【摘要】 提出了一种归一化图像矩阵奇异值分解来实现焊接目标的自动识别。为消除奇异值对图像方位和比例的依赖,首先采用图像的二阶矩确定出焊接目标的主轴方向和长度,然后将目标图像坐标旋转到主轴方向,并按主轴长度进行尺寸归一化处理后再进行奇异值分解,这样获得的图像奇异值将保持不变,可以用作焊接目标识别的特征匹配。采用传统的直接奇异值分解和归一化奇异值分解对不同角度不同比例的平板焊件图像进行了对比识别试验,同时还研究了图像灰度改变对奇异值的影响。结果表明,归一化奇异值分解对目标图像的平移、旋转、比例和灰度变化表现了良好的稳定性,能够用于焊接目标的自动识别。更多还原

【Abstract】 A normalized singular value decomposition(SVD)of image matrix was presented for the automatic recognition of welding targets.In order to eliminate the dependence of SVD on the orientation and scale of a target image,the direction and length of the major axis of the target image were first determined using the 2ed-order moments,and the x-coordinate of the target image was rotated to the direction of major axis and the length of major axis was normalized,and then the SVD of the target image was completed,and the singular value obtained by the normalized SVD method can be used as the feature matching of welding target recognintion because of its stability.The contrastive recognition experiments were made using the conventional direct SVD and proposed normalized SVD for a planar weldment image with different orientations and scales.In addition,the effect of the target image brightness on SVD was also investigated.The experimental results shown that the normalized SVD method has high stability to the translation,rotation,scale and brightness of target images,and can be applied to the automatic recognition of welding targets.更多还原