El análisis de modelos deformables tridimensionales tiene actualmente un creciente interés para muchos investigadores en el área de geometría computacional. El aumento de escáneres de rango y otros dispositivos que extraen información tridimensional, permite generar gran cantidad de modelos tridimensionales, los cuales tienen aplicaciones directas; por ejemplo: en la medicina, restauración de piezas de arte y nuevas interfaces interactivas, solo por mencionar algunas ´áreas de la industria. En la literatura existen técnicas orientadas a extraer características importantes, seleccionado solo algunos puntos importantes, los cuales serán denominados puntos de interés. Sin embargo, el nivel de granularidad con que es representado el modelo tridimensional mediante puntos de interés, es muy fino y requiere de mayor cantidad de información para representarlo.

Due to the increasing amount of data and the reduction of costs in 3D data acquisition devices, there has been a growing interest, in developing efficient and robust feature extraction algorithms for 3D shapes, invariants to isometric, topological and noise changes, among others. One of the key tasks for feature extraction in 3D shapes is the interest points detection; where interest points are salient structures, which can be used, instead of the whole object. In this research, we present a new approach to detect interest points in 3D shapes by analyzing the triangles that compose the mesh which represent the shape, in different way to other algorithms more complex such as Harris 3D or HKS. Our results and experiments of repeatability, confirm that our algorithm is stable and robust, in addition, the computational complexity is O(n log n), where n represents the number of faces of the m...

Algorithm k-means is useful for grouping operations; however, when is applied to large amounts of data, its computational cost is high. This research propose an optimization of k-means algorithm by using parallelization techniques and synchronization, which is applied to image segmentation. In the results obtained, the parallel k-means algorithm, improvement 50% to the algorithm sequential k-means.

En la presente investigación se propone un enfoque novedoso para extraer características distintivas de imagenes basado en el modelo de color HSV y filtros wavelets, con la finalidad de hacer un agrupamiento de imágenes que son similares entre si, por ejemplo mariposas de la misma especie. Además se investiga la mejor combinacion de características de color y forma. Los experimentos han demostrado un mejor rendimiento en la combinación color con el filtro de Gabor.

Due to the advancement of computing and the power of the new hardware, more economical, it is now feasible to have thousands of images which can be analyzed to allow classification for its shape and/or color. Furthermore, techniques and efficiency of the classification depends on the characteristics to be obtained of images in order to compare and classify them according to their similarity. Some images, such as model cars, planes and boats, can be discriminated by their shape. However, other images such as butterfly species where the shape is similar, the color plays an important role in the discrimination task. In this research we propose a novel approach to extract distinctive features of images by combining the HSV color model and wavelets filters. Furthermore, we investigate the best combination of features color and form. Experiments have shown improved performance by combining the...