Design of a pattern recognition system in thermographic and footprint images for flatfoot identification in children between five and six years old
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Universidad Distrital Francisco José de Caldas
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The following paper presents the main results of an exploratory research oriented to design and implementation of a pattern recognition system for flatfoot identification in children between 5 and 6 years. Patterns were determined from texture analysis of foot thermographic images, and from contour analysis of footprint images. For each case, an artificial neuronal network was trained, with base in a back propagation algorithm. In each trial, 70% of data were used for training, and 30% for validation. For experiments done, success rates greater than 80% were achieved. The best results was reached with contour patterns reduced by PCA, in a binary system, with a success rate of 90.84% in cross validation. Results are a contribution to study of diagnostic techniques for flatfoot treatment through use of technologic tools.
The following paper presents the main results of exploratory research-oriented to the design and implementation of a pattern recognition system for flatfoot identification in children between 5 and 6 years. Patterns were determined from texture analysis of foot thermographic images, and from contour analysis of footprint images. For each case, an artificial neuronal network was trained, with base in a back-propagation algorithm. In each trial, 70 % of data were used for training, and 30 % for validation. For experiments done, success rates greater than 80 % were achieved. The best results were reached with contour patterns reduced by principal components analysis, PCA, in a binary system, with a success rate of 90.84 % in cross-validation. Results are a contribution to the study of diagnostic techniques for flatfoot treatment through the use of technologic tools.
The following paper presents the main results of exploratory research-oriented to the design and implementation of a pattern recognition system for flatfoot identification in children between 5 and 6 years. Patterns were determined from texture analysis of foot thermographic images, and from contour analysis of footprint images. For each case, an artificial neuronal network was trained, with base in a back-propagation algorithm. In each trial, 70 % of data were used for training, and 30 % for validation. For experiments done, success rates greater than 80 % were achieved. The best results were reached with contour patterns reduced by principal components analysis, PCA, in a binary system, with a success rate of 90.84 % in cross-validation. Results are a contribution to the study of diagnostic techniques for flatfoot treatment through the use of technologic tools.
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flatfoot, texture patterns, footprint patterns, artificial neuronal networks, pie plano, patrones de textura, patrones de huella, redes neuronales artificiales