Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Mobile Robot Localisation Using Active Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Preemptive RANSAC for Live Structure and Motion Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Search for Real-Time Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving the Agility of Keyframe-Based SLAM
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A duality based approach for realtime TV-L1 optical flow
Proceedings of the 29th DAGM conference on Pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Editors Choice Article: Visual SLAM: Why filter?
Image and Vision Computing
Three-dimensional SLAM for mapping planetary work site environments
Journal of Field Robotics
Jointly compatible pair linking for visual tracking with probabilistic priors
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
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In the feature matching tasks which form an integral part of visual tracking or SLAM (Simultaneous Localisation And Mapping), there are invariably priors available on the absolute and/or relative image locations of features of interest. Usually, these priors are used post-hoc in the process of resolving feature matches and obtaining final scene estimates, via 'first get candidate matches, then resolve' consensus algorithms such as RANSAC or JCBB. In this paper we show that the dramatically different approach of using priors dynamically to guide a feature by feature matching search can achieve global matching with far fewer image processing operations and lower overall computational cost. Essentially, we put image processing into the loop of the search for global consensus. In particular, our approach is able to cope with significant image ambiguity thanks to a dynamic mixture of Gaussians treatment. In our fully Bayesian algorithm denoted Active Matching, the choice of the most efficient search action at each step is guided intuitively and rigorously by expected Shannon information gain. We demonstrate the algorithm in feature matching as part of a sequential SLAM system for 3D camera tracking with a range of settings, and give a detailed analysis of performance which leads to performance-enhancing approximations to the full algorithm.