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

By A. Ardeshir Goshtasby

A definitive and accomplished evaluation of present literature and the main innovative applied sciences within the box of snapshot registration. rather well geared up and written. essential for laptop experts.

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Edge contours in 2-D become edge surfaces in 3-D, so edge detection by curve fitting becomes edge detection by surface fitting. Edges in 3-D, however, form complex surface structures, which cannot easily be represented by parametric surfaces. Edge detection in 3-D by surface fitting is possible if implicit surfaces are used [34, 292]. For images containing simple objects with spherical or cylindrical topologies, superquadrics [21], hyperquadrics [69], and rational Gaussian surfaces [165] have been used.

B)–(d) Edges detected by the LoG operator, the Canny method, and the intensity ratios, respectively. In all cases, the standard deviation of the Gaussian smoother was 2 pixels, and weak edges were interactively removed to keep about the same number of edges by the three methods. 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)] .

13d shows edges obtained by the intensity ratios. Again, the standard deviation of the Gaussian smoother was 2 pixels and the weak edges were interactively removed to keep the same number of edges as those found by the Canny method and the LoG operator. The majority of edges determined by the three methods are the same. There are some differences between the edges detected by the three methods. 7 Edge detection in color images In gray scale images, sharp changes in intensities are considered edges.

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