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Development of Phase Congruency to Estimate the Direction of Maximum Information (tDMI) in Images with Straight Line Segments

机译:相一致性地估算直线段图像中的图像中最大信息(TDMI)的方向

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This paper aims to better understand a new property of Phase Congruency (PC) based image processing technique. It is illustrated that by optimizing the PC orientation, the direction that contains maximum information can be detected in images with straight line segments. We formulate the Direction of Maximum Information (tDMI) and develop the PC orientation optimization paradigm to compute it in such images. We also develop the Holmholtz Principle and Hough Transform based Line Segment Detection (HP-LSD and HT-LSD) methods to compute tDMI. We apply these methods to identify tDMI in 32 test images corrupted with gaussian and speckle noises with different variances. All methods show fairly good robustness to the noise variance. However, the results show that the PC orientation optimization method detects tDMI with estimation errors significantly lower than those of HP-LSD and HT-LSD methods. Furthermore, PC estimates tDMI directly, whereas, HT-LSD and HP-LSD require to measure lines' widths and lengths. The worst estimation error is obtained by using HT-LSD method, with an average error 102.81% for images with straight line segments of the same thickness. The average error of HP-LSD method over all 32 test images and noise conditions is 47.58%. The average estimation error of PC method over all scenarios is 15.80%, which shows more than 30% and 70% improvement compared to HP-LSD and HT-LSD methods, respectively. The results also show that it is better to consider both maximum and minimum momentums into the PC orientation optimization.
机译:本文旨在更好地了解基于相中(PC)的图像处理技术的新特性。示出了通过优化PC方向,可以在具有直线段的图像中检测到包含最大信息的方向。我们制定最大信息的方向(TDMI),并开发PC方向优化范例以将其计算在此类图像中。我们还开发Holmholtz原理和霍夫基于线段的线段检测(HP-LSD和HT-LSD)方法来计算TDMI。我们应用这些方法,以识别32次测试图像中的TDMI与具有不同差异的高斯和斑点噪声损坏。所有方法都表明了对噪声方差相当良好的稳健性。然而,结果表明,PC方向优化方法检测TDMI,估计误差明显低于HP-LSD和HT-LSD方法。此外,PC直接估计TDMI,而HT-LSD和HP-LSD需要测量线路的宽度和长度。通过使用HT-LSD方法获得最差的估计误差,其平均误差102.81%,其具有相同厚度的直线段的图像。所有32个测试图像和噪声条件的HP-LSD方法的平均误差为47.58%。与HP-LSD和HT-LSD方法相比,PC方法的平均估计误差为15.80%,显示出超过30%和70%的改进。结果还表明,最好将最大和最小势头视为PC方向优化。

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