Affine moment invariants: a new tool for character recognition
Pattern Recognition Letters
Noise and intensity invariant moments
Pattern Recognition Letters
The Method of Normalization to Determine Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
An Algorithm for Binary Contour Objects Representation and Recognition
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Robust stamps detection and classification by means of general shape analysis
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
General shape analysis applied to stamps retrieval from scanned documents
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
A new algorithm for 3D shape recognition by means of the 2D point distance histogram
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
An algorithm for the automatic estimation of image orientation
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Application of 2D fourier descriptors and similarity measures to the general shape analysis problem
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Estimation of position and radius of light probe images
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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The general shape analysis is a problem similar to the recognition or retrieval of shapes. The most important difference is that the processed object does not have to belong to a base class, but usually is only similar to the template representing the class. The most general information about a shape is here concluded, i.e. how round, elliptical, triangular, etc. it is. Such a problem can occur in applications with few general base classes, e.g. in pre-classification or the assignment of stamps extracted from an image to few classes in order to find the fraudulent stamp images (mainly governmental, official ones). In the paper seven shape descriptors were explored using the template matching approach. In order to select the best approach their performance was compared with results provided by almost two hundred humans and collected using appropriate inquiry forms.