Medium classification method and apparatus thereof
A classification method and technology of a classification device, which are applied to devices for accepting coins, inspection of the authenticity of banknotes, instruments, etc., can solve the problems of time-consuming classification, unfavorable to improve the efficiency of serial number recognition, etc., and achieve rapid and stable classification and reduction mode. Types, effects on circulation and regulation
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Embodiment 1
[0030] figure 1 It is a flowchart of the implementation of the medium classification method provided by the embodiment of the present invention, and is described in detail as follows:
[0031] In step S101, acquire banknote images;
[0032] In step S102, in the banknote image, cut out the digital image of the serial number;
[0033] In step S103, the digital image is processed using a skeleton thinning algorithm and a pre-established black pixel point value table;
[0034] Black pixels are: pixels whose color is black.
[0035] In step S104, digital recognition is carried out to the processed digital image;
[0036] In step S105, when the result of digital recognition is digital defacement, the serial number of the banknote image is classified as a defaced serial number.
[0037] When no number is identified or two numbers are recognized, the result of digital recognition is digital defacement, and the serial number of the banknote image is classified as a defaced serial n...
Embodiment 2
[0050] The embodiment of the present invention describes the implementation process of establishing a black pixel point value table, which is described in detail as follows:
[0051] Before processing the digital image using the skeleton thinning algorithm and the pre-established black pixel point value table, the medium classification method further includes:
[0052] Assign different values to the 8 areas around a black pixel to establish a black pixel value table.
[0053] According to the 8 fields of black pixels, there are two cases of black pixels and white pixels in any area of the 8 fields, so the number of combinations is 2 to the 8th power, and all cases can be mapped to a 0-255 in the index table.
[0054] Refer to Table 1. Table 1 is a better sample table of the black pixel point value table when the black pixel point is 0. The details are as follows:
[0055] Table 1
[0056] 1
2
4
8
0
16
32
64
128
[0057...
Embodiment 3
[0061] Figure 6 It is a flowchart of the implementation of step S103 of the medium classification method provided by the embodiment of the present invention, and is described in detail as follows:
[0062] In step S601, in the digital image, locate black pixels;
[0063] In step S602, use the pre-established black pixel point value table to extract the values of 8 areas around the positioned black pixel point;
[0064] In step S603, according to the values of the 8 areas around the black pixel point, using the skeleton thinning algorithm, it is judged whether the black pixel point is a boundary point and the connected component does not increase after the black pixel point is removed;
[0065] In step S604, when the black pixel point is a boundary point and the connected component does not increase after the black pixel point is removed, the black pixel point is deleted.
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