Journal of Shandong University(Engineering Science) ›› 2018, Vol. 48 ›› Issue (5): 55-60.doi: 10.6040/j.issn.1672-3961.0.2018.246

• Machine Learning & Data Mining • Previous Articles     Next Articles

Feature extraction method of color pseudo-random coded structured light

Guoxin WANG1,2(),Fengdong CHEN1,Guodong LIU1,*()   

  1. 1. School of Electrical Engineering & Automation, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
    2. Institute of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin 150022, Heilongjiang, China
  • Received:2018-06-07 Online:2018-10-01 Published:2018-06-07
  • Contact: Guodong LIU E-mail:wanggx1980@126.com;lgd@hit.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61275096)

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Abstract:

In order to enhance the feature information of weak texture target in three-dimensional reconstruction, a feature extraction method of structured light based on color square pseudo-random code was proposed. A pseudo-random coded structured light pattern composed of five kinds of color squares was designed and projected onto the target object. A gradient operator template was established to coarsely locate the corners of the down-sampled image. The local non-maximum was suppressed. The Harris algorithm was extended to color multi-channel images, corner detection was carried out on the coarse locating area of the original image, and then the precise position of the corner point was determined in the color image. The experiment results indicated that the proposed method could effectively guarantee the accuracy of feature extraction and had strong robustness with poor surface color and texture.

Key words: pseudo-random, coded structured light, feature extraction, three-dimensional reconstruction, corner detection

CLC Number: 

  • TP391.41

Fig.1

Color squares coded structured light image"

Fig.2

Gradient operator template"

Fig.3

Downsampled image corner extraction"

Fig.4

Corner extraction image"

Fig.5

Corner extraction test results"

Table 1

Comparison of corner extraction algorithm results"

算法种类 提取点数 正确点数 丢失点数 准确率 漏检率
FAST 2 199 1 356 833 0.62 0.38
经典Harris 1 406 1 299 890 0.92 0.41
彩色Harris 1 862 1 731 458 0.93 0.21
AGAST 2 381 1 559 630 0.65 0.29
本研究算法 1 912 1 826 363 0.96 0.17

Fig.6

Test results of anti-noise performance"

Table 2

Comparison of anti-noise performance results"

算法种类 提取点数 正确点数 丢失点数 准确率 漏检率
彩色Harris 2 156 1 530 659 0.71 0.30
本研究算法 2 014 1 913 276 0.95 0.13

Fig.7

Lighting and projection angle test results"

Table 3

Comparison of lighting and projection angle test results"

算法种类 提取点数 正确点数 丢失点数 准确率 漏检率
FAST 2 113 1 030 758 0.49 0.42
经典Harris 1 310 944 844 0.72 0.47
彩色Harris 1 968 1 519 269 0.77 0.15
AGAST 2 553 962 826 0.38 0.46
本研究算法 1 767 1 731 57 0.98 0.03
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