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Analysis of Segmentation and Identification of Square-Hexa-Round-Holed Nuts Using Sobel and Canny Edge Detector

  • Dayanand G. Savakar
  • Ravi HosurEmail author
  • Deepa Pawar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

In the existing real-time automobile shop, it is difficult to trace an object and identify its presence. The failure may happen due to its absence or improper match of shape as its identity. So to overcome this, we propose a method which can be used for the automatic identification of vehicular nuts based on the input image that contains a nut with square, hexa, rounded-head and pinned-bolt shapes. The application even works with the nut having clear view or any entity added like mud, noise, colour, etc. on the surface of nut. For the identification process the database has been designed to store different shapes for selected number of nut-shapes. By applying median filter during pre-processing stage, the Sobel-edge-detector and Canny-edge-detector; segmented and identified the captured images by identifying the edges to ascertain shape of the input. With the experimentations carried the method results with an accuracy of 86.1875%

Keywords

Shape-based Sobel Canny edge detector Segmentation Identification Vehicular 

References

  1. 1.
    Hosur, R., Savakar, D.G., Madabhavi, S.: Shape based object retrieval technique for vehicular spare parts. Int. J. Eng. Technol. (UAE) 7(4.5), 355–359 (2018)CrossRefGoogle Scholar
  2. 2.
    Santosh, K.C., Roy, P.P.: Arrow detection in biomedical images using sequential classifier. Int. J. Mach. Learn. Cybern. 9(6), 993–1006 (2018)CrossRefGoogle Scholar
  3. 3.
    Savakar, D.G., Hosur, R.: A relative 3D scan and construction for face using meshing algorithm. Multimedia Tools Appl. 77(19), 25253–25273 (2018)CrossRefGoogle Scholar
  4. 4.
    Santosh, K.C., Aafaque, A., Antani, S., Thoma, G.R.: Line segment-based stitched multipanel figure separation for effective biomedical CBIR. Int. J. Pattern Recogn. Artif. Intell. (IJPRAI) 31(6), 1–18 (2017)Google Scholar
  5. 5.
    Zohora, F.T., Santosh, K.C.: Foreign circular element detection in chest X-rays for effective automated pulmonary abnormality screening. Int. J. Comput. Vis. Image Process. (IJCVIP) 7(2), 36–49 (2017)CrossRefGoogle Scholar
  6. 6.
    Zohora, F.T., Santosh, K.C.: Circular foreign object detection in chest X-ray images. In: Santosh, K.C., Hangarge, M., Bevilacqua, V., Negi, A. (eds.) RTIP2R 2016. CCIS, vol. 709, pp. 391–401. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-4859-3_35CrossRefGoogle Scholar
  7. 7.
    Santosh, K.C., Vajda, S., Antani, S., Thoma, G.R.: Edge map analysis in chest X-rays for automatic abnormality screening. Int. J. Comput. Assist. Radiol. Surg. (IJCARS) 11(9), 1637–1646 (2016)CrossRefGoogle Scholar
  8. 8.
    Santosh, K.C., Candemir, S., Jaeger, S., Karargyris, A., Antani, S., Thoma, G.: Automatically detecting rotation in chest radiographs using principal rib-orientation measure for quality control. Int. J. Pattern Recogn. Artif. Intell. (IJPRAI) 29(2), 1557001 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Candemir, S., Borovikov, E., Santosh, K.C., Antani, S., Thoma, G.: RSILC: Rotation- and Scale-Invariant, Line-based Color-aware descriptor. Image Vis. Comput. 42, 1–12 (2015)CrossRefGoogle Scholar
  10. 10.
    Herrera, J.L., del-Blanco Narciso Garcia, C.R.: Edge based depth gradient refinement for 2D to 3D learnt prior conversion. In: IEEE 3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON) (2015)Google Scholar
  11. 11.
    Wu, Z., Li, J., Hu, J., Deng, W: Pose-invariant face recognition using 3D multi-depth generic elastic models. IEEE (2015)Google Scholar
  12. 12.
    Zhou, Y., Guo, H., Fu, R., Liang, G., Wang, C., Wu, X.: 3D reconstruction based on light field information. In: Proceeding of the 2015 IEEE International Conference on Information and Automation Lijiang, Held on August 2015Google Scholar
  13. 13.
    Kaneko, M., Hasegawa, T., Yamauchi, Y., Yamashita, T., Fujiyoshi, H., Murase, H: Fast 3D edge detection by using decision tree from depth image. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Congress Center Hamburg, Hamburg, Germany, 28 September–2 October 2015, pp. 1314–1319 (2015)Google Scholar
  14. 14.
    Xu, X., et al.: Adaptive block truncation filter for MVA depth image enhancement. In: 2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) (2014)Google Scholar
  15. 15.
    Singh, M., Sharma, R., Garg, D.: A new proposed issue for secure image steganography technique based on 2D block DCT and DCT. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2, 29–33 (2012)Google Scholar
  16. 16.
    Izadi, S., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: UIST 2011 (2011)Google Scholar
  17. 17.
    Alptekin Engin, M., Cavsoglu, B.: New approach in image compression: 3D spiral JPEG. IEEE Commun. Lett. 15(11), 1234–1236 (2011)CrossRefGoogle Scholar
  18. 18.
    Khare, A., Kumari, M., Khare, P.: Efficient algorithm for digital image steganography. J. Inf. Knowl. Res. Comput. Sci. Appl. 1(1), 1–5 (2010)Google Scholar
  19. 19.
    Bariya, P., Nishino, K.: Scale-hierarchical 3D object recognition in cluttered scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1657–1664 (2010)Google Scholar
  20. 20.
    Yu, Z., Ip, H.H.S., Kwok, L.F.: A robust watermarking scheme for 3D triangular mesh models. J. Pattern Recogn. Soc. 36(11), 2603–2614 (2003)CrossRefGoogle Scholar
  21. 21.
    Zhang, D., Lu, G.: Shape based image retrieval using Generic Fourier Descriptor. J. Sig. Process. Image Commun. 17(10), 825–848 (2002)CrossRefGoogle Scholar
  22. 22.
    Zhang, D., Lu, G.: A comparative study of curvature scale shape and Fourier descriptors for shape-based image retrieval. J. Vis. Commun. Image Represent. 14(1), 39–57 (2003)CrossRefGoogle Scholar
  23. 23.
    Liu, Y., Zhang, D., Lu, G., Ma, W.-Y.: A survey of content-based image retrieval with high-level semantics. J. Pattern Recogn. 40(1), 262–282 (2007)CrossRefGoogle Scholar
  24. 24.
    Krishnan, N., Varghese, J., Saudia, S., Mathew, S.P., et al.: A new adaptive class of filter operators for salt and pepper impluse corrupted images. Int. J. Imaging Sci. Eng. (IJISE) 1(2), 44–51 (2007)Google Scholar
  25. 25.
    Peng, S.-H., Kim, D.-H., Lee, S.-L., Chumg, C.-W.: A visual shape descriptor using sectors and shape context of contour lines. J. Inf. Sci. 180(16), 2925–2939 (2010)CrossRefGoogle Scholar
  26. 26.
    Wang, X.-Y., Yu, Y.-J., Yang, H.-Y.: An effective images retrieval scheme using color, texture and shape features. J. Comput. Stand. Interfaces 33, 59–68 (2010)CrossRefGoogle Scholar
  27. 27.
    Rao, S., Srinivas Kumar, S., Chandra Mohan, B.: Content-based image retrieval using exact legendre moment and support vector machine. Int. J. Multimedia Appl. 2(2), 69–79 (2010)CrossRefGoogle Scholar
  28. 28.
    Mathew, S.P., Balas, V.E., Zachariah, K.P., Samuel, P.: A content-based image retrieval system based on polar raster edge sampling signature. Acta Polytech. 11(3), 25–36 (2014)Google Scholar
  29. 29.
    Nanni, L., Lumini, A., Brahnam, S.: Ensemble of shape descriptors for shape retrieval and classification. Int. J. Adv. Intell. Paradigms (IJAIP) 6(2), 136–156 (2014)CrossRefGoogle Scholar
  30. 30.
    Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Automated fractured bone segmentation and labeling from CT images. J. Med. Syst. (2019).  https://doi.org/10.1007/s10916-019-1176-xCrossRefGoogle Scholar
  31. 31.
    Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Segmentation and analysis of CT images for bone fracture detection and labeling. In: Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques, Chap. 7. CRC Press (2019). ISBN 9780367139612Google Scholar
  32. 32.
    Hegadi, R.S., Navale, D.I., Pawar, T.D., Ruikar, D.D.: Multi feature-based classification of osteoarthritis in knee joint X-ray images. In: Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques, Chap. 5. CRC Press (2019). ISBN 9780367139612Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Rani Chennamma UniversityTorvi, VijayapurIndia
  2. 2.BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and TechnologyVijayapurIndia

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