  In this study, we propose methods for the automatic detection of photospheric
features (bright points and granules) from ultra-violet (UV) radiation, using a
feature-based classifier. The methods use quiet-Sun observations at 214 nm and
525 nm images taken by Sunrise on 9 June 2009. The function of region growing
and mean shift procedure are applied to segment the bright points (BPs) and
granules, respectively. Zernike moments of each region are computed. The
Zernike moments of BPs, granules, and other features are distinctive enough to
be separated using a support vector machine (SVM) classifier. The size
distribution of BPs can be fitted with a power-law slope -1.5. The peak value
of granule sizes is found to be about 0.5 arcsec^2. The mean value of the
filling factor of BPs is 0.01, and for granules it is 0.51. There is a critical
scale for granules so that small granules with sizes smaller than 2.5 arcsec^2
cover a wide range of brightness, while the brightness of large granules
approaches unity. The mean value of BP brightness fluctuations is estimated to
be 1.2, while for granules it is 0.22. Mean values of the horizontal velocities
of an individual BP and an individual BP within the network were found to be
1.6 km/s and 0.9 km/s, respectively. We conclude that the effect of individual
BPs in releasing energy to the photosphere and maybe the upper layers is
stronger than what the individual BPs release into the network.
