Open Access
Volume 38, Number 6, December 2020
Page(s) 1218 - 1224
Published online 02 February 2021
  1. Zhang J, Jia J, Zhu Z, et al. Fine Detection and Classification of Multi-Class Barcode in Complex Environments[C]//International Conference on Multimedia and Expo, 2019: 306-311 [Google Scholar]
  2. Liu Y, Yang J, Liu M, et al. Recognition of QR Code with Mobile Phones[C]//Chinese Control and Decision Conference, 2008: 203-206 [Google Scholar]
  3. Lee S J, Lim J, Tewolde G S, et al. Autonomous Tour Guide Robot by Using Ultrasonic Range Sensors and QR Code Recognition in Indoor Environment[C]//IEEE International Conference on Electro/Information Technology, 2014: 410-415 [Google Scholar]
  4. Gu Y, Zhang W. QR Code Recognition Based on Image Processing[C]//International Conference on Information Science and Technology, 2011: 733-736 [Google Scholar]
  5. Formiga A D, Lins R D, Simske S J, et al. An Assessment of Data Matrix Barcode Recognition under Scaling, Rotation and Cylindrical Warping[C]//ACM Symposium on Applied Computing, 2011: 266-267 [Google Scholar]
  6. Thielecke L, Aranyossy T, Dahl A, et al. Limitations and Challenges of Genetic Barcode Quantification[J]. Scientific Reports, 2017, 7(1): 43249-43263 [CrossRef] [Google Scholar]
  7. Hogpracha W, Vongpradhip S. Recognition System for QR Code on Moving Car[C]//International Conference on Computer Science and Education, 2015: 14-18 [Google Scholar]
  8. Goronzy G, Pelka M, Hellbruck H, et al. QRPos: Indoor Positioning System for Self-Balancing Robots Based on QR Codes[C]//International Conference on Indoor Positioning and Indoor Navigation, 2016: 1-8 [Google Scholar]
  9. Huang Q, Chen W, Huang X, et al. Data Matrix Code Location Based on Finder Pattern Detection and Bar Code Border Fitting[J]. Mathematical Problems in Engineering, 2012, 2012: 1-13 [CrossRef] [Google Scholar]
  10. Chen Y, Chi K, Hua K, et al. Design of Image Barcodes for Future Mobile Advertising[J]. Eurasip Journal on Image and Video Processing, 2017, 2017(1): 1-12 [Google Scholar]
  11. Nizampatnam S, Saha D, Chandak R, et al. Dynamic Contrast Enhancement and Flexible Odor Codes[J]. Nature Communications, 2018, 9(1): 1-35 [CrossRef] [Google Scholar]
  12. Hansen D K, Nasrollahi K, Rasmusen C B, et al. Real-Time Barcode Detection and Classification using Deep Learning[C]//9th International Joint Conference on Computational Intelligence, 2017 [Google Scholar]
  13. Zhang H, Shi G, Liu L, et al. Detection and Identification Method of Medical Label Barcode Based on Deep Learning[C]//International Conference on Image Processing, 2018: 1-6 [Google Scholar]
  14. Andrey Zharkov, Ivan Zagaynov. Universal Barcode Detector via Semantic Segmentation[C]//2019 International Conference on Document Analysis and Recognition, 2019 [Google Scholar]
  15. Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]//Computer Vision and Pattern Recognition, 2017: 6517-6525 [Google Scholar]
  16. Zhong P, Gong Z. A Hybrid DBN and CRF Model for Spectral-Spatial Classification of Hyperspectral Images[J]. Statistics, Optimization and Information Computing, 2017, 5(2): 75-98 [CrossRef] [Google Scholar]
  17. Unic J, Hirota K, Rosin P L, et al. A Hu Moment Invariant as a Shape Circularity Measure[J]. Pattern Recognition, 2010, 43(1): 47-57 [CrossRef] [Google Scholar]
  18. Li Z, Guo T, Bao F, et al. Teeth Category Classification via Hu Moment Invariant and Extreme Learning Machine[C]. International Conference on Computer Modeling and Simulation, 2018 [Google Scholar]
  19. Tran V T, Althobiani F, Ball A, et al. An Approach to Fault Diagnosis of Reciprocating Compressor Valves Using Teager-Kaiser Energy Operator and Deep Belief Networks[J]. Expert Systems with Applications, 2014, 41(9): 4113-4122 [CrossRef] [Google Scholar]
  20. Tao J, Liu Y, Yang D, et al. Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion[J]. Shock and Vibration, 2016, 2016: 1-9 [CrossRef] [Google Scholar]
  21. Zhang L, Gao H, Wen J, et al. A Deep Learning-Based Recognition Method for Degradation Monitoring of Ball Screw with Multi-Sensor Data Fusion[J]. Microelectronics Reliability, 2017, 75: 215-222 [CrossRef] [Google Scholar]
  22. Zhang T, Zhou W, Meng F, et al. Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse[J]. Electronics, 2019, 8(9): 946-971 [CrossRef] [Google Scholar]

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