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From low to high, information fusion can be divided into three levels. The feature-level fusion not only keeps the most original information, but also overcomes the unstable and large characteristics of original data. Fusion feature can be effectively used in seam image recognition. Firstly, we build the JARI robot system to research the seam tracking from the image identify. Secondly, principal component analysis (PCA) method based on multivariate statistical analysis is used in feature- level fusion. And it is applied in liver B- image recognition. The recognition results are analyzed and compared. Finally, through the gantry robot 9 degree system to verify the logic of the identify V type seam. The experimental results show that fusion feature can fully and effectively express seam image, which can bring better recognition results. Analyzing and comparing the feature selection results of different sample images, the results show that feature selection is stable and effective. Comparing with the results of direct PCA fusion applications, the recognition effect after feature selection is better, not only improves the average accuracy rate of recognition but also reduces the time complexity of the recognition process. It has better performance, can be more effectively applicated in welding image recognition. |
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CITATIONS
Cited by 1 scholarly publication.
Image processing
Image fusion
Image enhancement
Sensors
Robotic systems
Image filtering
Automatic tracking
