Multifractal characterisation and classification of bread crumb digital images
Adequate models of the bread crumb structure can be critical for understanding flow and transport processes in bread manufacturing, creating synthetic bread crumb images for photo-realistic rendering, evaluating similarities, and establishing quality features of different bread crumb types. In this...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/15160 |
| Acceso en línea: | http://hdl.handle.net/11336/15160 |
| Access Level: | acceso abierto |
| Palabra clave: | Fractal Multifractal Image Analysis Image Classification Feature Extraction https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | Adequate models of the bread crumb structure can be critical for understanding flow and transport processes in bread manufacturing, creating synthetic bread crumb images for photo-realistic rendering, evaluating similarities, and establishing quality features of different bread crumb types. In this article, multifractal analysis, employing the multifractal spectrum (MFS), has been applied to study the structure of the bread crumb in four varieties of bread (baguette, sliced, bran, and sandwich). The computed spectrum can be used to discriminate among bread crumbs from different types. Also, high correlations were found between some of these parameters and the porosity, coarseness, and heterogeneity of the samples. These results demonstrate that the MFS is an appropriate tool for characterising the internal structure of the bread crumb, and thus, it may be used to establish important quality properties it should have. The MFS has shown to provide local and global image features that are both robust and low-dimensional, leading to feature vectors that capture essential information for classification tasks. Results show that the MFS-based classification is able to distinguish different bread crumbs with very high accuracy. Multifractal modelling of the underlying structure can be an appropriate method for parameterising and simulating the appearance of different bread crumbs. |
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