The most basic feature of an image is edge, which is the junction of one attribute area and another attribute area in the image. It is the most uncertain place in the image and the place where the image information is most concentrated. The edge of an image contains rich information. So, the edge location plays an important role in image processing, and its positioning method directly affects the image effect. In order to further improve the accuracy of edge location for multidimensional image, an edge location method for multidimensional image based on edge symmetry is proposed. The method first detects and counts the edges of multidimensional image, sets the region of interest, preprocesses the image with the Gauss filter, detects the vertical edges of the filtered image, and superposes the vertical gradient values of each pixel in the vertical direction to obtain candidate image regions. The symmetry axis position of the candidate image region is analyzed, and its symmetry intensity is measured. Then, the symmetry of vertical gradient projection in the candidate image region is analyzed to verify whether the candidate region is a real edge region. The multidimensional pulse coupled neural network (PCNN) model is used to synthesize the real edge region after edge symmetry processing, and the result of edge location of the multidimensional image is obtained. The results show that the method has strong antinoise ability, clear edge contour, and precise location.

中文翻译：

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基于边缘对称算法的多维图像边缘定位方法

图像最基本的特征是边缘，它是图像中一个属性区域和另一个属性区域的交界处。它是图像中最不确定的地方，也是图像信息最集中的地方。图像的边缘包含丰富的信息。因此，边缘定位在图像处理中起着重要作用，其定位方法直接影响图像效果。为了进一步提高多维图像边缘定位的精度，提出了一种基于边缘对称性的多维图像边缘定位方法。该方法首先对多维图像的边缘进行检测和计数，设置感兴趣区域，用高斯滤波器对图像进行预处理，检测滤波后图像的垂直边缘，并在垂直方向上叠加每个像素的垂直梯度值以获得候选图像区域。分析候选图像区域的对称轴位置，并测量其对称强度。然后，分析候选图像区域中垂直梯度投影的对称性，以验证候选区域是否为真正的边缘区域。采用多维脉冲耦合神经网络（PCNN）模型合成边缘对称处理后的真实边缘区域，得到多维图像的边缘定位结果。结果表明，该方法抗噪能力强，边缘轮廓清晰，定位准确。然后，分析候选图像区域中垂直梯度投影的对称性，以验证候选区域是否为真正的边缘区域。利用多维脉冲耦合神经网络（PCNN）模型合成边缘对称处理后的真实边缘区域，得到多维图像的边缘定位结果。结果表明，该方法抗噪能力强，边缘轮廓清晰，定位准确。然后，分析候选图像区域中垂直梯度投影的对称性，以验证候选区域是否为真正的边缘区域。采用多维脉冲耦合神经网络（PCNN）模型合成边缘对称处理后的真实边缘区域，得到多维图像的边缘定位结果。结果表明，该方法抗噪能力强，边缘轮廓清晰，定位准确。