By A. Ardeshir Goshtasby
A definitive and finished assessment of present literature and the main innovative applied sciences within the box of snapshot registration. rather well prepared and written. essential for machine experts.
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Additional resources for 2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
These corner detectors compute the cornerness measure using the image gradients (ﬁrst-derivative image intensities). The cornerness measure can be deﬁned in terms of the second-derivative image intensities or a combination of the ﬁrst- and secondderivative image intensities. 10) as a cornerness measure to detect control points in images, where Ixx and Iyy are image second derivatives with respect to x and y, respectively, and Ixy is the derivative of image intensities with respect to both x and y.
62) where R(x, y), G(x, y), and B(x, y) are the red, green, and blue color values at pixel (x, y) and r, g, and b are unit vectors along red, green, and blue axes in the 3-D color space. Gradient direction at (x, y) is considered to be the direction θ(x, y) that maximizes  2 F (x, y) = [u(x, y) cos θ(x, y) + v(x, y) sin θ(x, y)] . 63) 38 PREPROCESSING The maximum θ(x, y) can be obtained by ﬁnding the derivative of F (x, y) with respect to θ(x, y), setting it to zero, and solving for θ(x, y).
A survey of thresholding methods is given by Bezdek et al. . A survey and comparison of edge detection methods is provided by Heath et al. . Edge detection by functional approximation has been described by Haralick  using bicubic surfaces. A class of iterative energy minimizing methods proposed by Kass et al.  delineates individual region boundaries. This class of methods, which has proven very effective in segmentation of medical images, has appeared in various forms in both 2-D and 3-D [11, 271, 285, 306, 337].