Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6937559 | Computer Vision and Image Understanding | 2016 | 14 Pages |
Abstract
We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Michael Villamizar, AnaÃs Garrell, Alberto Sanfeliu, Francesc Moreno-Noguer,