A computational model for interpretable visual category discovery of foliar shapes

dc.contributor.advisorGómez Jaramillo, Francisco Albeiro
dc.contributor.authorVictorino Guzmán, Jorge Enrique
dc.contributor.cvlacVictorino, Jorgespa
dc.contributor.googlescholarVictorino, Jorgespa
dc.contributor.orcidVictorino, Jorge [0000-0003-3331-4340]spa
dc.contributor.researchgroupCOMBIOSspa
dc.date.accessioned2023-09-13T22:05:41Z
dc.date.available2023-09-13T22:05:41Z
dc.date.issued2023-08-22
dc.descriptionilustraciones, diagramasspa
dc.description.abstractLos 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)spa
dc.description.abstractLeaf 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.eng
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ingenieríaspa
dc.format.extentxx, 75 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/84707
dc.language.isoengspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y Computaciónspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaspa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónspa
dc.subject.ddc510 - Matemáticas::514 - Topologíaspa
dc.subject.ddc570 - Biología::577 - Ecologíaspa
dc.subject.decsHojas de las plantasspa
dc.subject.decsPlant Leaveseng
dc.subject.lembMorfología (botánica)spa
dc.subject.lembBotany - morphologyeng
dc.subject.lembAnatomía vegetalspa
dc.subject.lembBotany - anatomyeng
dc.subject.lembPlant anatomyeng
dc.subject.proposalNovel category discoveryeng
dc.subject.proposalUnsupervised categorizationeng
dc.subject.proposalLeaf shapeeng
dc.subject.proposalContour analysiseng
dc.subject.proposalMorphologicaleng
dc.subject.proposalImage processingeng
dc.subject.proposalTopological analysiseng
dc.subject.proposalImage classificationeng
dc.titleA computational model for interpretable visual category discovery of foliar shapeseng
dc.title.translatedModelo computacional para el descubrimiento de categorías de formas foliaresspa
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TDspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentPúblico generalspa
dcterms.audience.professionaldevelopmentResponsables políticosspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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