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Robinson compass mask

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negation of the first four results. An edge, or contour is an tiny area with neighboring distinct pixel values. The convolution of each mask with the image would create a high value output where there is a rapid change of pixel value, thus an edge point is found. All the detected edge points would line up as edges.
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Babu C.R., Sreenivasa Reddy E., Prabhakara Rao B. (2015) Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU). In: Mandal J., Satapathy S., Kumar Sanyal M., Sarkar P., Mukhopadhyay A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent
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The direction axis is the line of zeros in the matrix. Robinson compass mask is similar to kirsch compass masks, but is simpler to implement. Since the matrix coefficients only contains 0, 1, 2, and are symmetrical, only the results of four masks need to be calculated, the other four results are the
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S Edy Victor Haryanto, M. Y. Mashor, A. S. Abdul Nasir, H. Jaafar, "A fast and accurate detection of Schizont plasmodium falciparum using channel color space segmentation method", Cyber and IT Service Management (CITSM) 2017 5th International Conference on, pp. 1-4,
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Dr. Borislav D Dimitrov, Dr. Vishal Goyal, Mr. Nehinbe Joshua, Mr. Vassilis Papataxiarhis "International Journal of Computer Science Issues(Volume 8, Issue1)", IJCSI PUBLICATION, Jan 2011,
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An example of Robinson compass masks applied to the original image. Obviously, the edges in the direction of the mask is enhanced.
237: 141: 252: 221: 216: 195: 174: 280: 275: 242: 1245: 19: 1081:{\displaystyle {\text{North East:}}{\begin{bmatrix}-2&-1&0\\-1&0&1\\0&1&2\end{bmatrix}}} 895:{\displaystyle {\text{South East:}}{\begin{bmatrix}0&-1&-2\\1&0&-1\\2&1&0\end{bmatrix}}} 709:{\displaystyle {\text{South West:}}{\begin{bmatrix}2&1&0\\1&0&-1\\0&-1&-2\end{bmatrix}}} 523:{\displaystyle {\text{North West:}}{\begin{bmatrix}0&1&2\\-1&0&1\\-2&-1&0\end{bmatrix}}} 211: 131: 802:{\displaystyle {\text{South:}}{\begin{bmatrix}1&0&-1\\2&0&-2\\1&0&-1\end{bmatrix}}} 430:{\displaystyle {\text{North:}}{\begin{bmatrix}-1&0&1\\-2&0&2\\-1&0&1\end{bmatrix}}} 988:{\displaystyle {\text{East:}}{\begin{bmatrix}-1&-2&-1\\0&0&0\\1&2&1\end{bmatrix}}} 616:{\displaystyle {\text{West:}}{\begin{bmatrix}1&2&1\\0&0&0\\-1&-2&-1\end{bmatrix}}} 343:
The Robinson compass mask is defined by taking a single mask and rotating it to form eight orientations:
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Systems and Computing, vol 340. Springer, New Delhi
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curvature 998: 905: 812: 719: 626: 533: 440: 347: 1146:Robinson South Mask 1114:Robinson North Mask 230:Feature description 1172: 1164: 1162:Robinson East Mask 1156: 1148: 1140: 1132: 1130:Robinson West Mask 1124: 1116: 1108: 1078: 1072: 985: 979: 892: 886: 799: 793: 706: 700: 613: 607: 520: 514: 427: 421: 271:Scale-space axioms 1222:978-1-4398-0206-9 1004: 911: 818: 725: 632: 539: 446: 353: 317: 316: 20:Feature detection 1253: 1246:Image processing 1225: 1214: 1208: 1204: 1198: 1195: 1189: 1185: 1087: 1085: 1084: 1079: 1077: 1076: 1005: 1002: 994: 992: 991: 986: 984: 983: 912: 909: 901: 899: 898: 893: 891: 890: 819: 816: 808: 806: 805: 800: 798: 797: 726: 723: 715: 713: 712: 707: 705: 704: 633: 630: 622: 620: 619: 614: 612: 611: 540: 537: 529: 527: 526: 521: 519: 518: 447: 444: 436: 434: 433: 428: 426: 425: 354: 351: 321:image processing 309: 302: 295: 191:Structure tensor 183:Structure tensor 75:Corner detection 16: 1261: 1260: 1256: 1255: 1254: 1252: 1251: 1250: 1231: 1230: 1229: 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detection 315: 314: 312: 311: 304: 297: 289: 286: 285: 284: 283: 278: 273: 265: 264: 258: 257: 256: 255: 250: 245: 240: 232: 231: 227: 226: 225: 224: 222:Hessian affine 219: 214: 206: 205: 201: 200: 199: 198: 193: 185: 184: 180: 179: 178: 177: 172: 164: 163: 159: 158: 152: 151: 150: 149: 144: 139: 134: 129: 121: 120: 118:Blob detection 114: 113: 112: 111: 106: 101: 96: 91: 89:Shi and Tomasi 86: 78: 77: 71: 70: 69: 68: 63: 58: 53: 48: 43: 38: 30: 29: 27:Edge detection 23: 22: 13: 10: 9: 6: 4: 3: 2: 1258: 1247: 1244: 1242: 1239: 1238: 1236: 1223: 1219: 1213: 1210: 1203: 1200: 1194: 1191: 1184: 1181: 1174: 1168: 1160: 1152: 1144: 1136: 1128: 1120: 1112: 1104: 1100: 1094: 1092: 1088: 1073: 1067: 1062: 1057: 1050: 1045: 1040: 1037: 1030: 1025: 1022: 1017: 1014: 1008: 980: 974: 969: 964: 957: 952: 947: 940: 937: 932: 929: 924: 921: 915: 887: 881: 876: 871: 864: 861: 856: 851: 844: 841: 836: 833: 828: 822: 794: 788: 785: 780: 775: 768: 765: 760: 755: 748: 745: 740: 735: 729: 701: 695: 692: 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631:South West: 445:North West: 262:Scale space 1235:Categories 1175:References 1038:− 1023:− 1015:− 938:− 930:− 922:− 862:− 842:− 834:− 786:− 766:− 746:− 693:− 685:− 670:− 600:− 592:− 584:− 502:− 494:− 474:− 404:− 384:− 364:− 331:used for 281:Pyramids 61:Robinson 1095:Example 56:Prewitt 41:Deriche 1224:(2010) 1220:  724:South: 352:North: 1207:2017. 910:East: 538:West: 104:SUSAN 51:Sobel 36:Canny 1218:ISBN 323:, a 248:GLOH 243:SURF 238:SIFT 147:PCBR 109:FAST 319:In 253:HOG 1237:: 1074:] 1068:2 1063:1 1058:0 1051:1 1046:0 1041:1 1031:0 1026:1 1018:2 1009:[ 981:] 975:1 970:2 965:1 958:0 953:0 948:0 941:1 933:2 925:1 916:[ 888:] 882:0 877:1 872:2 865:1 857:0 852:1 845:2 837:1 829:0 823:[ 795:] 789:1 781:0 776:1 769:2 761:0 756:2 749:1 741:0 736:1 730:[ 702:] 696:2 688:1 680:0 673:1 665:0 660:1 653:0 648:1 643:2 637:[ 609:] 603:1 595:2 587:1 577:0 572:0 567:0 560:1 555:2 550:1 544:[ 516:] 510:0 505:1 497:2 487:1 482:0 477:1 467:2 462:1 457:0 451:[ 423:] 417:1 412:0 407:1 397:2 392:0 387:2 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Index

Feature detection
Edge detection
Canny
Deriche
Differential
Sobel
Prewitt
Robinson
Roberts cross
Corner detection
Harris operator
Shi and Tomasi
Level curve curvature
Hessian feature strength measures
SUSAN
FAST
Blob detection
Laplacian of Gaussian (LoG)
Difference of Gaussians (DoG)
Determinant of Hessian (DoH)
Maximally stable extremal regions
PCBR
Ridge detection
Hough transform
Generalized Hough transform
Structure tensor
Generalized structure tensor
Affine shape adaptation
Harris affine
Hessian affine

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