The Computation of Visible-Surface Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Optimal Infinite Impulse Response Edge Detection Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Surface Reconstruction Using Refined Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Derived From B-Splines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Implementation of Scale-Space by Interpolatory Subdivision Scheme
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection in images using clustering algorithms
TELE-INFO'05 Proceedings of the 4th WSEAS International Conference on Telecommunications and Informatics
Illumination invariant face alignment using multi-band active appearance model
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Hi-index | 0.15 |
An image and surface representation based on regularization theory is introduced in this paper. This representation is based on a hybrid model derived from the physical membrane and plate models. The representation, called the 驴驴-representation, has two dimensions; one dimension represents smoothness or scale while the other represents the continuity of the image or surface. It contains images/surfaces sampled both in scale space and the weighted Sobolev space of continuous functions. Thus, this new representation can be viewed as an extension of the well-known scale space representation. We have experimentally shown that the proposed hybrid model results in improved results compared to the two extreme constituent models, i.e., the membrane and the plate models. Based on this hybrid model, a generalized edge detector (GED) which encompasses most of the well-known edge detectors under a common framework is developed. The existing edge detectors can be obtained from the generalized edge detector by simply specifying the values of two parameters, one of which controls the shape of the filter (驴) and the other controls the scale of the filter (驴). By sweeping the values of these two parameters continuously, one can generate an edge representation in the 驴驴 space, which is very useful for developing a goal-directed edge detection scheme for a specific task. The proposed representation and the edge detector have been evaluated qualitatively and quantitatively on several different types of image data such as intensity, range, and stereo images.