Amelia Simó
James I University
49 Papers
253 Citations
Amelia Simó is an academic researcher from James I University. The author has contributed to research in topics: Reproducing kernel Hilbert space & Shape analysis (digital geometry). The author has an hindex of 13, co-authored 47 publications.
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Papers
Life cycle assessment of ceramic tiles. Environmental and statistical analysis
TL;DR: In this paper, a life cycle assessment study of single-fired glazed stoneware was conducted to identify the stages that produce the greatest impact on the environment and the materials and processes that make the largest contribution to that impact.
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Bayesian detection of the fovea in eye fundus angiographies
María Ibáñez,Amelia Simó +1 more
TL;DR: The proposed methodology is based on Bayesian statistical methods that allow to incorporate the previous knowledge about the eye fundus in the model, and is applied to diAerent cases of diabetic retinopathy and vein occlusions.
63
Consumer behaviour and environmental education in the field of waste electrical and electronic toys: A Spanish case study
TL;DR: This paper reports on a project focused on obtaining the current consumption and disposal habits of electrical and electronic toys from a survey aimed at parents of children of nine pre- and primary schools, and identifying the most effective way of transmitting environmental information to parents and children to promote the collection of electricaland electronic toys at their end-of-life.
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Erythrocyte shape classification using integral-geometry-based methods
TL;DR: Good results are obtained in the automatic classification of erythrocytes in normal cells, sickle cells, and cells with other deformations, when a set of functions based on integral-geometry methods, an active contour-based segmentation method, and a k-NN classification algorithm are used.
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The $$k$$k-means algorithm for 3D shapes with an application to apparel design
TL;DR: This paper proposes to adapt the classical Lloyd algorithm to the context of Shape Analysis, focusing on the three dimensional case and presents a study comparing its performance with the Hartigan-Wong $$k$$k-means algorithm, one that was previously adapted to the field of Statistical Shape Analysis.
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