CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2012 vol.34 Issue No.03 - March
Issue No.03 - March (2012 vol.34)
M. Muhammad , Signal & Image Process. Lab., Gwangju Inst. of Sci. & Technol., Gwangju, South Korea
Tae-Sun Choi , Signal & Image Process. Lab., Gwangju Inst. of Sci. & Technol., Gwangju, South Korea
Shape from focus (SFF), which relies on image focus as a cue within sequenced images, represents a passive technique in recovering object shapes in scenes. Although numerous methods have been recently proposed, less attention has been paid to particular factors affecting them. In regard to SFF, one such critical factor impacting system application is the total number of images. A large data set requires a huge amount of computation power, whereas decreasing the number of images causes shape reconstruction to be crude and erroneous. The total number of images is inversely proportional to interframe distance or sampling step size. In this paper, interframe distance (or sampling step size) criteria for SFF systems have been formulated. In particular, light ray focusing is approximated by the use of a Gaussian beam followed by the formulation of a sampling expression using Nyquist sampling. Consequently, a fitting function for focus curves is also obtained. Experiments are performed on simulated and real objects to validate the proposed schemes.
shape recognition, curve fitting, Gaussian processes, image sampling, image sequences, optical focusing, optical microscopy, focus curve fitting function, optical microscopy, shape from focus sampling, SFF sampling, image focus, image sequence, object shape recovery, shape reconstruction, interframe distance, sampling step size, light ray focusing, Gaussian beam, Nyquist sampling, Shape, Optical imaging, Mathematical model, Approximation methods, Optical sensors, Lenses, curve fitting., Sampling step size, shape from focus, 3D shape recovery, optical microscopy
M. Muhammad, Tae-Sun Choi, "Sampling for Shape from Focus in Optical Microscopy", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 3, pp. 564-573, March 2012, doi:10.1109/TPAMI.2011.144