Skeletonization of sparse shapes using dynamic competitive neural networks

La detección de regiones y objetos en imágenes digitales es un tema de suma importancia en la resolución de numerosos problemas correspondientes al área de reconocimiento de patrones. En esta dirección los algoritmos de esqueletización son una herramienta muy utilizada ya que permiten reducir la cantidad de información disponible facilitando la extracción de características para su posterior reconocimiento y clasificación. Además, esta transformación de la información original en sus características esenciales, facilita la eliminación de ruidos locales presentes en la entrada de datos. Este artículo propone una nueva estrategia de esqueletización aplicable a imágenes esparcidas a partir de una red neuronal competitiva dinámica entrenada con el método AVGSOM. La estrategia desarrollada en este trabajo determina los arcos que forman el esqueleto combinando el aprendizaje no supervisado del AVGSOM con un árbol de dispersión mínima (minimun spaning tree). El método propuesto ha sido aplicado en imágenes con diferente forma y grado de dispersión. En particular, los resultados obtenidos han sido comparados con soluciones existentes mostrando resultados satisfactorios. Finalmente se presentan algunas conclusiones así como algunas líneas de trabajo futurasThe detection of regions and objects in digital images is a topic of utmost importance for solving several problems related to the area of pattern recognition. In this direction, skeletonization algorithms are a widely used tool since they allow us to reduce the quantity of available data, easing the detection of characteristics for their recognition and classification. In addition, this transformation of the original data in its essential characteristics eases the elimination of local noise which is present in the data input. This paper proposes a new skeletonization strategy applicable to sparse images from a competitive, dynamic neural network trained with the AVGSOM method. The strategy developed in this paper determines the arc making up the skeleton combining AVGSOM non-supervised learning with a minimum spanning tree. The proposed method has been applied in images with different spanning shape and degree. In particular, the results obtained have been compared to existing solutions, showing successful results. Finally, some conclusions, together with some future lines of work, are presented.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Skeletonization of sparse shapes using dynamic competitive neural networks

The detection of regions and objects in digital images is a topic of utmost importance for solving several problems related to the area of pattern recognition. In this direction, skeletonization algorithms are a widely used tool since they allow us to reduce the quantity of available data, easing the detection of characteristics for their recognition and classification. In addition, this transformation of the original data in its essential characteristics eases the elimination of local noise which is present in the data input. This paper proposes a new skeletonization strategy applicable to sparse images from a competitive, dynamic neural network trained with the AVGSOM method. The strategy developed in this paper determines the arc making up the skeleton combining AVGSOM non-supervised learning with a minimum spanning tree. The proposed method has been applied in images with different spanning shape and degree. In particular, the results obtained have been compared to existing solutions, showing successful results. Finally, some conclusions, together with some future lines of work, are presented.Facultad de Informátic

Skeletonization of sparse shapes using dynamic competitive neural networks

La detección de regiones y objetos en imágenes digitales es un tema de suma importancia en la resolución de numerosos problemas correspondientes al área de reconocimiento de patrones. En esta dirección los algoritmos de esqueletización son una herramienta muy utilizada ya que permiten reducir la cantidad de información disponible facilitando la extracción de características para su posterior reconocimiento y clasificación. Además, esta transformación de la información original en sus características esenciales, facilita la eliminación de ruidos locales presentes en la entrada de datos. Este artículo propone una nueva estrategia de esqueletización aplicable a imágenes esparcidas a partir de una red neuronal competitiva dinámica entrenada con el método AVGSOM. La estrategia desarrollada en este trabajo determina los arcos que forman el esqueleto combinando el aprendizaje no supervisado del AVGSOM con un árbol de dispersión mínima (minimun spaning tree). El método propuesto ha sido aplicado en imágenes con diferente forma y grado de dispersión. En particular, los resultados obtenidos han sido comparados con soluciones existentes mostrando resultados satisfactorios. Finalmente se presentan algunas conclusiones así como algunas líneas de trabajo futurasThe detection of regions and objects in digital images is a topic of utmost importance for solving several problems related to the area of pattern recognition. In this direction, skeletonization algorithms are a widely used tool since they allow us to reduce the quantity of available data, easing the detection of characteristics for their recognition and classification. In addition, this transformation of the original data in its essential characteristics eases the elimination of local noise which is present in the data input. This paper proposes a new skeletonization strategy applicable to sparse images from a competitive, dynamic neural network trained with the AVGSOM method. The strategy developed in this paper determines the arc making up the skeleton combining AVGSOM non-supervised learning with a minimum spanning tree. The proposed method has been applied in images with different spanning shape and degree. In particular, the results obtained have been compared to existing solutions, showing successful results. Finally, some conclusions, together with some future lines of work, are presented.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Letter-Level Shape Description by Skeletonization in Faded Documents

We present a method for determining the skeletal shape description for letters in texts faded due to ageing and/or poor ink quality. The proposed algorithm is interesting in that it neither involves assumptions about demarcation of object regions from the background, nor does it require pixel connectivity in the text regions. Consequently, it may be applied for obtaining the shape descriptions of "sparse" regions, which are characteristic of letters in faded documents. Given the pixel distribution for a letter or a word from a faded document, the method involves an iterative evolution of a piecewise-linear approximation of the principal curve of this pixel distribution. By constraining the principal curve to lie on the edges of the Delaunay triangulation of the shape distribution, the adjacency relationships between regions in the shape can be detected and used in evolving the skeleton. The approximation of the principal curve, on convergence, gives the final skeletal shape. The skelet..