Imaging Systems

Download e-book for kindle: 2-D and 3-D Image Registration: for Medical, Remote Sensing, by A. Ardeshir Goshtasby

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

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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 [98] 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. [32]. A survey and comparison of edge detection methods is provided by Heath et al. [186]. Edge detection by functional approximation has been described by Haralick [180] using bicubic surfaces. A class of iterative energy minimizing methods proposed by Kass et al. [220] 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].

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