Fundamentals of Machine Vision
This text is intended to help readers understand and construct machine vision systems that perform useful tasks, based on the state of the art. It covers fundamentals drawn from image processing and computer graphics to the methods of applied machine vision techniques. The text is useful as a short course supplement, as a self-study guide, or as a primary or supplementary text in an advanced undergraduate or graduate course.
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VISION IN HUMANS AND MACHINES
OBJECTS AND REGIONS
algorithms application axis background boundary centroid chain code Chapter cluster CODECs complex components compression computer graphics computer vision contour coordinates correlation curve data structure descriptors detection determined Digital Image Processing discrete convolution discussed display domain edge edge detection ellipse evaluation example extract fish Fourier transform frame buffer frame grabber frame rate fusion gated video tracker graphic objects graylevels grayscale hardware histogram host computer image data image processing software image sequences imaging system input intensity Khoros light machine vision problem machine vision system mask measure MPEG Myler noise NTSC number of pixels objects and regions operation Optical optimal threshold output pattern perception perform pixel values pliers polygon quantization QuickTime recognition representation resolution retina robot sampling scanning Section segmentation sensors shown in Figure signal spatial filter spatial frequency target techniques television texture tracking UCFImage Video for Windows visual system VITREO window