Virtual models in 3D digital reconstruction: detection and analysis of symmetry

Gothandaraman, Rajkumar; Muthuswamy, Sreekumar

doi:10.1007/s11554-021-01115-w

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…Computing the main axis of inertia can be very useful to characterize some aspects of the object. Indeed, it has been involved in many works focusing on symmetry evaluation like the works presented in [1][2][3][4] or for pedestrian detection and for real time estimation of body posture purposes in [5] and in [6], respectively. In addition, the authors in [7] and in [8], have involved the main axis of inertia computation in medical applications for cross-sectional clinical images analysis purpose and for vessel centerline extraction in retinal image one.…”

Section: Introductionmentioning

confidence: 99%

Scale space radon transform-based inertia axis and object central symmetry estimation

Goumeidane¹,

Ziou²,

Nacereddine³

2023

Preprint

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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.

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