Abstract
In order to reduce the cost, computer vision technology is introduced into the measurement of workpiece size and shape on the factory production line. At present, the most widely used solution is the neural network model based on big data. However, the lack of data and the high cost of data processing also greatly limit the practical application of this aspect. The method of feature extraction brings challenges to the real-time, rotation invariance, and anti-noise of online detection. In this paper, firstly, Harris operator is used to extract feature points quickly. Then a two-layer scale space based on causality is constructed to filter the noise and project downward to obtain the robust feature position, which provides a basis for subsequent processing.
Highlights
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With the popularization of automation production in industrial production, a stable and efficient workpiece detection system is more and more important for industrial production
It can be seen that the position of feature extraction has a certain offset
Summary
- Antilinearity rather than Hermiticity as a guiding principle for quantum theory Philip D Mannheim. University of South China, Department of Computer Science and Technology, HengYang, ChangShen Road 28 421001. Abstract.In order to reduce the cost, computer vision technology is introduced into the measurement of workpiece size and shape on the factory production line. The most widely used solution is the neural network model based on big data. The lack of data and the high cost of data processing greatly limit the practical application of this aspect. The method of feature extraction brings challenges to the real-time, rotation invariance, and anti-noise of online detection. Harris operator is used to extract feature points quickly. A two-layer scale space based on causality is constructed to filter the noise and project downward to obtain the robust feature position, which provides a basis for subsequent processing
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