Aprendizaje automático a partir de formas = Machine Learning from Shapes
Author
Muñoz Perera, AdriánAdvisor
Suárez González, AlbertoEntity
UAM. Departamento de Ingeniería InformáticaDate
2018-07Subjects
Directional data; Von Mises distribution; shape clustering; Informática; MatemáticasEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Abstract
In this work the problem of learning from images to perform grouping and classification of shapes
is addressed. The key idea of the shape recognition approach is to encode the instances available
for learning in the form of directional data, that will be used to characterize those instances and
perform comparisons among them. The objects to study are thus 2 and 3 dimensional shapes,
that will be characterized by the distribution of the direction of the normal vectors to the tangent
hyperplanes at the boundary of the shape. In two dimensions, this boundary is a contour,
and these directional data will in fact form a curve, that manipulated as functional data can
be used to encode the shapes in two discrete representations: a normalized histogram and a
kernel density estimation for the probability function. These representations are used to extract
characteristics based on metrics defined in the space of circular distributions, categorize the encoded
shapes and finally compare them. These characterization and comparison techniques will
be later embedded in some clustering and classification algorithms, applying them in a simple
shape recognition problem and a real world problem of clustering and classification with fish
otolith shapes.
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Google Scholar:Muñoz Perera, Adrián
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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/
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