Noise and intensity invariant moments
Pattern Recognition Letters
Shape measures for content based image retrieval: a comparison
Information Processing and Management: an International Journal
Deformable Templates for Feature Extraction from Medical Images
ECCV '90 Proceedings of the First European Conference on Computer Vision
Invariant Signatures from Polygonal Approximations of Smooth Curves
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Shape classification using smooth principal components
Pattern Recognition Letters
Polygonal approximation of closed contours
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Recognition of trademarks during sport television broadcasts
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Polar contour shape descriptors in the template matching approach to object recognition
Annales UMCS, Informatica
Polar contour shape descriptors in the template matching approach to object recognition
Annales UMCS, Informatica
An experimental comparison of seven shape descriptors in the general shape analysis problem
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Shape retrieval and recognition on mobile devices
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
| Hi-index | 0.00 |
Representation of an object in image for machine learning applications (recognition, retrieval, identification, etc.) has to be based on a previously chosen feature. Binary shape is a very popular and commendable one. It has many advantages and can be successfully used in many applications, especially in engineering. To achieve better characteristics, various shape transformations are used. Obviously, they should be robust to as many shape deformations as it is possible. In this paper results of exhaustive exploration of a new method are presented. This method is based on transformation from Cartesian to polar coordinates, but it overcomes few problems, that were not solved yet. Above all, the proposed transform is more robust to occlusion and noise, two the most challenging problems.