基于四元数的改进型最小核值相似区边缘检测

针对基于亮度和色度的彩色图像边缘检测在检测过程中忽略亮度和色度之间关联性而导致部分边缘不能有效地被检测出来的问题,提出了一种基于四元数的改进型最小核值相似区(SUSAN)边缘检测算法。首先,利用四元数矢量旋转原理将HSI颜色空间的三维信息映射成二维平面信息实现空间降维,同时引入标量V来综合表示H、S、I三通道之间的关系;然后,将标量V作为算子的核函数;最后,利用改进的SUSAN算子完成图像的边缘检测。实验结果表明,提出的算法针对色度相同、饱和度存在差异以及饱和度相同、色度存在差异的彩色图像,在边缘检测的定位误差率上降低了1.5%。在实际的应用中,能够更好地获得图像中的目标信息,同时也为后续的分割和识别研究提供更好的先验知识。

Quaternion-based edge detection by improved smallest univalue segment assimilating nucleus

The color image edge detection based on luminance and chrominance ignores the correlation between luminance and chrominance in the process of detecting edge, which leads to some edges can not be effectively detected. An edge detection method based on quaternion using improved Smallest Univalue Segment Assimilating Nucleus (SUSAN) was proposed. Firstly, spatial dimension reduction was realized through turning 3D information of HSI color space into 2D information, which based on the vector rotation principle of quaternion, meanwhile, the introduction of the scalar V was used to comprehensively express the three-channel relationship among H, S and I; secondly, kernel function of the algorithm was represented by the scalar V; finally, edge detection was completed by the improved SUSAN. The experimental results show that for the color images that have the same chrominance and different saturation, or have the same saturation and deviational chrominance, the presented algorithm reduces the location error rate by 1.5%. The proposed method can get better information of the target in the image of real applications, and provide better prior knowledge for the segmentation and recognition in the subsequent research.