@article {14189,
	title = {A syntactic approach to scale-space-based corner description},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {16},
	year = {1994},
	month = {1994/07//},
	pages = {748 - 751},
	abstract = {Planar curves are described by information about corners integrated over various levels of resolution. The detection of corners takes place on a digital representation. To compensate for ambiguities arising from sampling problems due to the discreteness, results about the local behavior of curvature extrema in continuous scale-space are employed},
	keywords = {Computer vision, corner detection, curvature extrema, edge detection, IMAGE PROCESSING, image resolution, Image segmentation, Laboratories, Large-scale systems, PARALLEL PROCESSING, pattern recognition, planar curves, resolution, Sampling methods, sampling problems, scale space based corner description, SHAPE, Smoothing methods, syntactic approach},
	isbn = {0162-8828},
	doi = {10.1109/34.297957},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
@conference {14233,
	title = {Multi-resolution shape description by corners},
	booktitle = {, 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1992. Proceedings CVPR {\textquoteright}92},
	year = {1992},
	month = {1992/06/15/18},
	pages = {271 - 276},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A robust method for describing planar curves in multiple resolution using curvature information is presented. The method is developed by taking into account the discrete nature of digital images as well as the discrete aspect of a multiresolution structure (pyramid). The main contribution lies in the robustness of the technique, which is due to the additional information that is extracted from observing the behavior of corners in the whole pyramid. Furthermore, the resulting algorithm is conceptually simple and easily parallelizable. Theoretical results are developed analyzing the curvature of continuous curves in scale-space and showing the behavior of curvature extrema under varying scale. The results are used to eliminate any ambiguities that might arise from sampling problems due to the discreteness of the representation. Experimental results demonstrate the potential of the method},
	keywords = {ambiguities, Automation, computational geometry, Computer vision, continuous curves, corners, curvature extrema, curvature information, curve fitting, digital images, Feature extraction, IMAGE PROCESSING, image resolution, Image segmentation, Laboratories, multiple resolution, multiresolution structure, parallelizable, planar curves, Robustness, Sampling methods, scale-space, SHAPE, Smoothing methods, varying scale},
	isbn = {0-8186-2855-3},
	doi = {10.1109/CVPR.1992.223264},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
