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

By A. Ardeshir Goshtasby

To grasp the basics of photograph registration, there isn't any extra finished resource than 2-D and 3D photo Registration. as well as delving into the proper theories of photo registration, the writer provides their underlying algorithms. you are going to additionally become aware of state-of-the-art strategies to take advantage of in distant sensing, business, and scientific functions. Examples of photograph registration are offered all through, and the significant other site comprises all of the photographs utilized in the e-book and offers hyperlinks to software program and algorithms mentioned within the textual content, permitting you to breed the implications within the textual content and boost photos on your personal learn wishes. 2-D and 3-D snapshot Registration serves as a great textbook for periods in picture registration in addition to a useful operating source.

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Example text

Region boundaries have high gradients and changing the threshold value corresponding to an intensity at region boundaries will not change the region sizes significantly. To find the threshold value where change in pixel count becomes minimum, the number of pixels falling on one side of a threshold value must be determined and pixel count must be tracked as the threshold value is changed. This process is computationally more expensive than finding the average intensity of high gradient pixels, but the obtained threshold value will be optimal [421].

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)] .

Edge detection by intensity ratios responds to changes in surface reflectance and surface normal. Among the edge detection methods discussed in this chapter, the curve fitting method is most expensive, but it enables selection of locally maximum curvature 40 PREPROCESSING points or inflection points as point features. The selection of such point features by traditional means requires fitting curves to the edge contours and locating the curvature maxima or inflection points. 3-D edge detection methods closely follow those of 2-D methods.

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