Open AccessCausal Features Extraction for Workpiece
Author(s) -
Li Liu,
Chen Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012190
Subject(s) - computer science , filter (signal processing) , noise (video) , artificial intelligence , position (finance) , factory (object oriented programming) , feature extraction , operator (biology) , feature (linguistics) , scale space , rotation (mathematics) , limit (mathematics) , artificial neural network , data processing , data mining , pattern recognition (psychology) , computer vision , image processing , mathematics , mathematical analysis , biochemistry , chemistry , linguistics , philosophy , finance , repressor , transcription factor , economics , image (mathematics) , gene , programming language , operating system
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.