By Asim Bhatti (ed.)
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Sec. 4 describes the main stages of a stereo correspondence system in which MRFS can be applied. Sec. 5 describes the camera model that will be considered in this chapter together with some important related issues like: camera calibration, the epipolar constraint and image rectiﬁcation. Sec. 6 describes the concept of Markov random ﬁelds, and related procedures, like simulated annealing. Sec. 7 introduces MRFs for the edge detection problem. Sec. 8 describes, in detail, how MRFs can 2 36 Stereo Advances in Theory and Applications of StereoVision Vision Correspondence field Pinhole camera−Right Stereo pair Scene Pinhole camera−Left preprocessing Feature extraction Reconstruction/ Reconstructed interpolation scene Feature based correspondence Coarse robust correspondence field Fig.
Kanade (Kanade & Okutomi, 1994) proposed a model of the statistical distribution of the disparity at a point about the center. Such distribution is assumed to be Gaussian with variance proportional to the distance between the points. Nishihara (Faugeras, 1993, sec. 2) proposed an improvement of area-based techniques introducing the use of sign of the Laplacian of Gaussian to reduce the sensitive to noise. , 1986) proposed an algorithm to solve the problem of correspondence on the basis of the limits of the disparity gradient, derived from experiments performed on the human visual system’s (HVS) ability to fuse stereograms.
With just one scaling function and one wavelet, but also in the vector case where there is more than one scaling functions and wavelets are involved. A multi-wavelet basis is characterized by r scaling and r wavelet functions. Here r denotes the multiplicity of the scaling functions and wavelets in the vector setting with r > 1. In case of multiwavelets, the notion of multiresolution changes as the basis for A0 is now generated by the translates of r scaling functions as ⎡ ⎤ φ0 (t) ⎢ φ1 (t) ⎥ ⎢ ⎥ Φ(t) = ⎢ (12) ..
ADVANCES IN THEORY AND APPLICATIONS OF STEREO VISION by Asim Bhatti (ed.)