Machine Vision: Algorithms, Architectures, and Systems |
Contents
Introducing Local Autonomy to Processor Arrays | 31 |
Integrating Vision Modules on a FineGrained | 57 |
Computer Architectures for Machine Vision | 97 |
Copyright | |
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algorithm analysis applications architectures array Artificial Intelligence Automation autonomy Besl boundaries camera circuit complexity component Computer Vision Connection Machine constraints contours curve described developed edge detection elements encoding example filament filter flaw Gaussian geometric global goal node graph grid junctions hardware hypercube IEEE image data image plane image processing implemented input integrated label image machine vision machine vision systems manipulator Markov Random Field matching measures method microcode noise object operations optical flow order R-TREE orientation parallel path Pattern Recognition performance pipeline pixels position primitives problem processors projected pyramid R-TREES range images region growing representation represented Research resolution robot sample scan scene seed region segmentation selected sensor sequence shape shown in Figure SIMD smart sensing space spatial stripes surface fitting surface normal surface structures surface type label task techniques texture threshold tion Tomaso Poggio vision modules visual