Resumen de tesis doctoral

Methodologies for Reducing the Amount of Required Images Used for Articled–Object Recognition

Metodologías para la Reducción del Número de Imágenes Requeridas para el Reconocimiento de Objetos Articulados

Graduated: Luis Carlos Altamirano Robles
Instituto Mexicano del Petróleo, PIMAyC
Eje Central Lázaro Cárdenas 152, C.P. 07730
Del. Gustavo A. Madero, México, D. F.
E–mail: laltamir@imp.mx

Advisor 1: Leopoldo Altamirano
Instituto Nacional de Astrofísica, Óptica y Electrónica,
Puebla, Mexico
E–mail: robles@inaoep.mx

Advisor 2: Matías Alvarado Mentado
Instituto Mexicano del Petróleo
e–mail: matiasa@cic.ipn.mx

Graduated on March 19, 2002

Abstract

The appearance–based approaches are such that any object's model is made through a set of training images describing the object's appearance. In this PhD. thesis, the usage of non–uniform sampling is introduced for building this image set. Non–uniform sampling is held by a linear interpolation technique, which is used to determine the strictly necessary images. Main results are: a significant reduction in the quantity of necessary images for the object's model, as well as more precise models than those obtained by uniform sampling. Non–uniform sampling is used in conjunction with the eigenspaces technique for object recognition, producing a more efficient hybrid technique.

Keywords: non–uniform sampling, object recognition, appearance–based model, interpolation, eigenspaces.

Resumen

Los enfoques basados en apariencia construyen el modelo de un objeto, por medio de un conjunto de imágenes de entrenamiento que describe la apariencia del objeto. En esta tesis doctoral se propone el empleo del muestreo no–uniforme para generar tal conjunto de imágenes. El muestreo no–uniforme es soportado mediante una técnica de interpolación, que determina cuáles son las imágenes estrictamente requeridas para construir el modelo. Los resultados principales obtenidos con esta propuesta son: una reducción significativa de la cantidad de imágenes requeridas para construir el modelo del objeto, además de una mejora en la precisión de los modelos así generados, respecto a los obtenidos con muestreo uniforme. El muestreo no–uniforme es empleado junto con la técnica de espacios fundamentales (eigenspaces) para realizar el reconocimiento del objeto, obteniéndose una técnica híbrida más eficiente.

Palabras Clave: non–uniform sampling, object recognition, appearance–based model, interpolation, eigenspaces.

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Acknowledgments

This research was sponsored in part by CONACyT under Ref. 131991 A. L.C. Altamirano was supported by CONACyT graduate scholarship No. 89635. The author would like to thank Dr. Matías Alvarado from Instituto Mexicano del Petróleo (PIMAyC), for reviewing and correcting a preliminary version of this work.

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