Algorithms for clustering data
Algorithms for clustering data
International Journal of Computer Vision
Topological mapping for mobile robots using a combination of sonar and vision sensing
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
New results on binary space partitions in the plane
Computational Geometry: Theory and Applications
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Multidimensional binary search trees used for associative searching
Communications of the ACM
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Texture classification using wavelet transform
Pattern Recognition Letters
Learning to autonomously select landmarks for navigation and communication
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Mobile Robotics: A Practical Introduction
Mobile Robotics: A Practical Introduction
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
An automated vision based on-line novel percept detection method for a mobile robot
Robotics and Autonomous Systems
Enhancing minimum spanning tree-based clustering by removing density-based outliers
Digital Signal Processing
Hi-index | 0.10 |
The mechanisms by which humans and animals use visually-acquired landmarks to find their way around have proved fascinating. Considerable evidence suggests that animals navigate not only on the basis of the overall geometry of the space but also on the basis of a configural representation of the cues. In contrast to earlier linear models of elemental feature representation, configural representation requires individual stimulus be represented in the context of other stimuli and is typified by non-linear learning tasks such as the transverse patterning problem. This paper explores the suitability of configural representation for automatic scene recognition in robot navigation by conducting experiments designed to infer semantic prediction of a scene from different configurations of its stimuli. The main contribution of this work is that it provides a methodology for automatic landmark-based scene identification with the aid of a reinforcement learning based software package, called the working memory toolkit (WMtk), which allows reward associations between a target location and the conjunctive representations of its stimuli. Experimental results obtained with two different target locations are presented and compared with those of two other classification mechanisms, a support vector machine approach and a simple linear two-class classifier, perceptron.