Browsing by Author "Undurraga Rius, Cristóbal Alejandro"
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- ItemMejoramiento de algoritmos de seguimiento utilizando modelos de saliencia(2011) Undurraga Rius, Cristóbal Alejandro; Mery Quiroz, Domingo; Pontificia Universidad Católica de Chile. Escuela de IngenieríaUno de los grandes desafíos de la visión por computador es mejorar los sistemas automáticos para la detección y seguimiento de objetos o regiones en un conjunto de imágenes. Un enfoque que ha cobrado importancia recientemente se basa en la extracción de descriptores, tales como el descriptor de covarianza, ya que logran permanecer invariantes en las regiones de estas imágenes a pesar de los cambios de posición, traslación, rotación y escala. Utilizando el mismo descriptor de covarianza proponemos, en este trabajo, un novedoso algoritmo de saliencia, el cual detecta las zonas más importantes de una imagen y es capaz de determinar en una imagen aquella(s) región(es) más relevantes que pueden ser utilizadas tanto en el reconocimiento como en el seguimiento de objetos.
- ItemPerformance Evaluation of the Covariance Descriptor for Target Detection(IEEE, 2009) Cortez Cargill, Pedro Manuel; Undurraga Rius, Cristóbal Alejandro; Mery Quiroz, Domingo; Soto Arriaza, Álvaro MarceloIn computer vision, there has been a strong advance in creating new image descriptors. A descriptor that has recently appeared is the Covariance Descriptor, but there have not been any studies about the different methodologies for its construction. To address this problem we have conducted an analysis on the contribution of diverse features of an image to the descriptor and therefore their contribution to the detection of varied targets, in our case: faces and pedestrians. That is why we have defined a methodology to determinate the performance of the covariance matrix created from different characteristics. Now we are able to determinate the best set of features for face and people detection, for each problem. We have also achieved to establish that not any kind of combination of features can be used because it might not exist a correlation between them. Finally, when an analysis is performed with the best set of features, for the face detection problem we reach a performance of 99%, meanwhile for the pedestrian detection problem we reach a performance of 85%. With this we hope we have built a more solid base when choosing features for this descriptor, allowing to move forward to other topics such as object recognition or tracking.