Defect automatic detection for tire X-ray images using inverse transformation of principal component residual

We develop an image reconstruction algorithm in this paper using inverse transformation of principal component residual to automatically detect the tire defects in its X-ray image. A tire X-ray image to be inspected is first transformed to a new coordinate system, i.e. principal component space, in which the k major components and their corresponding eigenvectors represent the dominant normal textures of tires; Unlike traditional principal component analysis, we set the k major component eigenvalues as zeros, then the above principal component space matrix is called principal component residual in this paper, which stands for none-dominant textures of tires that is considered as abnormal or defect textures. We perform inverse transformation of this principal component residual and reconstruct the defect left only image. To localize the defect part, binarization operation is processed on the obtained left over image with upper and lower thresholds statistically. The proposed scheme can both reveal the defect locations and also present defect patch shapes for identifications. The experiment results show the high effectiveness of our scheme in detecting defects for tire X-ray images.