Contribuciones al reconocimiento de objetos desde primitivas de elementos de contorno

En esta tesis se proponen nuevos modelos de deformación de contornos en espacios de veov para la resolución de problemas de emparejamiento de objetos en visión artificial así mismo. Estos modelos se generalizan para modelar su evolución temporal y realizar aplicaciones de seguimiento de objetos en secuencias de imágenes. Los resultados presentados incluyen la aplicación de diferentes tipos de filtros dinámicos tanto lineales como no lineales lo que permite su aplicación en un amplio rango de problemas

A Subset Approach to Contour Tracking in Clutter

A new method for tracking contours of moving objects in clutter is presented. For a given object, a model of its contours is learned from training data in the form of a subset of contour space. Greater complexity is added to the contour model by analyzing rigid and non-rigid transformations of contours separately. In the course of tracking, multiple contours may be observed due to the presence of extraneous edges in the form of clutter; the learned model guides the algorithm in picking out the correct one. The algorithm, which is posed as a solution to a minimization problem, is made efficient by the use of several iterative schemes. Results applying the proposed algorithm to the tracking of a flexing finger and to a conversing individual's lips are presented. 1. Problem Motivation and Review This work addresses the tracking of moving contours in a video-stream. Specifically, given a sequence of images in which a known object of interest is in motion, the goal is to track the object's..