Scale space radon transform-based inertia axis and object central symmetry estimation
This work addresses image analysis tasks for computer vision applications, but it is incremental as it builds on existing SSRT methods for specific geometric estimations.
The paper tackles the problem of estimating the main axis of inertia and central symmetry of objects in images by using the Scale Space Radon Transform (SSRT), showing that with an appropriate scale parameter, the SSRT maximum aligns with the inertia axis and enabling successful classification of objects as centrally symmetric or not on datasets.
Inertia Axes are involved in many techniques for image content measurement when involving information obtained from lines, angles, centroids... etc. We investigate, here, the estimation of the main axis of inertia of an object in the image. We identify the coincidence conditions of the Scale Space Radon Transform (SSRT) maximum and the inertia main axis. We show, that by choosing the appropriate scale parameter, it is possible to match the SSRT maximum and the main axis of inertia location and orientation of the embedded object in the image. Furthermore, an example of use case is presented where binary objects central symmetry computation is derived by means of SSRT projections and the axis of inertia orientation. To this end, some SSRT characteristics have been highlighted and exploited. The experimentations show the SSRT-based main axis of inertia computation effectiveness. Concerning the central symmetry, results are very satisfying as experimentations carried out on randomly created images dataset and existing datasets have permitted to divide successfully these images bases into centrally symmetric and non-centrally symmetric objects.