Fast Normalized Cross Correlation for Real Time Automatic Counting Objects System
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Abstract
Manufacturers produces a large quantity of objects like bottles, seeds, bolts. The counting of this objects takes a lot of time when a large order is received. Automatic counting system can be used for such type of bulk orders. This reduces the chances of human errors in counting. Using human labor has occurred for a long time but errors and mistakes happen. In the present age of increasing demand in productivity the factory process, counting automation technique is necessary to help people and increase efficiency in the workplace. A sensor device has been used to automatically counting the products on conveyor belts. That is simple for the generally opaque products. And the interval distance of the object is adequately aligned together. However, sensor devices are still limited. It cannot count all products. Therefore, we are applying the principles of image processing to help in counting objects on a conveyor belt moving in real time by using template matching with fast normalized cross correlation. The experimental results indicate that our proposed approach offers significant performance improvements in the automatic counting products, with the maximum of 98.8%.
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