Abstract:
Depth estimation has turned into an emerging and challenging eld of research
in computer vision. The ubiquitous availability of stereo display technology has
increased the demand of visual media with depth information. All major block-
buster movies and games are now being released with stereo versions. Strict pa-
rameters are required for generating stereo image in multiview set up. Thus the
high-complexity of multiview imaging arrangement of capturing stereo image has
motivated researchers to nd a robust method of estimating depth from conven-
tional 2D imaging set up. In addition to that 3D display technology has evolved
into an advanced stage, but huge amount of media are still in 2D, in such a case
depth estimation from monocular image is the only solution for generating a stereo
view. Hence, research e orts are ongoing to develop low-complexity depth estima-
tion algorithm for a scene specially from its monoscopic images captured using a
CCD camera. Existing depth calculation methods from monocular images include
depth from motion, depth form geometry and depth estimation using a learned
database. These methods are limited by object geometry, prior knowledge of the
scene as well as highly prone to noise. So, there is still a search for robust and
autonomous depth calculation algorithm which does not depend on speci c scene
classes.
Motivated by the noise robust and invariant properties of orthogonal moments to
the geometry of objects, this thesis presents a new moment based depth estimation
method, which is independent of any prior knowledge about the scene. In par-
ticular, the Gaussian-Hermite moments (GHMs) which are very popular in visual
signal processing are chosen to estimate the focus cue of a pixel from its neighbor-
hood. It is known that there exists a signi cant correlation among the neighboring
pixels in terms of depth information except for the sharp edges of an object. Hence
a closed from expression of image matting is applied on the focus map of the im-
age to generate the desired depth map. Extensive experiments are carried out
in order to compare the proposed GHM-based depth estimation method with the
existing methods using commonly-referred images in the literature. Performance
comparisons of depth estimation in terms of visual quality, stereo generation and
mean opinion score show that the proposed method performs signi cantly better
than other methods.
