This accessible textbook presents an introduction to computer vision algorithms for industrially-relevant applications of X-ray testing. Features: introduces the mathematical background for monocular and multiple view geometry; describes the main techniques for image processing used in X-ray testing; presents a range of different representations for X-ray images, explaining how these enable new features to be extracted from the original image; examines a range of known X-ray image classifiers and classification strategies; discusses some basic concepts for the simulation of X-ray images and presents simple geometric and imaging models that can be used in the simulation; reviews a variety of applications for X-ray testing, from industrial inspection and baggage screening to the quality control of natural products; provides supporting material at an associated website, including a database of X-ray images and a Matlab toolbox for use with the bookas many examples.Listing 4.9 : Region Growing % PencaseRegionGrowing.m close all X = Xloadimg(a#39;Ba#39;, 3, 4); X = imresize(X, 0.35); % input ... sj = 190; % seed Y = Xregiongrowing(X, th, [sj si]); % segmentation of the selected region figure(1) imshow(X, ); title(a#39;input imagea#39;); hold ... regiona#39;); figure(3) Xbinview(X, bwperim(Y) );title(a#39;edges of the regiona#39;); The output of this code is shown in Fig. ... In our example, a region is segmented using a very simple classifier (the features of a segmented region must be inanbsp;...
|Title||:||Computer Vision for X-Ray Testing|
|Publisher||:||Springer - 2015-07-24|