Name
Abstract | ||
|---|---|---|
Determining the orientation of a shape is a common task in many image processing applications. It is usually part of the image preprocessing stages and further processing may rely on an adequate method to determine the orientation. There are several methods for computing the orientation of a shape, each of them with its own strengths and weaknesses; a method which performs outstandingly for one application may have a poor performance for a different application. In this paper we present a new method for computing shape orientation based on the projection of the tangent vectors of a shape onto a line and weighting them using a function of the curvature. Some of the results from Zunic (2008) [14] are particular cases of the results presented here. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patcog.2010.03.026 | Pattern Recognition |
Keywords | Field | DocType |
poor performance,image processing application,shape orientation,particular case,tangent vector,different application,common task,new method,curvature weighted gradient,adequate method,own strength,shape,gradient,orientation,image processing | Gradient method,Computer vision,Weighting,Curvature,Pattern recognition,Tangent vector,Early vision,Image processing,Preprocessor,Artificial intelligence,Strengths and weaknesses,Mathematics | Journal |
Volume | Issue | ISSN |
43 | 9 | Pattern Recognition |
Citations | PageRank | References |
2 | 0.36 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Martinez-Ortiz | 1 | 25 | 6.54 |
| Joviša unić | 2 | 62 | 5.16 |