问题描述
通过图像识别和分割，我已经获得了野外植物的抽象表示（即我完全知道图像中所有植物的所有坐标）。
现在，我想以这种抽象表示法检测作物行-并且还不太清楚该怎么做。 我的问题是：
- 图像中的行可能会稍微旋转，而不能完全沿北/南方向旋转（角度可能会在-10°到+ 10°之间变化）
- 每幅图像的作物行数可以随每幅图像而变化，并且不是固定的-在进行处理之前也是未知的
- 每个处理过的图像中作物行的旋转可能略有不同
- 我有数百个图像/表示要处理（因此手工完成显然不是切实可行的:-)），所以我需要一种以后可以使用的算法陷入循环
您是否可以至少通过某种策略（或代码片段）来帮助我进行此类作物行检测？理想情况下，最后对于每个作物行，我都会有一个线性方程式的参数（y = m * x + t），以便可以使用abline()，但是我可以接受任何东西。最后，它看起来可能像这样（这里纯粹是出于手工目的）：
图像的基本数据在这里：
structure(c(5278.072,2632.564,393.34,4057.704,3805.599,611.269,1823.835,3359.069,3598.284,5262.873,2069.963,1579.745,4539.584,3579.977,4296.46,1831.153,2333.835,1126.639,152.948,4030.205,3368.738,2066.733,855.111,2579.665,3092.37,1318.357,1109.438,3578.606,375.756,3796.788,4520.064,1807.36,5001.773,87.272,4033.594,836.708,639.13,3105.628,1569.256,2831.851,826.444,3557.598,1078.643,576.266,4789.585,3091.929,5239.658,1099.954,1807.972,2534.677,4271.841,5019.276,2053.246,1536.071,3347.644,4019.766,3793.392,5257.628,604.323,2561.307,1792.665,884.25,109.456,3066.108,3750.833,4511.819,2815.08,119.468,4499.801,2582.512,2822.354,3773.842,1054.719,4251.171,4002.476,2018.277,1775.284,4959.269,2541.009,4742.312,2265.149,3071.313,1779.218,3972.64,2822.409,5217.848,1265.449,1522.899,3057.732,5364.729,346.341,4226.012,3287.299,1767.18,3991.963,1811.498,2785.251,4488.214,822.509,2016.435,3022.344,2528.079,4470.315,3017.716,572.771,97.748,5168.119,4199.643,2006.285,3946.505,2771.626,3495.94,1745.531,3734.241,3265.819,4963.116,1058.788,300.408,1252.845,4453,5411.107,2768.93,557.806,2004.424,2218.582,4214.073,4698.292,5149.238,4953.886,1238.343,3502.518,2753.044,5417.502,1031.945,2518.901,1483.487,4450.737,2258.484,289.261,2987.945,5156.371,4171.407,1995.901,781.96,3918.94,1974.667,316.758,1470.993,5160.868,3237.828,521.251,787.228,1039.416,1202.261,3456.837,4148.167,2200.492,2720.912,4915.451,3902.744,4435.419,1209.418,1471.057,4641.269,3913.51,5412.672,1953.878,2220.277,4911.249,1006.368,2974.173,4410.827,1688.391,293.729,1462.871,4618.785,5150.904,2689.061,1952.56,5389.383,2176.387,995.073,4125.245,498.978,5137.266,5358.118,1444.34,1674.431,2689.288,2465.351,4566.352,765.125,1196.984,1687.859,258.247,1914.911,4575.408,3421.147,495.879,979.079,1922.943,4097.704,737.439,3410.562,234.74,2159.697,471.983,1418.991,2440.575,1942.708,1162.525,5312.409,2162.656,5059.814,1411.412,4558.905,247.618,4319.106,3411.827,1786.69,1670.462,1180.524,1640.636,4715.993,3576.548,3566.57,3589.872,3565.564,3531.571,3415.178,3511.07,3510.051,3487.762,3470.791,3443.062,3369.329,3386.999,3387.786,3277.473,3376.266,3421.932,3387.869,3367.994,3346.403,3259.785,3296.081,3297.633,3285.163,3300.119,2941.504,3264.344,3277.9,3235.499,3198.869,3235.508,3156.907,3221.313,3123.96,3165.979,3186.806,3148.158,3129.906,3035.963,2987.899,3053.684,3050.107,3052.643,3037.767,3037.525,2994.456,3006.454,2960.606,2973.443,2919.843,2917.246,2939.87,2914.804,2886.588,2920.769,2906.616,2908.866,2868.052,2885.769,2860.088,2801.168,2853.439,2853.863,2847.141,2805.677,2806.183,2718.094,2661.652,2695.19,2656.518,2612.372,2603.286,2602.449,2591.63,2595.714,2593.287,2575.333,2572.15,2476.559,2435.917,2538.626,2514.215,2458.875,2477.5,2385.366,2421.47,2220.899,2397.842,2396.848,2393.501,2352.039,2292.429,2315.84,2328.682,2256.508,2236.925,2192.809,2241.279,2144.107,2195.016,2185.86,2112.28,2098.085,2020.843,1971.232,1979.691,1968.859,1943.755,1974.743,1891.801,1944.186,1951.423,1872.022,1928.441,1880.504,1912.82,1893.822,1878.889,1850.38,1834.762,1851.886,1806.117,1776.713,1682.26,1733.805,1714.941,1700.778,1686.258,1703.367,1549.601,1682.525,1563.277,1632.103,1609.4,1621.888,1587.126,1545.346,1537.933,1542.424,1366.974,1494.822,1498.618,1494.055,1450.098,1407.89,1345.613,1388.68,1380.527,1368.772,1372.391,1161.35,1297.577,1312.849,1304.972,1286.721,1292.485,1257.53,1241.146,1263.164,1217.146,1226.615,993.046,1166.837,1112.254,1072.249,1117.723,1061.758,1098.207,1084.597,1059.916,1059.685,1063.814,1054.735,944.2,982.653,963.989,969.55,941.066,907.014,930.988,776.849,877.918,889.259,805.872,831.361,803.752,786.654,791.649,814.271,794.444,776.833,694.969,664.718,653.238,661.703,652.696,655.997,637.118,539.101,555.694,491.482,459.712,453.73,490.567,391.441,409.506,319.697,391.505,390.46,308.658,310.59,285.799,268.86,245.89,195.933,243.418,214.203,172.129,173.754,191.456,194.795,98.098,99.4479999999999,62.1419999999998),.Dim = c(224L,2L))
解决方法
以下内容可能会有所帮助：
对于每个检测到的植物点，找到最近的邻近植物。希望这会经常发现同一作物行中的植物。如果先验地知道图像大致在北/南方向，我们应该更喜欢在垂直方向上看更多，以选择相邻的植物。一种方法是将“距离”重新定义为最近的邻居搜索，例如各向异性
distance = 10 * (x0 - x1)² + (y0 - y1)²
这里是产生的图，在每个植物与其最近的邻居之间划了一条线段：
这并不完美，但可能是一个有用的开始。大多数农作物行都很幸运，可以将四棵或更多棵植物正确地链接在一起。
从这里开始思考可能的策略：
-
确定连接的组件，即植物的“链”。
-
对于每个链，以最小二乘法回归最佳拟合线。或者更好的方法是使用RANSAC algorithm，以使拟合健壮地忽略原本在共线链中的单个杂散植物。
-
再次使用大致的北/南方向，仅在最接近垂直线的情况下才考虑最佳拟合线“有效”。假设它是有效的，找到所有接近最佳拟合线的植物。如果附近有很多植物，则最合适的行可能是作物行。