matchKeypoints matches keypoints detected in two separate
images. This is useful to find common features for image registration, for
instance.
Usage
matchKeypoints(
source,
target,
descriptor_matcher = "BruteForce-Hamming",
match_frac = 0.15
)Arguments
- source, target
Single-channel
Imageobjects containing the BRIEF descriptors of the source and target images, as produced byORBkeypoints.- descriptor_matcher
A character string indicating the type of the descriptor matcher to use. It can be one of the followings: "BruteForce", "BruteForce-L1", "BruteForce-Hamming" (the default), "BruteForce-Hamming(2)", or "FlannBased".
- match_frac
The fraction of top matches to keep (default: 0.15).
Value
A three-column matrix with the identities of the keypoints matched between the source and target images, and the distance between them (a lower distance indicates a better match).
Author
Simon Garnier, garnier@njit.edu
Examples
balloon1 <- image(system.file("sample_img/balloon1.png", package = "Rvision"))
balloon2 <- image(system.file("sample_img/balloon2.png", package = "Rvision"))
kp1 <- ORBkeypoints(balloon1, n_features = 40000)
kp2 <- ORBkeypoints(balloon2, n_features = 40000)
matchKeypoints(kp1$descriptors, kp2$descriptors, match_frac = 1)
#> source target distance
#> [1,] 2 10 16
#> [2,] 7 11 16
#> [3,] 5 9 22
#> [4,] 34 33 27
#> [5,] 33 31 29
#> [6,] 40 35 31
#> [7,] 28 30 33
#> [8,] 91 102 34
#> [9,] 95 105 35
#> [10,] 8 15 35
#> [11,] 41 60 38
#> [12,] 78 94 38
#> [13,] 43 61 40
#> [14,] 16 36 42
#> [15,] 54 40 42
#> [16,] 59 40 42
#> [17,] 4 98 42
#> [18,] 45 64 43
#> [19,] 38 33 43
#> [20,] 56 76 43
#> [21,] 96 5 44
#> [22,] 35 33 44
#> [23,] 79 94 44
#> [24,] 81 100 44
#> [25,] 6 24 45
#> [26,] 44 63 46
#> [27,] 88 101 46
#> [28,] 50 70 48
#> [29,] 21 56 48
#> [30,] 103 19 48
#> [31,] 65 79 48
#> [32,] 92 96 49
#> [33,] 64 19 49
#> [34,] 52 79 49
#> [35,] 66 19 50
#> [36,] 26 60 50
#> [37,] 47 41 51
#> [38,] 83 83 52
#> [39,] 32 43 52
#> [40,] 76 93 53
#> [41,] 60 43 54
#> [42,] 51 24 56
#> [43,] 42 34 56
#> [44,] 46 40 56
#> [45,] 68 4 57
#> [46,] 53 43 58
#> [47,] 19 25 58
#> [48,] 80 59 58
#> [49,] 75 88 60
#> [50,] 67 81 60
#> [51,] 36 106 62
#> [52,] 22 56 62
#> [53,] 12 89 62
#> [54,] 17 24 62
#> [55,] 74 88 62
#> [56,] 85 90 63
#> [57,] 27 127 63
#> [58,] 93 121 63
#> [59,] 14 37 63
#> [60,] 102 19 64
#> [61,] 117 126 65
#> [62,] 39 99 65
#> [63,] 115 10 65
#> [64,] 18 24 65
#> [65,] 94 98 65
#> [66,] 89 95 66
#> [67,] 61 75 66
#> [68,] 63 76 66
#> [69,] 3 27 66
#> [70,] 58 17 66
#> [71,] 97 45 67
#> [72,] 20 27 67
#> [73,] 29 93 67
#> [74,] 30 41 67
#> [75,] 70 86 67
#> [76,] 104 17 68
#> [77,] 84 90 68
#> [78,] 86 92 68
#> [79,] 62 41 69
#> [80,] 107 40 69
#> [81,] 10 89 69
#> [82,] 37 99 69
#> [83,] 23 25 69
#> [84,] 98 76 69
#> [85,] 25 18 69
#> [86,] 57 76 70
#> [87,] 105 98 70
#> [88,] 11 54 70
#> [89,] 90 30 70
#> [90,] 31 90 71
#> [91,] 99 17 71
#> [92,] 119 61 72
#> [93,] 9 26 72
#> [94,] 112 13 73
#> [95,] 120 26 73
#> [96,] 15 37 73
#> [97,] 24 43 73
#> [98,] 72 40 73
#> [99,] 1 40 73
#> [100,] 118 127 74
#> [101,] 100 30 74
#> [102,] 48 4 74
#> [103,] 73 62 75
#> [104,] 106 127 75
#> [105,] 69 29 77
#> [106,] 55 100 77
#> [107,] 114 10 79
#> [108,] 116 125 79
#> [109,] 13 109 79
#> [110,] 49 77 80
#> [111,] 77 23 81
#> [112,] 111 35 83
#> [113,] 113 3 83
#> [114,] 108 17 84
#> [115,] 87 29 84
#> [116,] 109 93 85
#> [117,] 71 14 85
#> [118,] 82 1 87
#> [119,] 110 122 88
#> [120,] 101 33 89