Scale and rotation invariant 3D object detection using spherical nonlinear correlations

Three-dimensional object recognition with scale and rotation changes is addressed. The recognition method is described in terms of correlations between spherical surfaces. Tridimensionality is codified into range images. We used the phase Fourier transform of a range image which gives information about the orientation of the 3D object surfaces. A 3D object orientation map containing information about all possible rotations of the object is obtained. This map distribution is calculated using the amplitude of the phase Fourier transform for different views of the object. From that 3D object description it is possible to achieve detection and rotation estimation by performing a correlation between unit spheres even when only partial information is presented. In addition, a scale change of a rotated 3D object implies a change of the intensity in the unit sphere. We define correlations between the reference unit sphere and a certain target-patch placed on the surface of the unit sphere. Various experiments are carried out to confirm the correct detection. We also validate the method when other false targets were used. In addition to tolerance to scale and rotation, high discrimination against false targets is also achieved.