Abstract
This paper presents a new technique to derive features for images that are translated, scaled equally/unequally and rotated. The problem is formulated using conventional regular moments. It is shown that the conventional regular moment-invariants remain no longer invariant when the image is scaled unequally in the x- and y-directions. A method is proposed to form moment-invariants that do not change under such unequal scaling. The newly formed moments are also invariant to translation, and reflection. However, it is not invariant for images that are rotated. A neural network is trained to estimate the angle of rotation and by using it invariant moments for images that are unequally scaled, translated and rotated are derived. Computer simulation results are also included to show the validity of the method proposed.
| Original language | English |
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| Title of host publication | IEEE International Conference on Systems, Man and Cybernetics 1997 |
| Pages | 3158-3163 |
| Number of pages | 6 |
| Volume | 4 |
| Publication status | Published - 1997 |
| Externally published | Yes |
| Event | IEEE International Conference on Systems, Man and Cybernetics 1997 - Orlando, United States of America Duration: 12 Oct 1997 → 15 Oct 1997 https://ieeexplore.ieee.org/xpl/conhome/4942/proceeding?isnumber=13619 (Proceedings) |
Publication series
| Name | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
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| ISSN (Print) | 0884-3627 |
Conference
| Conference | IEEE International Conference on Systems, Man and Cybernetics 1997 |
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| Abbreviated title | SMC 1997 |
| Country/Territory | United States of America |
| City | Orlando |
| Period | 12/10/97 → 15/10/97 |
| Internet address |