概率霍夫變換（Progressive Probabilistic Hough Transform）的原理很簡單，如下所述：
1.隨機獲取邊緣圖像上的前景點，映射到極坐標系畫曲線；
2.當極坐標系里面有交點達到最小投票數，將該點對應x-y坐標系的直線L找出來；
3.搜索邊緣圖像上前景點，在直線L上的點（且點與點之間距離小於maxLineGap的）連成線段，然后這些點全部刪除，並且記錄該線段的參數（起始點和終止點），當然線段長度要滿足最小長度；
4.重復1. 2. 3.。
In "A real-time lane marking localization, tracking and communication system":
Hough transform (HT) was proposed in [30] and is usually used to detect lines and circles; it has been used as the core method of lane marking detection in [3] and [31]. The core formula of HT is:
λ=xcos(θ)+ysin(θ)
λ is the length between the origin and the pedal of detected line and θ is the angle of its perpendicular line.
In [32], Matas et al. proposed the PPHT, which has been commonly accepted as one of the best line detection methods based on Hought transform theory. The algorithm PPHT proceeds as follows:
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Randomly, select a new point for voting in the accumulator array, with contributions to all available bins (as referenced in [32], bin stands for a pair of (λ, θ)). Then remove the selected pixel from the input image.
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Check if the highest peak (the pair of (λ, θ) with the most voting points) in the updated accumulator is greater than a pre-defined threshold th(N). If not then go to Step 1.
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Find all lines with the parameter (λ, θ) which was specified by the peak in Step 2. Choose the longest segment (which can be denoted by starting point Pt0 and ending point Pt1) of all lines.
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Remove all the points of the longest line from the input image.
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Remove all the points of the selected line in Step 3 (Pt0−Pt1) from the accumulator, which means those points do not attend any other voting process.
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If the selected segment is longer than a pre-defined minimum length, then take the segment (Pt0−Pt1) as one of the output results.
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Go to Step 1.
References:
[3] D.O. Cualain, C. Hughes, M. Glavin, E. Jones. Automotive standards-grade lane departure warning system. IET Intell. Transp. Syst., 6 (1) (2012), pp. 44–57
[30] P.V.C. Hough, A method and means for recognizing complex patterns, US Patent: 3,069,654 (1962).
[31] K. Zu. Realtime lane tracking of curved local road. Proceedings of the IEEE Intelligent Transportation Systems (2006), pp. 1149–1155
[32] J. Matas, C. Galambos, J. Kittler. Robust detection of lines using the progressive probabilistic hough transform. Comput. Vision Image Underst., 78 (1) (2000), pp. 119–137