Using symmetry for detecting and locating objects in a picture
Computer Vision, Graphics, and Image Processing
Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Real-time attention for robotic vision
Real-Time Imaging - Special issue on natural and artificial real-time imaging and vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preprocessing of face images: detection of features and pose normalization
Computer Vision and Image Understanding
Communications of the ACM - Robots: intelligence, versatility, adaptivity
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Attentional Landmarks and Active Gaze Control for Visual SLAM
IEEE Transactions on Robotics
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Simultaneous Localization and Mapping (SLAM) based on visual information is a challenging problem. One of the main problems with visual SLAM is to find good quality landmarks, that can be detected despite noise and small changes in viewpoint. Many approaches use SIFT interest points as visual landmarks. The problem with the SIFT interest points detector, however, is that it results in a large number of points, of which many are not stable across observations. We propose the use of local symmetry to find regions of interest instead. Symmetry is a stimulus that occurs frequently in everyday environments where our robots operate in, making it useful for SLAM. Furthermore, symmetrical forms are inherently redundant, and can therefore be more robustly detected. By using regions instead of points-of-interest, the landmarks are more stable. To test the performance of our model, we recorded a SLAM database with a mobile robot, and annotated the database by manually adding ground-truth positions. The results show that symmetrical regions-of-interest are less susceptible to noise, are more stable, and above all, result in better SLAM performance.