, Q., Zhang, G., Cui, J., & Long, J. (2018). A survey of clustering with deep learning: From the perspective of network architecture. IEEE Access, 6, 39501–39514. Mohtashamian, M., Karimian, M., Moola, F., Kavousi, K., & Masoudi-Nejad, A. (2021). Automated Plant Species Identification Using Leaf Shape-Based Classification Techniques: A Case Study on Iranian Maples. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 45(3), 1051–1061. Molnar, C. (2020). Interpretable machine learning. USA: Lulu.com. Montavon, G., Kauffmann, J., Samek, W., & Müller, K.-R. (2022). Explaining the predictions of unsupervised learning models. xxAI-Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, 117–138. Springer. Mutalib, S., Hasbullah, N. H., Abdul-Rahman, S., Shamsuddin, M. R., & Ab Malik, A. M. (2022). Herbal Plant Analysis Based on Leaf Features using K-Means Clustering. IOP Conference Series: Earth and Environmental Science, 1019, 012026. IOP Publishing. Nasibov, E. N., & Ulutagay, G. (2009). Robustness of density-based clustering methods with various neighborhood relations. Fuzzy Sets and Systems, 160(24), 3601–3615. Nicotra, A. B., Leigh, A., Boyce, C. K., Jones, C. S., Niklas, K. J., Royer, D. L., & Tsukaya, H. (2011). The evolution and functional significance of leaf shape in the angiosperms. Functional Plant Biology, 38(7), 535–552. Niklas, K. J. (1992). Plant biomechanics: an engineering approach to plant form and function. Chicago, IL: University of Chicago press. Olivares, L., Victorino, J., & Gómez, F. (2016). Automatic leaf shape category discovery. Pattern Recognition (ICPR), 2016 23rd International Conference On, 1023–1028. IEEE. Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11(285–296), 23–27. Oza, K. K., Desai, R. J., & Raole, V. M. (2021). Digital Morphometrics: A Tool for Leaf Morpho-Taxonomical Studies. Indian Journal of Advanced Botany, 1(2), 1–7. De La Paz Pollicelli, M., Idaszkin, Y. L., Gonzalez-José, R., & Márquez, F. (2018). Leaf shape variation as a potential biomarker of soil pollution. Ecotoxicology and Environmental Safety, 164, 69–74. Pinaya, W. H. L., Tudosiu, P.-D., Gray, R., Rees, G., Nachev, P., Ourselin, S., & Cardoso, M. J. (2022). Unsupervised brain imaging 3D anomaly detection and segmentation with transformers. Medical Image Analysis, 79, 102475. Radford, A. E., Ahles, H. E., & Bell, C. R. (2010). Manual of the vascular flora of the Carolinas. Chapel Hill, North Carolina, USA: Univ of North Carolina Press. Ranganathan, S. R. (1937). Prolegomena to library classification. Madras Library Association, Madras. Reeds, K. M. (1976). Renaissance humanism and botany. Annals of Science, 33(6), 519–542. Salo, H. M., Nguyen, N., Alakärppä, E., Klavins, L., Hykkerud, A. L., Karppinen, K., … Häggman, H. (2021). Authentication of berries and berry-based food products. Comprehensive Reviews in Food Science and Food Safety, 20(5), 5197–5225. Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., … Lin, C.-T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664–681. Shen, J., & Han, J. (2022). Automated taxonomy discovery and exploration. Springer Nature. Shimshoni, I., Georgescu, B., & Meer, P. (2006). 1 Adaptive Mean Shift Based Clustering in High Dimensions. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice, 203–220. Sifre, L., & Mallat, S. (2013). Rotation, scaling and deformation invariant scattering for texture discrimination. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1233–1240. Smith, D. R., & Brown, J. A. (2004). Biological Populations. Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters, 77. Stojnić, S., Viscosi, V., Marković, M., Ivanković, M., Orlović, S., Tognetti, R., … Loy, A. (2022). Spatial patterns of leaf shape variation in European beech (Fagus sylvatica L.) provenances. Trees, 36(1), 497–511. Stroud, S., Fennell, M., Mitchley, J., Lydon, S., Peacock, J., & Bacon, K. L. (2022). The botanical education extinction and the fall of plant awareness. Ecology and Evolution, 12(7), e9019. Stuessy, T. F. (2009). Plant taxonomy: the systematic evaluation of comparative data. Columbia University Press. Suzuki, S., & Others. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1), 32–46. Thomasson, A. (2022). Categories. Retrieved from The Stanford Encyclopedia of Philosophy website: https://plato.stanford.edu/archives/win2022/entries/categories/ Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327. Uesaka, Y. (1984). A new type Fourier descriptor method that is effective also to open contour. IEICE Trans Inf Syst, 67(3), 166–173. Van Rijsbergen, C. J. (1974). Foundation of evaluation. Journal of Documentation, 30(4), 365–373. Vaze, S., Han, K., Vedaldi, A., & Zisserman, A. (2022). Generalized category discovery. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7492–7501. Victorino, J., & Gómez, F. (2015, September). A comparative study of dimensionality reduction methods for p-type based contour representations. Computing Colombian Conference (10CCC), 2015 10th, 294–301. IEEE. Victorino, Jorge, & Gómez, F. (2019). Contour analysis for interpretable leaf shape category discovery. Plant Methods, 15(1), 1–12. Victorino, Jorge, Rudas, J., Reyes, A. M., Pulido, C., Chaparro, L. F., Estrada, C., … Gómez, F. (2021). Highly Sessional Aggressive Behaviors Link to Temporal Dynamics Shared Across Space. IEEE Access, 9, 165072–165084. Victorino, Jorge, Rudas, J., Reyes, A. M., Pulido, C., Chaparro, L. F., Narváez, L. A., … Gómez, F. (2020). Spatial-temporal patterns of aggressive behaviors. A case study Bogotá, Colombia. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 667–672. IEEE. Wäldchen, J., & Mäder, P. (2018). Plant species identification using computer vision techniques: A systematic literature review. Archives of Computational Methods in Engineering, 25(2), 507–543. Wang, H., Liu, P.-L., Li, J., Yang, H., Li, Q., & Chang, Z.-Y. (2021). Why more leaflets? The role of natural selection in shaping the spatial pattern of leaf-shape variation in Oxytropis diversifolia (Fabaceae) and two close relatives. Frontiers in Plant Science, 12, 681962. Wang, J., Ma, Z., Nie, F., & Li, X. (2021). Progressive self-supervised clustering with novel category discovery. IEEE Transactions on Cybernetics. Wang, N., Palmroth, S., Maier, C. A., Domec, J.-C., & Oren, R. (2019). Anatomical changes with needle length are correlated with leaf structural and physiological traits across five Pinus species. Plant, Cell & Environment, 42(1), 1690–1704. Wasserman, L. (2018). Topological data analysis. Annual Review of Statistics and Its Application, 5, 501–532. Wkeglarczyk, S. (2018). Kernel density estimation and its application. ITM Web of Conferences, 23, 00037. EDP Sciences. Xia, X., Pan, X., Li, N., He, X., Ma, L., Zhang, X., & Ding, N. (2022). GAN-based anomaly detection: A review. Neurocomputing, 493, 497–535. Yang, C. (2021). Plant leaf recognition by integrating shape and texture features. Pattern Recognition, 112, 107809. Yang, K., Wu, J., Li, X., Pang, X., Yuan, Y., Qi, G., & Yang, M. (2022). Intraspecific leaf morphological variation in Quercus dentata Thunb.: a comparison of traditional and geometric morphometric methods, a pilot study. Journal of Forestry Research, 1–14. Yousefi, E., Baleghi, Y., & Sakhaei, S. M. (2017). Rotation invariant wavelet descriptors, a new set of features to enhance plant leaves classification. Computers and Electronics in Agriculture, 140, 70–76. Zhang, D., Han, J., Zhao, L., & Meng, D. (2019). Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework. International Journal of Computer Vision, 127(4), 363–380. Zhang, Q.-S., & Zhu, S.-C. (2018). Visual interpretability for deep learning: a survey. Frontiers of Information Technology & Electronic Engineering, 19(1), 27–39. Zheng, Y., Xu, F., Li, Q., Wang, G., Liu, N., Gong, Y., … Xu, S. (2018). QTL mapping combined with bulked segregant analysis identify SNP markers linked to leaf shape traits in Pisum sativum using SLAF sequencing. Frontiers in Genetics, 9, 615. Zvereva, E. L., & Kozlov, M. V. (2021). Biases in ecological research: attitudes of scientists and ways of control. Scientific Reports, 11(1), 226. |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gómez Jaramillo, Francisco Albeiro415aa92d5615e8a2fa29cfa0a28ec210Victorino Guzmán, Jorge Enrique369e52bb4554837653778520d5bdef50COMBIOSVictorino, Jorge [0000-0003-3331-4340]Victorino, JorgeVictorino, Jorge2023-09-13T22:05:41Z2023-09-13T22:05:41Z2023-08-22https://repositorio.unal.edu.co/handle/unal/84707Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasLos estudios de descripción morfológica de hojas son complejos en la medida que requieren de personal altamente entrenado y de la consulta de una gran cantidad de documentación disponible como i.e., sistemas de categorías visuales en manuales botánicos, libros, bases de datos en línea, herbarios, inclusive contrastar hallazgos con otros expertos. Por tanto, estos estudios demandan una inversión significativa de recursos y tienen una alta carga de trabajo manual. Por otro lado, la cantidad de botánicos disponibles y en formación no logra suplir las necesidades actuales de la creciente cantidad de informaci\'on foliar resultante de la automatización y la creciente complejidad de las preguntas de investigación. En este escenario se requieren procesos computacionales automáticos que proveen una descripción morfológica cualitativa y cuantitativa que alivian en gran medida la carga de trabajo de los expertos. La dificultad de usar enfoques automáticos en análisis morfológicos se materializa si hace falta alguna de estas funcionalidades: 1. extraer los rasgos relevantes de la forma para que puedan analizarse por separado, 2. producir categorías robustas que emergen de la representaci\'on de cada rasgo, y 3. capacidad de explicación de las categorías en el contexto del problema biológico. En este trabajo se propone una estrategia computacional para el descubrimiento de categorías de formas de hojas que ayuda a automatizar estas funcionalidades clave. Primero, un algoritmo extrae cada rasgo y lo representa de manera adecuada (contractiva) en un espacio de características (morfoespacio) específico. Luego, la muestra proyectada en el morfoespacio es analizada y organizada bajo los conceptos de vecindad, cohesión y persistencia. Este método realiza un análisis del n\'umero de grupos para todos los tama\~nos vecindad y escoge la cantidad de grupos óptima, en otras palabras, las categorías. Este sistema de categorías tiene la propiedad de explicar el fenómeno subyacente de manera cualitativa y cuantitativa. De esta forma, durante el análisis de vecindad surge el dendrograma de la categorizaci\'on. La interpretación de los resultados est\'a dada por el morfoespacio y por el dendrograma. La efectividad del enfoque propuesto se eval\'ua frente a sistemas de categorías establecidos por expertos. Los resultados evidencian que el enfoque puede producir categorizaciones razonables similares a lo reportado en el manual de Hickey. Este enfoque permitirá a los biólogos hacer descripciones cualitativas y cuantitativas de la morfología útiles en estudios de variabilidad morfológica, taxonomía, plasticidad, adaptación y ecología. (Texto tomado de la fuente)Leaf morphological description studies are complex because they require highly trained personnel and the consultation of a large amount of available documentation, such as visual category systems in botanical manuals, books, online databases, and herbariums, and commonly should be contrasted with other experts. These studies require a significant resource investment and a high manual workload. On the other hand, the number of botanists available and in training for performing these studies cannot meet the current needs of the growing amount of foliar information resulting from automation and the increasing complexity of research questions. In this scenario, automatic computational processes are required to provide a qualitative and quantitative morphological description that significantly alleviates the experts' workload. The difficulty of using automatic approaches in morphological analysis materializes if any of these functionalities are missing: 1. extracting the relevant features from the shape so that they can be analyzed separately, 2. producing robust categories that emerge from the representation of each feature, and 3. explanatory capacity of the categories in the context of the biological problem. This work proposes a computational strategy for discovering leaf-shape categories that helps to overcome these limitations. First, an algorithm extracts each feature and represents it appropriately (contractive) in a specific feature space (morphospace). Then, the points in the morphospace are analyzed and organized under the concepts of neighborhood, cohesion, and persistence. The method accounts for these features and analyzes the number of clusters for all neighborhood sizes, and chooses the optimal number of clusters, in other words, the number of categories. This system of categories has the property of explaining the underlying phenomenon qualitatively and quantitatively. In this way, during the neighborhood analysis, the categorization dendrogram emerges. Finally, the interpretation of the results is given by the morphospace and by the dendrogram. The effectiveness of the proposed approach is evaluated against category systems established by experts. The results show that the proposed approach can produce useful categorizations similar to what is reported in Hickey's manual, a widely used botanist manual. This approach allows biologists to make qualitative and quantitative descriptions of leaf morphology, helping them to describe variability, taxonomy, plasticity, adaptation, and ecological changes.DoctoradoDoctor en Ingenieríaxx, 75 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación510 - Matemáticas::514 - Topología570 - Biología::577 - EcologíaHojas de las plantasPlant LeavesMorfología (botánica)Anatomía vegetalBotany - morphologyBotany - anatomyPlant anatomyNovel category discoveryUnsupervised categorizationLeaf shapeContour analysisMorphologicalImage processingTopological analysisImage classificationA computational model for interpretable visual category discovery of foliar shapesModelo computacional para el descubrimiento de categorías de formas foliaresTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAdams, D. C., Rohlf, F. J., & Slice, D. E. (2004). Geometric morphometrics: ten years of progress following the ‘revolution’. Italian Journal of Zoology, 71(1), 5–16.Alpaydin, E. (2010). Introduction to Machine Learning (2nd ed.). Cambridge, Massachusetts, USA: The MIT Press.Amézquita, E. J., Quigley, M. Y., Ophelders, T., Munch, E., & Chitwood, D. H. (2020). The shape of things to come: Topological data analysis and biology, from molecules to organisms. Developmental Dynamics, 249(7), 816–833.Amlekar, M. M., & Gaikwad, A. T. (2019). Plant classification using image processing and neural network. In Data Management, Analytics and Innovation (pp. 375–384). New York, NY: Springer.Anderson, D. R. (1976). Guidelines for line transect sampling of biological populations. USA: The Unit.Aurenhammer, F., Klein, R., & Lee, D.-T. (2013). Voronoi diagrams and Delaunay triangulations. Singapore: World Scientific Publishing Company.Ball, H., Exell, A., Harding, J., L’eonard, J., Lewis, J., Melderis, A., … Van der Veken, P. (1962). Systematics association committee for descriptive biological terminology. II. Terminology of simple symmetrical plane shapes (Chart 1). Taxon, 41(11), 145–156.Barite, M. G. (2000). The notion of category: its implications in subject analysis and in the construction and evaluation of indexing languages. KO KNOWLEDGE ORGANIZATION, 27(1–2), 4–10.Batut, B., Hiltemann, S., Bagnacani, A., Baker, D., Bhardwaj, V., Blank, C., … Others. (2018). Community-driven data analysis training for biology. Cell Systems, 6(6), 752–758.Baumgartner, A., Donahoo, M., Chitwood, D. H., & Peppe, D. J. (2020). The influences of environmental change and development on leaf shape in Vitis. American Journal of Botany, 107(4), 676–688.Beentje, H. (2010). The Kew Plant Glossary: An Illustrated Dictionary of Plant Terms. Richmond, London, UK: Kew Publishing, Royal Botanical Gardens, Kew.Bender, A. L. D., Chitwood, D. H., & Bradley, A. S. (2017). Heritability of the structures and 13C fractionation in tomato leaf wax alkanes: a genetic model system to inform paleoenvironmental reconstructions. Frontiers in Earth Science, 5, 47.Berdugo-Lattke, M. L., Gónzalez, F., Rangel-Ch, J. O., & Gómez, F. (2016). P-type based dimensionality reduction for open contours of Colombian Páramo plant species. Ecological Informatics, 36, 1–7.Biot, E., Cortizo, M., Burguet, J., Kiss, A., Oughou, M., Maugarny-Calès, A., … Others. (2016). Multiscale quantification of morphodynamics: MorphoLeaf software for 2D shape analysis. Development, 143(18), 3417–3428.Boggess, A., & Narcowich, F. J. (2015). A first course in wavelets with Fourier analysis. Hoboken, NJ, USA: John Wiley & Sons.Bor, N. L. (1953). Manual of Indian forest botany. Oxford, England, UK: Oxford University Press.; Geoffrey Cumberlege.Bookstein, F. L. (1997). Morphometric tools for landmark data: geometry and biology. Cambridge, UK: Cambridge University Press.Brown, V. K., & Lawton, J. H. (1991). Herbivory and the evolution of leaf size and shape. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 333(1267), 265–272.Bryson, A. E., Wilson Brown, M., Mullins, J., Dong, W., Bahmani, K., Bornowski, N., … Others. (2020). Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves. Applications in Plant Sciences, 8(12), e11404.Bucksch, A., Atta-Boateng, A., Azihou, A. F., Battogtokh, D., Baumgartner, A., Binder, B. M., … Others. (2017). Morphological plant modeling: unleashing geometric and topological potential within the plant sciences. Frontiers in Plant Science, 8, 900.Budd, G. E. (2021). Morphospace. Current Biology, 31(19), R1181–R1185.Cal, A. J., Sanciangco, M., Rebolledo, M. C., Luquet, D., Torres, R. O., McNally, K. L., & Henry, A. (2019). Leaf morphology, rather than plant water status, underlies genetic variation of rice leaf rolling under drought. Plant, Cell and Environment, 42, 1532–1544.Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep clustering for unsupervised learning of visual features. Proceedings of the European Conference on Computer Vision (ECCV), 132–149.Chazal, F., & Michel, B. (2021). An introduction to topological data analysis: fundamental and practical aspects for data scientists. Frontiers in Artificial Intelligence, 4, 667963.Chignell, M., Wang, L., Zare, A., & Li, J. (2022). The Evolution of HCI and Human Factors: Integrating Human and Artificial Intelligence. ACM Transactions on Computer-Human Interaction.Chitwood, D. H. (2021). The shapes of wine and table grape leaves: An ampelometric study inspired by the methods of Pierre Galet. Plants, People, Planet, 3(2), 155–170.Chitwood, D. H., & Mullins, J. (2022). A predicted developmental and evolutionary morphospace for grapevine leaves. Quantitative Plant Biology, 3, e22.Chitwood, D. H., & Otoni, W. C. (2017). Morphometric analysis of Passiflora leaves: the relationship between landmarks of the vasculature and elliptical Fourier descriptors of the blade. GigaScience, 6(1), 1–13.Chitwood, D. H., & Sinha, N. R. (2016). Evolutionary and environmental forces sculpting leaf development. Current Biology, 26(7), R297–R306.Coussement, J., Steppe, K., Lootens, P., Roldán-Ruiz, I., & De Swaef, T. (2018). A flexible geometric model for leaf shape descriptions with high accuracy. Silva Fennica, 52(2).Daelli, V., van Rijsbergen, N. J., & Treves, A. (2010). How recent experience affects the perception of ambiguous objects. Brain Research, 1322, 81–91.De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50(1), 1–18.Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv Preprint.Eldar, Y. C., & Oppenheim, A. V. (2003). MMSE whitening and subspace whitening. Information Theory, IEEE Transactions On, 49(7), 1846–1851.Ellis, B., Daly, D. C., Hickey, L. J., Johnson, K. R., Mitchell, J. D., Wilf, P., & Wing, S. L. (2009). Manual of leaf architecture. Ithaca, NY, USA: Cornell University Press Ithaca.Erwig, M. (2000). The graph Voronoi diagram with applications. Networks: An International Journal, 36(3), 156–163.Failmezger, H., Lempe, J., Khadem, N., Cartolano, M., Tsiantis, M., & Tresch, A. (2018). MowJoe: a method for automated-high throughput dissected leaf phenotyping. Plant Methods, 14(1), 27.Faktor, A., & Irani, M. (2013). ``Clustering by Composition’’—Unsupervised Discovery of Image Categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6), 1092–1106.Da Fona Costa, L., & Cesar, R. M., Jr. (2018). Shape classification and analysis: theory and practice. Crc Press.Franz, N. M. (2010). Biological taxonomy and ontology development: scope and limitations. Biodiversity Informatics, 7(1).Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. K. (2001). Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291(5502), 312–316.Fritz, M. A., Rosa, S., & Sicard, A. (2018). Mechanisms underlying the environmentally induced plasticity of leaf morphology. Frontiers in Genetics, 9, 478.Fu, W.-Y., Teng, J.-C., Tang, B., Wang, Q.-Q., Yang, W., Tao, L., … Deng, Y. (2022). The Lobed-Leaf Phenotype in Brassica juncea Is Associated with the BjLMI1 Locus as Evidenced Using GradedPool-Seq. Agronomy, 12(11), 2696.Fukunaga, K., & Hostetler, L. D. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. Information Theory, IEEE Transactions On, 21(1), 32–40.Gati, I., & Tversky, A. (1984). Weighting common and distinctive features in perceptual and conceptual judgments. Cognitive Psychology, 16(3), 341–370.Gehan, M. A., Fahlgren, N., Abbasi, A., Berry, J. C., Callen, S. T., Chavez, L., … Others. (2017). PlantCV v2: Image analysis software for high-throughput plant phenotyping. PeerJ, 5, e4088.Giesen, J., Cazals, F., Pauly, M., & Zomorodian, A. (2006). The conformal alpha shape filtration. The Visual Computer, 22, 531–540.Goëau, H., Joly, A., Bonnet, P., Selmi, S., Molino, J.-F., Barthélémy, D., & Boujemaa, N. (2014). Lifeclef plant identification task 2014. CLEF2014 Working Notes. Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014, 598–615. CEUR-WS.Goldstone, R. L., Kersten, A., & Carvalho, P. F. (2013). Concepts and categorization.Gomes, J., & Velho, L. (2015). From fourier analysis to wavelets (Vol. 3). New York, USA: Springer.Grauman, K., & Darrell, T. (2006). Unsupervised learning of categories from sets of partially matching image features. Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference On, 1, 19–25. IEEE.Gonzalez, R., & Woods, R. (2006). Digital Image Processing (3rd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc.Gray, A. (1867). Manual of the botany of the northern United States. USA: Ivison & Company.Gupta, S., Rosenthal, D. M., Stinchcombe, J. R., & Baucom, R. S. (2020). The remarkable morphological diversity of leaf shape in sweet potato (Ipomoea batatas): The influence of genetics, environment, and G× E. New Phytologist, 225(5), 2183–2195.Han, J., Quan, R., Zhang, D., & Nie, F. (2017). Robust object co-segmentation using background prior. IEEE Transactions on Image Processing, 27(4), 1639–1651.Han, K., Rebuffi, S.-A., Ehrhardt, S., Vedaldi, A., & Zisserman, A. (2021). Autonovel: Automatically discovering and learning novel visual categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 6767–6781.Hawkins, W. G., Leichner, P. K., & Yang, N.-C. (1988). The circular harmonic transform for SPECT reconstruction and boundary conditions on the Fourier transform of the sinogram. IEEE Transactions on Medical Imaging, 7(2), 135–138.Hawthorne, W., & Lawrence, A. (2013). Plant identification: creating user-friendly field guides for biodiversity management. Routledge.He, N., Liu, C., Tian, M., Li, M., Yang, H., Yu, G., … Hou, J. (2018). Variation in leaf anatomical traits from tropical to cold-temperate forests and linkage to ecosystem functions. Functional Ecology, 32(1), 10–19.Hickey, M., & King, C. (2000). The Cambridge illustrated glossary of botanical terms. Cambridge, UK: Cambridge University Press.Holland, I., & Davies, J. A. (2020). Automation in the life science research laboratory. Frontiers in Bioengineering and Biotechnology, 8, 571777.Huang, F., Gan, Y., Zhang, D., Deng, F., & Peng, J. (2018). Leaf shape variation and its correlation to phenotypic traits of Soybean in northeast China. Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology, 40–45.Jia, X., Han, K., Zhu, Y., & Green, B. (2021). Joint representation learning and novel category discovery on single-and multi-modal data. Proceedings of the IEEE/CVF International Conference on Computer Vision, 610–619.Kant, I. (1908). Critique of pure reason. 1781. Modern Classical Philosophers, Cambridge, MA: Houghton Mifflin, 370–456.Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). USA: Sthda.Keeney, E. (1992). The botanizers: amateur scientists in nineteenth-century America. Chapel Hill, North Carolina, USA: Univ of North Carolina Press.Kruschke, J. K. (2008). Models of categorization. The Cambridge Handbook of Computational Psychology, 267–301.Kuhl, F. P., & Giardina, C. R. (1982). Elliptic Fourier features of a closed contour. Computer Graphics and Image Processing, 18(3), 236–258.Lee, Y. J., & Grauman, K. (2009). Shape discovery from unlabeled image collections. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2254–2261. IEEE.Letouzey, R., & Others. (1986). Manual of forest botany. Tropical Africa. Vol. 2 A. Families (1st part). Vol. 2 B. Families (2nd part). Nogent-sur-Marne, France: Centre technique forestier tropical.Li, M., An, H., Angelovici, R., Bagaza, C., Batushansky, A., Clark, L., … Others. (2018). Topological data analysis as a morphometric method: using persistent homology to demarcate a leaf morphospace. Frontiers in Plant Science, 9, 553.Li, M., Frank, M. H., Coneva, V., Mio, W., Chitwood, D. H., & Topp, C. N. (2018). The persistent homology mathematical framework provides enhanced genotype-to-phenotype associations for plant morphology. Plant Physiology, 177(4), 1382–1395.Li, M., Frank, M. H., Coneva, V., Mio, W., Chitwood, D. H., & Topp, C. N. (2017). The persistent homology mathematical framework provides enhanced genotype-to-phenotype associations for plant morphology. Plant Physiology, 177(4), 1382–1395.Liantoni, F., Prakisya, N. P. T., Aristyagama, Y. H., & Hatta, P. (2021). Comparative analysis of hierarchical clustering with improve feature for herbs leaves. Journal of Physics: Conference Series, 1808, 012025. IOP Publishing.Lin, W.-C., Tsai, C.-F., Hu, Y.-H., & Jhang, J.-S. (2017). Clustering-based undersampling in class-imbalanced data. Information Sciences, 409, 17–26.Lipton, Z. C. (2016). The mythos of model interpretability. arXiv Preprint.Liu, C., Li, Y., Xu, L., Chen, Z., & He, N. (2019). Variation in leaf morphological, stomatal, and anatomical traits and their relationships in temperate and subtropical forests. Scientific Reports, 9(1), 5803.Liu, Y., & Tuytelaars, T. (2022). Residual tuning: Toward novel category discovery without labels. IEEE Transactions on Neural Networks and Learning Systems, 1–15. doi:"10.1109/TNNLS.2022.3140235"Lucas, T. C. D. (2020). A translucent box: interpretable machine learning in ecology. Ecological Monographs, 90(4), e01422.Mähler, N., Schiffthaler, B., Robinson, K. M., Terebieniec, B. K., Vučak, M., Mannapperuma, C., … Street, N. R. (2020). Leaf shape in Populus tremula is a complex, omnigenic trait. Ecology and Evolution, 10(21), 11922–11940.Marcinkevičs, R., & Vogt, J. E. (2020). Interpretability and explainability: A machine learning zoo mini-tour. arXiv Preprint arXiv:2012. 01805.Mark, de B., Otfried, C., Marc, van K., & Mark, O. (2008). Computational geometry algorithms and applications. Germany: Springer.Mazzocchi, F. (2008). Complexity in biology: exceeding the limits of reductionism and determinism using complexity theory. EMBO Reports, 9(1), 10–14.Min, E., Guo, X., Liu, Q., Zhang, G., Cui, J., & Long, J. (2018). A survey of clustering with deep learning: From the perspective of network architecture. IEEE Access, 6, 39501–39514.Mohtashamian, M., Karimian, M., Moola, F., Kavousi, K., & Masoudi-Nejad, A. (2021). Automated Plant Species Identification Using Leaf Shape-Based Classification Techniques: A Case Study on Iranian Maples. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 45(3), 1051–1061.Molnar, C. (2020). Interpretable machine learning. USA: Lulu.com.Montavon, G., Kauffmann, J., Samek, W., & Müller, K.-R. (2022). Explaining the predictions of unsupervised learning models. xxAI-Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, 117–138. Springer.Mutalib, S., Hasbullah, N. H., Abdul-Rahman, S., Shamsuddin, M. R., & Ab Malik, A. M. (2022). Herbal Plant Analysis Based on Leaf Features using K-Means Clustering. IOP Conference Series: Earth and Environmental Science, 1019, 012026. IOP Publishing.Nasibov, E. N., & Ulutagay, G. (2009). Robustness of density-based clustering methods with various neighborhood relations. Fuzzy Sets and Systems, 160(24), 3601–3615.Nicotra, A. B., Leigh, A., Boyce, C. K., Jones, C. S., Niklas, K. J., Royer, D. L., & Tsukaya, H. (2011). The evolution and functional significance of leaf shape in the angiosperms. Functional Plant Biology, 38(7), 535–552.Niklas, K. J. (1992). Plant biomechanics: an engineering approach to plant form and function. Chicago, IL: University of Chicago press.Olivares, L., Victorino, J., & Gómez, F. (2016). Automatic leaf shape category discovery. Pattern Recognition (ICPR), 2016 23rd International Conference On, 1023–1028. IEEE.Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11(285–296), 23–27.Oza, K. K., Desai, R. J., & Raole, V. M. (2021). Digital Morphometrics: A Tool for Leaf Morpho-Taxonomical Studies. Indian Journal of Advanced Botany, 1(2), 1–7.De La Paz Pollicelli, M., Idaszkin, Y. L., Gonzalez-José, R., & Márquez, F. (2018). Leaf shape variation as a potential biomarker of soil pollution. Ecotoxicology and Environmental Safety, 164, 69–74.Pinaya, W. H. L., Tudosiu, P.-D., Gray, R., Rees, G., Nachev, P., Ourselin, S., & Cardoso, M. J. (2022). Unsupervised brain imaging 3D anomaly detection and segmentation with transformers. Medical Image Analysis, 79, 102475.Radford, A. E., Ahles, H. E., & Bell, C. R. (2010). Manual of the vascular flora of the Carolinas. Chapel Hill, North Carolina, USA: Univ of North Carolina Press.Ranganathan, S. R. (1937). Prolegomena to library classification. Madras Library Association, Madras.Reeds, K. M. (1976). Renaissance humanism and botany. Annals of Science, 33(6), 519–542.Salo, H. M., Nguyen, N., Alakärppä, E., Klavins, L., Hykkerud, A. L., Karppinen, K., … Häggman, H. (2021). Authentication of berries and berry-based food products. Comprehensive Reviews in Food Science and Food Safety, 20(5), 5197–5225.Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., … Lin, C.-T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664–681.Shen, J., & Han, J. (2022). Automated taxonomy discovery and exploration. Springer Nature.Shimshoni, I., Georgescu, B., & Meer, P. (2006). 1 Adaptive Mean Shift Based Clustering in High Dimensions. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice, 203–220.Sifre, L., & Mallat, S. (2013). Rotation, scaling and deformation invariant scattering for texture discrimination. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1233–1240.Smith, D. R., & Brown, J. A. (2004). Biological Populations. Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters, 77.Stojnić, S., Viscosi, V., Marković, M., Ivanković, M., Orlović, S., Tognetti, R., … Loy, A. (2022). Spatial patterns of leaf shape variation in European beech (Fagus sylvatica L.) provenances. Trees, 36(1), 497–511.Stroud, S., Fennell, M., Mitchley, J., Lydon, S., Peacock, J., & Bacon, K. L. (2022). The botanical education extinction and the fall of plant awareness. Ecology and Evolution, 12(7), e9019.Stuessy, T. F. (2009). Plant taxonomy: the systematic evaluation of comparative data. Columbia University Press.Suzuki, S., & Others. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1), 32–46.Thomasson, A. (2022). Categories. Retrieved from The Stanford Encyclopedia of Philosophy website: https://plato.stanford.edu/archives/win2022/entries/categories/Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327.Uesaka, Y. (1984). A new type Fourier descriptor method that is effective also to open contour. IEICE Trans Inf Syst, 67(3), 166–173.Van Rijsbergen, C. J. (1974). Foundation of evaluation. Journal of Documentation, 30(4), 365–373.Vaze, S., Han, K., Vedaldi, A., & Zisserman, A. (2022). Generalized category discovery. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7492–7501.Victorino, J., & Gómez, F. (2015, September). A comparative study of dimensionality reduction methods for p-type based contour representations. Computing Colombian Conference (10CCC), 2015 10th, 294–301. IEEE.Victorino, Jorge, & Gómez, F. (2019). Contour analysis for interpretable leaf shape category discovery. Plant Methods, 15(1), 1–12.Victorino, Jorge, Rudas, J., Reyes, A. M., Pulido, C., Chaparro, L. F., Estrada, C., … Gómez, F. (2021). Highly Sessional Aggressive Behaviors Link to Temporal Dynamics Shared Across Space. IEEE Access, 9, 165072–165084.Victorino, Jorge, Rudas, J., Reyes, A. M., Pulido, C., Chaparro, L. F., Narváez, L. A., … Gómez, F. (2020). Spatial-temporal patterns of aggressive behaviors. A case study Bogotá, Colombia. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 667–672. IEEE.Wäldchen, J., & Mäder, P. (2018). Plant species identification using computer vision techniques: A systematic literature review. Archives of Computational Methods in Engineering, 25(2), 507–543.Wang, H., Liu, P.-L., Li, J., Yang, H., Li, Q., & Chang, Z.-Y. (2021). Why more leaflets? The role of natural selection in shaping the spatial pattern of leaf-shape variation in Oxytropis diversifolia (Fabaceae) and two close relatives. Frontiers in Plant Science, 12, 681962.Wang, J., Ma, Z., Nie, F., & Li, X. (2021). Progressive self-supervised clustering with novel category discovery. IEEE Transactions on Cybernetics.Wang, N., Palmroth, S., Maier, C. A., Domec, J.-C., & Oren, R. (2019). Anatomical changes with needle length are correlated with leaf structural and physiological traits across five Pinus species. Plant, Cell & Environment, 42(1), 1690–1704.Wasserman, L. (2018). Topological data analysis. Annual Review of Statistics and Its Application, 5, 501–532.Wkeglarczyk, S. (2018). Kernel density estimation and its application. ITM Web of Conferences, 23, 00037. EDP Sciences.Xia, X., Pan, X., Li, N., He, X., Ma, L., Zhang, X., & Ding, N. (2022). GAN-based anomaly detection: A review. Neurocomputing, 493, 497–535.Yang, C. (2021). Plant leaf recognition by integrating shape and texture features. Pattern Recognition, 112, 107809.Yang, K., Wu, J., Li, X., Pang, X., Yuan, Y., Qi, G., & Yang, M. (2022). Intraspecific leaf morphological variation in Quercus dentata Thunb.: a comparison of traditional and geometric morphometric methods, a pilot study. Journal of Forestry Research, 1–14.Yousefi, E., Baleghi, Y., & Sakhaei, S. M. (2017). Rotation invariant wavelet descriptors, a new set of features to enhance plant leaves classification. Computers and Electronics in Agriculture, 140, 70–76.Zhang, D., Han, J., Zhao, L., & Meng, D. (2019). Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework. International Journal of Computer Vision, 127(4), 363–380.Zhang, Q.-S., & Zhu, S.-C. (2018). Visual interpretability for deep learning: a survey. Frontiers of Information Technology & Electronic Engineering, 19(1), 27–39.Zheng, Y., Xu, F., Li, Q., Wang, G., Liu, N., Gong, Y., … Xu, S. (2018). QTL mapping combined with bulked segregant analysis identify SNP markers linked to leaf shape traits in Pisum sativum using SLAF sequencing. Frontiers in Genetics, 9, 615.Zvereva, E. L., & Kozlov, M. V. (2021). Biases in ecological research: attitudes of scientists and ways of control. Scientific Reports, 11(1), 226.EstudiantesInvestigadoresMaestrosPúblico generalResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84707/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL79643755.2023.pdf79643755.2023.pdfTesis de Doctorado en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf11361842https://repositorio.unal.edu.co/bitstream/unal/84707/2/79643755.2023.pdf487c919836a17cbe27a8d46a3097f1b1MD52THUMBNAIL79643755.2023.pdf.jpg79643755.2023.pdf.jpgGenerated Thumbnailimage/jpeg5354https://repositorio.unal.edu.co/bitstream/unal/84707/3/79643755.2023.pdf.jpg24c04404032f1aa66b6c82fbafd01b13MD53unal/84707oai:repositorio.unal.edu.co:unal/847072023-09-13 23:03:31.14Repositorio Institucional Universidad Nacional de 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