Affiliations: [a] Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece | [b] Department of Digital Systems, University of Piraeus, Piraeus, Greece
Correspondence:
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Corresponding author: K.K. Delibasis, Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece. E-mail:[email protected]
Abstract: A significant number of Computer Vision and Artificial Intelligence
applications are based on descriptors that are extracted from segmented
objects. One widely used class of such descriptors are the invariant
moments, with Zernike moments being reported as some of the most efficient
descriptors. The calculation of image moments requires the definition of
distance and angle of any pixel from the centroid pixel of a specific
object. While this is straightforward in images acquired by projective
cameras, the classic definition of distance and angle may not be applicable
to omni-directional images obtained by fish-eye cameras. In this work, we
provide an efficient definition of distance and angle between pixels in
omni-directional images, based on the calibration model of the acquisition
camera. Thus, a more appropriate calculation of moment invariants from
omni-directional videos is achieved in time domain. A large dataset of
synthetically generated binary silhouettes, as well as segmented human
silhouettes from real indoor videos are used to assess experimentally the
effectiveness of the proposed Zernike descriptors in recognising different
poses in omni-directional video. Comparative numerical results between the
traditional Zernike moments and the moments based on the proposed
corrections of the Zernike polynomials are presented. Results from other
state of the art image descriptors are also included. Results show that the
proposed correction in the calculation of Zernike moments improves pose
classification accuracy significantly. The computational complexity of the
proposed implementation is also discussed.
Keywords: Pattern recognition, computer vision, image descriptors, Zernike moment invariants, Omni-directional image/video, fish-eye camera calibration, silhouette pose recognition
