Publication
Outdoor view recognition based on landmark grouping and logistic regression
Journal Article (2013)
Journal
International Journal of Pattern Recognition and Artificial Intelligence
Pages
1-21
Volume
27
Number
3
Doc link
http://dx.doi.org/10.1142/S0218001413550045
File
Abstract
Vision-based robot localization outdoors has remained more elusive than its indoors counterpart. Drastic illumination changes and the scarceness of suitable landmarks are the main difficulties. This paper attempts to surmount them by deviating from the main trend of using local features. Instead, a global descriptor called landmark-view is defined, which aggregates the most visually-salient landmarks present in each scene. Thus, landmark co-occurrence and spatial and saliency relationships between them are added to the single landmark characterization, based on saliency and color distribution. A suitable framework to compare landmark-views is developed, and it is shown how this remarkably enhances the recognition performance, compared against single landmark recognition. A view-matching model is constructed using logistic regression. Experimentation using 45 views, acquired outdoors, containing 273 landmarks, yielded good recognition results. The overall percentage of correct view classification obtained was 80.6%, indicating the adequacy of the approach.
Categories
computer vision, image matching, mobile robots, robot vision.
Scientific reference
E. Todt and C. Torras. Outdoor view recognition based on landmark grouping and logistic regression. International Journal of Pattern Recognition and Artificial Intelligence, 27(3): 1-21, 2013.
Follow us!