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

By A. Ardeshir Goshtasby

A complete source at the basics and state-of-the-art in snapshot registration This complete ebook presents the correct theories and underlying algorithms had to grasp the fundamentals of picture registration and to find the state-of-the-art strategies utilized in scientific purposes, distant sensing, and business functions. 2-D and 3-D photo Registration starts with definitions of major phrases after which presents a close exam-ple of picture registration, describing each one serious step. subsequent, preprocessing suggestions for picture registration are mentioned. The center of the textual content provides insurance of all of the key innovations had to comprehend, implement,and overview numerous picture registration equipment. those key equipment contain: * function choice * characteristic correspondence * Transformation services * overview equipment * snapshot fusion * picture mosaicking

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Extra resources for 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications

Example text

SUMMARY u(x, y) · v(x, y) = 39 ∂R(x, y) ∂R(x, y) ∂G(x, y) ∂G(x, y) + ∂x ∂y ∂x ∂y + ∂B(x, y) ∂B(x, y) . 63) will show the gradient magnitude at (x, y). Knowing the gradient direction and gradient magnitude at each image pixel, the edges are detected by locating image pixels with locally maximum gradient magnitudes in the gradient direction. An example of edges determined in this manner in a color image is shown in Fig. 14. The original image is shown in Fig. 14a and the obtained edges are shown in Fig.

5 directions at two sides of a ridge point have opposite signs and the gradient magnitudes at ridge points vary rather slowly. When walking along a ridge contour,if change in the gradient of the ridge contour is greater than the gradient in the direction normal to it, the edge contour representing the ridge will not be detected and the edge contour will be fragmented. To avoid an edge contour from being fragmented, locally minimum gradients that are connected from both sides to locally maximum gradients should also be considered as edges and kept [95].

11d. Comparing edges determined by curve fitting and by the LoG operator,we see that edges detected by curve fitting are longer and smoother, while edges obtained by the LoG operator are better positioned. Since edge contours obtained by curve fitting are in continuous form, it is possible to generate them in an arbitrary scale. 5 Edge detection by functional approximation In this method an image is considered a single-valued surface and the local shape of the surface is approximated by a biquadratic patch.

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