A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
User-adaptive hand gesture recognition system with interactive training
Image and Vision Computing
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Describing local patches to register image keypoints is an important task for building a huge database from video frames. When searching for an efficient descriptor, task is twofold: features must describe the featuring patches at a high efficiency, while the dimensionality should be kept at a manageable low value. The main assumption in finding local descriptors is the defect of continuity in the discrete neighborhood or the imperfectness of local shape formats. Curve fitting methods for noisy shapes are called: active contours are generated around keypoints. Local contours are characterized by a small number of Fourier descriptors, resulting a new feature set of low dimensionality. Similarity among different images are searched through these descriptors. The method was tested on 22 real-life video frames made by an outdoor surveillance camera of a city police central.