By A. Ardeshir Goshtasby

ISBN-10: 0471649546

ISBN-13: 9780471649540

ISBN-10: 0471724262

ISBN-13: 9780471724261

A complete source at the basics and state-of-the-art in photograph registration This complete booklet offers the proper theories and underlying algorithms had to grasp the fundamentals of photo registration and to find the state-of-the-art recommendations utilized in clinical purposes, distant sensing, and business purposes. 2-D and 3-D picture Registration starts off with definitions of major phrases after which presents an in depth exam-ple of photo registration, describing each one serious step. subsequent, preprocessing recommendations for photo registration are mentioned. The middle of the textual content offers insurance of all of the key recommendations had to comprehend, implement,and assessment a number of photograph registration tools. those key equipment comprise: * characteristic choice * characteristic correspondence * Transformation capabilities * assessment equipment * photograph fusion * picture mosaicking

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Additional info for 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications

Example text

Therefore, when only locally maximum gradients in the gradient direction are detected, some critical edges are missed, causing a boundary contour to break. By considering locally minimum gradients that are connected from both sides to locally maximum gradients as edges, the edge contour in Fig. 8d is obtained. This edge contour now represents the intended boundary more accurately than the fragmented edge contours shown in Fig. 8b. 3 Edge detection by intensity ratios A 2-D image represents the projection of a 3-D scene onto a plane, recording intensities proportional to brightnesses in the scene.

For a high-contrast image with very little noise, a smaller σ should be used to allow a curve to reproduce sharp corners. An example of edge detection by curve fitting is shown in Fig. 11b. The gradient threshold value was 10, branches in a tree containing more than 10 pixels were cut off and treated as new trees, regions containing fewer than 10 pixels were considered noise and removed from the image, and the standard deviation of the Gaussian was 2 pixels. Edges obtained by the LoG operator with σ = 2 pixels and gradient magnitudes greater than ten are shown in Fig.

This is not surprising since the evaluation was done visually, and the human visual system distinguishes more colors in the Lab color coordinate system than in the RGB coordinate system [188, 221]. 3 Feature Selection Image features are unique image properties that can be used to establish correspondence between two images. The most desired features are points, because their coordinates can be directly used to determine the parameters of a transformation function that registers the images. In some images it may not be possible to detect point features; however, lines or regions may be abundant.

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