Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Synthesis of interest point detectors through genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Using Evolution to Learn How to Perform Interest Point Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Coarse-to-fine vision-based localization by indexing scale-Invariant features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimization of the hölder image descriptor using a genetic algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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The basic problem for a mobile vision system is determining where it is located within the world. In this paper, a recognition system is presented that is capable of identifying known places such as rooms and corridors. The system relies on a bag of features approach using locally prominent image regions. Real-world locations are modeled using a mixture of Gaussians representation, thus allowing for a multimodal scene characterization. Local regions are represented by a set of 108 statistical descriptors computed from different modes of information. From this set the system needs to determine which subset of descriptors captures regularities between image regions of the same location, and also discriminates between regions of different places. A genetic algorithm is used to solve this selection task, using a fitness measure that promotes: 1) a high classification accuracy; 2) the selection of a minimal subset of descriptors; and 3) a high separation among place models. The approach is tested on two real world examples: a) using a sequence of still images with 4 different locations; and b) a sequence that contains 8 different locations. Results confirm the ability of the system to identify previously seen places in a real-world setting.