Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (4): 1345-1356.doi: 10.13229/j.cnki.jdxbgxb20171157

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Extended contour⁃based fast affine invariant feature extracting

Feng⁃wen ZHAI1(),Jian⁃wu DANG1,2,Yang⁃ping WANG1,2,Jing JIN1,Wei⁃wei LUO1   

  1. 1. School of Electronic and Information Engineering, Lanzhou Jiao Tong University, Lanzhou 730070, China
    2. Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing, Lanzhou 730070, China
  • Received:2017-09-26 Online:2019-07-01 Published:2019-07-16

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Abstract:

To overcome the poor classification ability of the fast affine invariant feature extracting algorithm, an extended contour-based fast affine invariant feature extracting algorithm is developed. Firstly, the flaw in the proving and calculating procedures of the fast affine invariant feature extracting algorithm is proposed. Secondly, the definition of extended contour is proposed which is used to amend the fast affine invariant feature extraction algorithm. Thirdly, through simulation experiments, the parameter setting intervals in the algorithm are fixed and the numbers and powers of the component functions are determined. The simulation experiments are carried out to compared the proposed algorithm with the original fast affine invariant feature extracting algorithm and the multi-scale auto-convolution algorithm. The experimental results show that the proposed algorithm has better classification and recognition ability than the original fast affine invariant feature extracting algorithm and the multi-scale auto-convolution algorithm.

Key words: computer application, pattern recognition, affine invariant feature, extended contour, multi?scale auto convolution(MSA)

CLC Number: 

  • TP391.4

Fig.1

Integration area affine transformation, α=0.5"

Fig.2

Schematic diagram of extended contour trimming process (α=0.5)"

Fig.3

Extended contour images"

Fig.4

Sample images 1"

Fig.5

"

Fig.6

Sample images 2"

Fig.7

"

Fig.8

Sample images 3"

Fig.9

"

Fig.10

Comparison figures of separability measures of the interval setting test, results of COIL?100 database"

Fig.11

Comparison figures of separability measures of interval setting test, results of FISH database"

Fig.12

Comparison figures for component function numbers"

Fig.13

Comparison figures of separability measures of component number setting test"

Fig.14

Comparison figures for component function powers with COIL?100"

Fig.15

Comparison figures for component function powers with FISH"

Table 1

Comparison table of separability measures of the FAST_AFFINE,NEW_FAST_AFFINE and MSA"

J12 J13 J14 J23 J24 J34
T1 O 1.1032 2.7945 1.9064 0.4488 1.9761 8.0326
N 5.5070 7.2457 2.1370 2.8211 5.7799 7.0127
M 5.4017 8.3021 2.9541 2.2322 6.7721 10.5159
T2 O 5.0131 0.6587 0.9866 1.6523 9.3518 1.2623
N 5.3153 0.7623 1.3552 1.7161 8.2898 1.5486
M 4.6857 0.7613 1.6459 1.2520 5.2240 1.5440
T3 O 0.5680 1.2638 0.8324 0.8459 0.3213 0.6597
N 0.6110 2.2677 1.8270 0.9857 0.3839 0.4931
M 0.8256 2.1455 2.1840 0.2496 0.2407 0.5085
T4 O 0.8703 0.7001 0.4781 1.3752 0.877 0.2941
N 1.1560 3.5281 1.6704 1.7401 0.4597 1.1943
M 1.266 3.5481 1.8885 1.4718 0.2786 1.4369
T5 O 0.3155 0.3709 0.2800 0.4098 0.2011 0.3410
N 0.6723 0.6052 0.1345 1.4938 0.7477 0.7438
M 0.9683 0.7693 0.2938 1.6291 0.6796 0.9896
T6 O 1.2256 0.9845 0.5956 1.4401 0.2489 0.9891
N 1.8734 1.7978 1.1511 3.3365 1.4667 3.0824
M 2.1548 1.5891 1.3045 3.3482 1.6935 2.9658
T7 O 1.0424 1.7442 1.1964 1.5861 0.4114 1.6633
N 0.6220 1.2677 0.4870 2.8803 0.7553 2.9218
M 0.8511 0.8024 0.9422 2.5131 0.3957 3.4842
T8 O 9.4425 3.3529 1.9051 1.8982 1.5830 0.8370
N 3.1828 11.2549 4.0737 7.2851 2.2636 2.8422
M 6.7318 19.4526 4.5319 10.4487 1.5937 3.5344
T9 O 6.2368 0.5544 0.2710 0.9613 4.8863 0.4478
N 4.7109 1.2118 0.6017 0.4666 1.9455 0.6242
M 4.0699 1.1646 0.5917 0.4083 1.8814 0.6345
T10 O 1.3416 5.8264 5.6180 44.8413 9.1621 16.6656
N 0.5326 4.6143 2.8479 50.9191 5.3271 10.3754
M 0.3492 2.7033 1.9654 36.5806 7.6850 14.0528

Fig.16

Time efficiency contrast figures"

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