A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry analysis of line drawings using the Hough transform
Pattern Recognition Letters
International Journal of Computer Vision
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
A hough transform technique for the detection of reflectional symmetry and skew-symmetry
Pattern Recognition Letters
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
A Region-Based Method for Model-Free Object Tracking
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
A Dynamic Programming Based Algorithm for Optimal Edge Detection in Medical Images
MIAR '01 Proceedings of the International Workshop on Medical Imaging and Augmented Reality (MIAR '01)
Fast Radial Symmetry for Detecting Points of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Color-based Tracking by Kalman Particle Filter
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Detecting symmetry and symmetric constellations of features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Segmentation and modeling of visually symmetric objects by robot actions
International Journal of Robotics Research
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Bilateral symmetry is a salient visual feature of many man-made objects. In this paper, we present research that uses bilateral symmetry to identify, segment and track objects in real time using vision. Apart from the assumption of symmetry, the algorithms presented do not require any object models, such as color, shape or three-dimensional primitives. In order to counter the high computational cost of traditional symmetry detection methods, a novel computationally efficient algorithm is proposed. To investigate symmetry as an object feature, our fast detection scheme is applied to the tasks of object detection, segmentation and tracking. We find that objects with a line of symmetry can be segmented without relying on color or shape models by using a dynamic programming approach. Object tracking is achieved by estimating symmetry line parameters using a Kalman filter. The tracker operates at 40 frames per second on 640 x 480 video while running on a standard laptop PC. We use 10 difficult real-world tracking sequences to test our approach. We also quantitatively analyze symmetry as a tracking feature by comparing detected symmetry lines against ground truth. Color tracking is also performed to provide a qualitative comparison.