Title :
Improving object position estimation based on non-linear mapping using Relevance Vector Machine
Author :
Robles-Castro, Jesus ; Duchén-Sánchez, Gonzalo ; Takahashi, Haruhisa
Author_Institution :
SEPI, ESIME, Culhuacan, Mexico
fDate :
Feb. 28 2011-March 2 2011
Abstract :
The objective of the proposed work is object position estimation, in which the system, after training with examples of images including objects such as cars, should be capable of indicating accurately by coordinates. The method is different from simple object detection, since it uses the context, i.e. the whole image. The key idea is to take an approach with Relevance Vector Machine (RVM) since it leads to sparse models and theoretically better performance is expected compared to previous proposals. The RVM mapping was done first as a training stage, in this case by using the same image database as the conventional method used as comparison with a previous Support Vector Regression proposal, where cars in different positions and sizes are included, and with exact coordinates given explicitly to the system, after this, it can perform without previous training.
Keywords :
learning (artificial intelligence); object detection; image training; nonlinear mapping; object detection; object position estimation; relevance vector machine; Data mining; Image color analysis; Kernel; Proposals; Shape; Spline; Training;
Conference_Titel :
Electrical Communications and Computers (CONIELECOMP), 2011 21st International Conference on
Conference_Location :
San Andres Cholula
Print_ISBN :
978-1-4244-9558-0
DOI :
10.1109/CONIELECOMP.2011.5749355