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

Stroke trajectory generation for a robotic Chinese calligrapher.

Lam, Hiu Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 84-89).Abstracts in English and Chinese.Chapter Chapter 1: --- Introduction --- p.1Chapter 1.1. --- Overview on Robotics --- p.1Chapter 1.2. --- Literture Review on Art-Robot --- p.1Chapter 1.3. --- Robot artist for Chinese Calligraphy and Paintings --- p.3Chapter 1.4. --- Motivation and Research Objective --- p.4Chapter 1.5. --- Thesis Outline --- p.5Chapter Chapter 2: --- Intelligent Robotic Art System --- p.6Chapter 2.1. --- Previous Configuration --- p.6Chapter 2.1.1. --- 3 DOF Manipulator --- p.7Chapter 2.1.2. --- Digital Image Input System --- p.7Chapter 2.2. --- Hardware Modification --- p.8Chapter 2.2.1. --- Additional Degree of Freedoms --- p.8Chapter 2.2.2. --- Infra-red Sensing System for Manipulator Positioning --- p.9Chapter 2.2.3. --- Axial-rotary Brush --- p.11Chapter 2.2.4. --- Interface program --- p.13Chapter 2.2.5. --- Vibration Reduction --- p.16Chapter Chapter 3: --- Skeletonization Based on Delaunay Triangulation and Bezier Interpolation --- p.18Chapter 3.1. --- Background Theory --- p.20Chapter 3.1.1. --- Smoothed Local Symmetry --- p.20Chapter 3.1.2. --- Delaunay Triangulation --- p.21Chapter 3.1.3. --- Bezier Curve --- p.23Chapter 3.2. --- Algorithm --- p.24Chapter 3.2.1. --- Edge Sampling --- p.24Chapter 3.2.2. --- Triangle Modification --- p.26Chapter 3.2.3. --- Triangle Filtering and Replacement --- p.28Chapter 3.2.4. --- Internal Edge Refinement --- p.30Chapter 3.2.5. --- Skeletal Interpolation --- p.31Chapter 3.3. --- Experiments --- p.32Chapter 3.4. --- Chapter Summary --- p.36Chapter Chapter 4: --- Stroke Segmentation for Chinese Words --- p.37Chapter 4.1. --- Rule-based Spurious Branches Removal --- p.38Chapter 4.1.1. --- Spurious Branch in Stroke Terminal --- p.40Chapter 4.1.2. --- Spurious Branch Caused by Turning Stroke --- p.42Chapter 4.2. --- Stroke Connectivity Determination --- p.44Chapter 4.2.1. --- Gradient of Medial Axis --- p.45Chapter 4.2.2. --- Gradient of Branch Boundary --- p.47Chapter 4.2.3. --- Branch Width --- p.49Chapter 4.2.4. --- Combined Objective Function --- p.50Chapter 4.3. --- Stroke Generation --- p.51Chapter 4.3.1. --- Stroke Connection between Branches --- p.52Chapter 4.3.2. --- Stroke Generation in Stroke Terminal --- p.53Chapter 4.4. --- Experiment Using Intelligent Robotic Art System --- p.54Chapter 4.5. --- Discussion --- p.59Chapter Chapter 5: --- Experimental Acquisition of Brush Footprints --- p.61Chapter 5.1. --- Brush Footprint Extraction --- p.62Chapter 5.2. --- Graphical Interface for Inputting Sample Points of Brush Footprints --- p.64Chapter 5.3. --- Curve Fitting for Brush Footprint Sample Points --- p.70Chapter 5.3.1. --- Curve Fitting Using Genetic Algorithm --- p.70Chapter 5.3.2. --- Curve Fitting by Least Squares Regression --- p.72Chapter 5.4. --- Discussion --- p.74Chapter Chapter 6: --- Trajectory Generation for Robotic Chinese Calligraphy --- p.75Chapter 6.1. --- Stroke Trajectory Searching with According Stroke Width --- p.75Chapter 6.2. --- Improvement in Stroke Trajectory --- p.77Chapter 6.3. --- Experiment --- p.80Conclusion and Future Work --- p.82References --- p.84Appendix --- p.90Chapter 9.1. --- Segmented Strokes of Bada Shanren's Calligraphy --- p.9