Unsupervised Learning Using Neural Networks
Image recognition using neural networks
Imagine an image processor unaffected by lighting and optics…
For those of you familiar with machine vision, it is no news that the first key to success of an imaging application resides in its ability to obtain good quality images with the least possible lighting and contextual variations. If you take the example of a glossy object, the distribution of its color intensities will vary depending on the angle of incidence of the light and its wavelength. As a result, the color shades of the object will change as it moves or rotates. Coping with this problem implies the installation of optics, lighting and positioning equipment. The performance of the machine vision application depends on the accuracy and consistency of their calibration. The slightest change of hardware positioning affects the system. Nevertheless, once these settings are defined and frozen, one can start processing images to isolate regions of interest from background regions and measure objects for inspection or recognition. Repeatability and accuracy are achieved… until someone trips over a cable or moves your equipment.
Source: Global Sensing
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