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access icon free Minimum elastic bounding box algorithm for dimension detection of 3D objects: a case of airline baggage measurement

Motivated by the interference of appendages in airline baggage dimension detection using three-dimensional (3D) point cloud, a minimum elastic bounding box (MEBB) algorithm for dimension detection of 3D objects is developed. The baggage dimension measurements using traditional bounding box method or shape fitting method can cause large measurements due to the interference of appendages. Starting from the idea of ‘enclosing’, an elastic bounding box model with the deformable surface is established. On the basis of using principal component analysis to obtain the main direction of the bounding box, the elastic rules for deformable surfaces are developed so as to produce a large elastic force when it comes into contact with the main body part and to produce a small elastic force when it comes into contact with the appendages part. The airline baggage measurement shows how to use MEBB for dimension detection, especially for the processing of isotropic density distribution, the elasticity computing and the adaptive adjustment of elasticity. Results on typical baggage samples, comparisons to other methods, and error distribution experiments with different algorithm parameters show that the authors’ method can reliably obtain the size of the main body part of the object under the interference of appendages.

References

    1. 1)
      • 23. Roos, F., Kellner, D., Klappstein, J., et al: ‘Estimation of the orientation of vehicles in high-resolution radar images’. IEEE Mtt-S Int. Conf. on Microwaves for Intelligent Mobility, Heidelberg, Germany, 2015, pp. 14.
    2. 2)
      • 9. Hamzeloo, E., Massinaei, M., Mehrshad, N.: ‘Estimation of particle size distribution on an industrial conveyor belt using image analysis and neural networks’, Powder Technol., 2014, 261, (7), pp. 185190.
    3. 3)
      • 2. Kolokytha, S., Speller, R., Robson, S.: ‘The development of an ‘on-belt tomosynthesis’ system for cost-effective (3D) baggage screening’. Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, Baltimore, MD, USA, 2013, p. 87090Z.
    4. 4)
      • 26. Leo, M., Natale, A., Del-Coco, M., et al: ‘Robust estimation of object dimensions and external defect detection with a low-cost sensor’, J. Nondestruct. Eval., 2017, 36, (1), p. 17.
    5. 5)
      • 16. Chen, N., Kemeny, J., Jiang, Q., et al: ‘Automatic extraction of blocks from 3D point clouds of fractured rock’, Comput. Geosci., 2017, 109, pp. 149161.
    6. 6)
      • 15. Xu, Z., Peng, J., Chen, X.: ‘A method for vehicle three-dimensional size measurement based on laser ranging’. Int. Conf. on Transportation Information and Safety, Wuhan, China, 2015, pp. 3437.
    7. 7)
      • 7. Lee, K.S., Ji, M.S.: ‘A study on the adoption of self-bag-drop system to enhance airport operation’, J. Korean Soc. Aviat. Aeronaut., 2015, 23, (2), pp. 7583.
    8. 8)
      • 5. Gao, Q., Li, T., Luo, Q.: ‘An algorithm for inspecting the number of self-check-in airline luggage based on hierarchical clustering’. Sixth Int. Conf. on Measuring Technology and Mechatronics Automation, Zhangjiajie, China, 2014, pp. 7174.
    9. 9)
      • 31. Wei, Y., Wang, Y., Wu, Q., et al: ‘Research on fixed direction hull bounding volume in collision detection’, J. Softw., 2001, 12, (7), pp. 10561063.
    10. 10)
      • 28. Groenwall, C.A., Millnert, M.C.: ‘Vehicle size and orientation estimation using geometric fitting’. Automatic Target Recognition XI, Orlando, FL, USA, 2001, vol. 4379, pp. 412423.
    11. 11)
      • 30. O'Rourke, J.: ‘Finding minimal enclosing boxes’, Int. J. Comput. Inf. Sci., 1985, 14, (3), pp. 183199.
    12. 12)
      • 32. Dimitrov, D., Knauer, C., Kriegel, K., et al: ‘On the bounding boxes obtained by principal component analysis’. 22nd European Workshop on Computational Geometry, Delphi, Greece, 2006, pp. 193196.
    13. 13)
      • 27. Xiang, Y., Sun, Y., Li, C.: ‘A rooftop extraction method using color feature, height map information and road information’. Image and Signal Processing for Remote Sensing XVIII, Edinburgh, UK, 2012, p. 85370T.
    14. 14)
      • 22. Chaudhuri, D., Samal, A.: ‘A simple method for fitting of bounding rectangle to closed regions’, Pattern Recognit., 2007, 40, (7), pp. 19811989.
    15. 15)
      • 29. Seo, S., Lee, J., Kim, Y.: ‘Extraction of boundaries of rooftop fenced buildings from airborne laser scanning data using rectangle models’, IEEE Geosci. Remote Sens. Lett., 2014, 11, (2), pp. 404408.
    16. 16)
      • 24. Kwak, E., Habib, A.: ‘Automatic representation and reconstruction of DBM from LiDAR data using recursive minimum bounding rectangle’, ISPRS J. Photogramm. Remote Sensing, 2014, 93, (7), pp. 171191.
    17. 17)
      • 33. Ye, A., Gong, S., Wang, C., et al: ‘Point cloud density extraction based on stochastic distribution estimation’, Comput. Eng., 2009, 35, (4), pp. 183186.
    18. 18)
      • 25. Yang, J., Jiang, Z.: ‘Rectangle fitting via quadratic programming’. Int. Workshop on Multimedia Signal Processing, Xia'men, China, 2015, pp. 16.
    19. 19)
      • 35. Grönwall, C., Gustafsson, F., Millnert, M.: ‘Ground target recognition using rectangle estimation’, IEEE Trans. Image Process., 2006, 15, (11), p. 3400.
    20. 20)
      • 21. Chaudhuri, D., Kushwaha, N.K., Sharif, I., et al: ‘Finding best-fitted rectangle for regions using a bisection method’, Mach. Vis. Appl., 2012, 23, (6), pp. 12631271.
    21. 21)
      • 37. Kai, H., Ruthotto, S., Kragic, D.: ‘Minimum volume bounding box decomposition for shape approximation in robot grasping’. IEEE Int. Conf. on Robotics and Automation, Pasadena, CA, USA, 2008, pp. 16281633.
    22. 22)
      • 4. Song, J.W., Kim, D.C., Lee, J.H.: ‘A study about adjacent baggage recognition to control the adjoined bags in airport’. Int. Conf. on Control and Automation, Jeju, South Korea, 2015, pp. 59.
    23. 23)
      • 36. Lu, L., Choi, Y.K., Wang, W., et al: ‘Variational 3D shape segmentation for bounding volume computation’, Comput. Graph. Forum, 2007, 26, (3), pp. 329338.
    24. 24)
      • 18. Kohno, Y., Maeda, M., Hamamura, T., et al: ‘The measurement of carried cartons using multiple kinect sensors’. Int. Conf. on Machine Vision Applications, Kyoto, Japan, 2013, pp. 2023.
    25. 25)
      • 19. Ni, H., Lin, X., Ning, X., et al: ‘Edge detection and feature line tracing in 3D-point clouds by analyzing geometric properties of neighborhoods’, Remote Sens., 2016, 8, (9), p. 710.
    26. 26)
      • 11. Jia, X., Guo, T., Zhao, J.: ‘A machine vision-based method of high-precision measurement of the size of workpiece’. Seventh Int. Symp. on Precision Mechanical Measurements, Xia'men, China, 2016, p. 99031Y.
    27. 27)
      • 20. Zhao, W., Zhao, C., Wen, Y., et al: ‘An adaptive corner extraction method of point cloud for machine vision measuring system’. Int. Conf. on Machine Vision and Human-Machine Interface, Kaifeng, China, 2010, pp. 8083.
    28. 28)
      • 6. Yin, D., Gao, Q., Luo, Q.: ‘Automatic airline baggage counting using 3D image segmentation’. Int. Workshop on Pattern Recognition, Singapore, 2017, p. 104430P.
    29. 29)
      • 13. Mustafah, Y.M., Noor, R., Hasbi, H., et al: ‘Stereo vision images processing for real-time object distance and size measurements’. Int. Conf. on Computer and Communication Engineering, Kuala Lumpur, Malaysia, 2012, pp. 659663.
    30. 30)
      • 17. Ferreira, B.Q., Griné, M., Gameiro, D., et al: ‘VOLUMNECT: measuring volumes with kinect’. Int. Society for Optical Engineering, San Francisco, CA, USA, 2014, p. 9013.
    31. 31)
      • 8. Maiti, A., Chakravarty, D., Biswas, K., et al: ‘Development of a mass model in estimating weight-wise particle size distribution using digital image processing’, Int. J. Mining Sci. Technol., 2017, 27, (3), pp. 435443.
    32. 32)
      • 10. Li, H., Qian, Y., Cao, P., et al: ‘Calculation method of surface shape feature of rice seed based on point cloud’, Comput. Electron. Agric., 2017, 142, pp. 416423.
    33. 33)
      • 12. Georgousis, S., Stentoumis, C., Doulamis, N., et al: ‘A hybrid algorithm for dense stereo correspondences in challenging indoor scenes’. IEEE Int. Conf. on Imaging Systems and Techniques, Chania, Greece, 2016, pp. 460465.
    34. 34)
      • 34. Sarkar, A., Biswas, A., Dutt, M., et al: ‘Finding a largest rectangle inside a digital object and rectangularization’, J. Comput. Syst. Sci., 2017, pp. 114.
    35. 35)
      • 1. Mery, D., Mondragon, G., Riffo, V., et al: ‘Detection of regular objects in baggage using multiple X-ray views’, Insight: Non-Destr. Test. Cond. Monit., 2013, 55, (1), pp. 1620.
    36. 36)
      • 3. Flitton, G., Breckon, T.P., Megherbi, N.: ‘A 3D extension to cortex like mechanisms for 3D object class recognition’. Computer Vision and Pattern Recognition(CVPR), Providence, RI, USA, 2012, pp. 36343641.
    37. 37)
      • 14. Marin, V.E., Chang, W.H.W., Nejat, G.: ‘Generic design methodology for the development of three-dimensional structured-light sensory systems for measuring complex objects’, Opt. Eng., 2014, 53, (11), p. 112210.
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