Extraction and Selection of Objects in Digital Images by the Use of Straight Edges Segments

Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 135)

Abstract

New method for finding geometric structures in digital gray-level images is proposed. The method is based on grouping straight line segments, which correspond to the edges of the object. It includes extraction of straight line segments by oriented filtering of gradient image and gives the ordered list of segments with the endpoints’ coordinates for each segment. Adaptive algorithm for straight edge segments extraction is developed that uses angle adjustment of oriented filter in order to extract the line corresponding to the real edges accurately. This algorithm permits the extraction and localization of artificial objects with the rectangular or polygonal shape in digital images. Perceptual grouping approach is applied to extracted segments in order to obtain the simple and complex structures of lines using their crossings. Proposed approach uses the points of intersection of ordered segments as the main property of object structure and also takes into account some specific properties of grouped lines, such as the anti-parallelism, proximity, and adjacency. At the first step, the simple structures are obtained by lines grouping taking into consideration all crossing lines or only part of them. At the second step, these simple structures are joined allowing for restrictions. Initial image is transformed to a collection of closed rectangular or polygonal structures with their locations and orientations. Structures obtained by this method represent an intermediate-level description of interesting objects, which have polygonal view (buildings, parts of roads, bridges, and some natural places of landscape). Application with real aerial and satellite images shows a good ability to separate and extract the specific objects like buildings and other line-segment-rich structures.

Keywords

Line-segment detector Local descriptors Geometric primitives Edge-based feature detector Perceptual contour grouping Object recognition Content-based image retrieval Building and road extraction Feature-based image matching 

Notes

Acknowledgements

Author thanks to Prof. Rudolf Germer from TU Berlin for collaboration, Dr. J. Wernicke from EMT (Penzberg) for picture material and HTW Berlin and DAAD for support of the work.

References

  1. 1.
    Iqbal, Q., Aggarwal, J.K.: Retrieval by classification of images containing large manmade objects using perceptual grouping. Pattern Recognit. 35(7), 1463–1479 (2002)CrossRefMATHGoogle Scholar
  2. 2.
    Movahedi, V.: Contour Grouping. Department of Computer Science and Engineering & Centre for Vision Research, Qual Exam, York University (2009)Google Scholar
  3. 3.
    Sohn, G.: Extraction of buildings from high-resolution satellite data and Lidar. In: XXth ISPRS Congress, vol. XXXV, Part B3, pp. 1036–1042 (2004)Google Scholar
  4. 4.
    Srinivasan,P., Wang, L., Shi, J.: Grouping contours via a related image. In: 21st International Conference on Neural Information Processing Systems (NIPS’2008), pp. 1553–1560 (2008)Google Scholar
  5. 5.
    Ferrari, V., Fevrier, L., Jurie, F., Schmid, C.: Groups of adjacent contour segments for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 30(1), 36–51 (2008)CrossRefGoogle Scholar
  6. 6.
    Lu, Ch., Latecki, L.J., Adluru, N., Yang, X., Ling, H.: Shape guided contour grouping with particle filters. In: IEEE 12th International Conference on Computer Vision (ICCV’2009), pp. 2288–2295 (2009)Google Scholar
  7. 7.
    Hedau, V., Arora, H., Ahuja, N.: Matching images under unstable segmentations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’2008), pp. 551–563 (2008)Google Scholar
  8. 8.
    Shao, J., Mohr, R., Fraser, C.: Multi-image matching using segment features. Int. Arch. Photogramm. Remote Sens. XXXIII(Part B3), 837–844 (2000)Google Scholar
  9. 9.
    Mikolajczyk, K., Zisserman, A., Schmid, C.: Shape recognition with edge-based features. In: British Machine Vision Conference (BMVC’2003), pp. 779–788 (2003)Google Scholar
  10. 10.
    Kadir, T., Brady, M.: Saliency, scale and image description. Int. J. Comput. Vis. 45(2), 83–105 (2001)CrossRefMATHGoogle Scholar
  11. 11.
    Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Comput. Graph. Vis. 3(3), 177–280 (2007)CrossRefGoogle Scholar
  12. 12.
    Volkov, V., Germer, R., Oneshko, A., Oralov, D.: Object description and extraction by the use of straight line segments in digital images. In: International Conference on Image Processing, Computer Vision and Pattern Recognition (IPCV’2011), pp. 588–594 (2011)Google Scholar
  13. 13.
    Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3D objects. Int. J. Comput. Vis. 7(3), 263–284 (2006)Google Scholar
  14. 14.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  15. 15.
    Sohn, G., Dowman, I.J.: Extraction of buildings from high resolution satellite data. In: Baltsavias, E.P., Gruen, A., VanGool, L. (eds.) Automatic Extraction of Man-Made Objects from Aerial and Space Images (III), pp. 345–355. CRC Press (2001)Google Scholar
  16. 16.
    Brown, M., Hua, G., Winder, S.: Discriminative learning of local image descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 1–14 (2011)CrossRefGoogle Scholar
  17. 17.
    Horaud, R., Veillon, F., Skordas, T.: Finding geometric and relational structures in an image. In: Faugeras, O. (ed.) Computer Vision—ECCV 90, LNCS, vol. 427, pp. 374–384. Springer (1990)Google Scholar
  18. 18.
    Kim, S.K., Ranganah, H.S.: Efficient algorithms to extract geometric features of edge image. In: International Conference on Image Process, Computer Vision, and Pattern Recognition (IPCV’2010), vol. 2, pp. 519–525 (2010)Google Scholar
  19. 19.
    Volkov, V., Germer, R., Oneshko, A., Oralov, D.: Object description and finding of geometric structures on the base of extracted straight edge segments in digital images In: International Conference on Image Process, Computer Vision and Pattern Recognition (IPCV’2012), Part II, pp. 805–812 (2012)Google Scholar
  20. 20.
    Grompone von Gioi, R., Jakubovich, J., Morel, J.M., Randall, G.: LSD: a line segment detector. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722–732 (2010)CrossRefGoogle Scholar
  21. 21.
    Medioni, G., Nevatia, R.: Matching images using linear features. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 675–685 (1984)CrossRefGoogle Scholar
  22. 22.
    Fu, Z., Sun, Z.: An algorithm of straight line features matching on aerial imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XXXVII(Part B3b), 97–102 (2008)Google Scholar
  23. 23.
    Zhao, Y., Chen, Y.Q.: Connected equi-length line segments for curve and structure matching. J. Pattern Recognit. Artif. Intell. 18(6), 1019–1037 (2004)CrossRefGoogle Scholar
  24. 24.
    Lavigne, D.A., Saeedi, P., Dlugan, A., Goldstein, N., Zwick, H.: Automatic building detection and 3D shape recovery from single monocular electro-optic imagery. In: Kadar, I. (ed.) Signal Processing, Sensor Fusion, and Target Recognition XVI, SPIE Defence & Security Symposium, vol. 6567, Article id. 656716 (2007)Google Scholar
  25. 25.
    Magli, E., Olmo, G., Presti, L.L.: On-board selection of relevant images: an application to linear feature recognition. IEEE Trans. Image Process. 10(4), 543–553 (2001)CrossRefMATHGoogle Scholar
  26. 26.
    Tretyak, E., Barinova, O., Kohli, P., Lempitsky, V.: Geometric image parsing in man-made environments. Int. J. Comput. Vis. 97(3), 305–321 (2012)CrossRefGoogle Scholar
  27. 27.
    Theng, L.B.: Automatic building extraction from satellite imagery. Eng. Lett. 13(3), EL_13_3_5 (2006)Google Scholar
  28. 28.
    Jin, X., Davis, C.H.: Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information. EURASIP J. Appl. Signal Process. 14, 2196–2206 (2005)CrossRefMATHGoogle Scholar
  29. 29.
    Ettarid, M., Rouchdi, M., Labouab, L.: Automatic extraction of buildings from high resolution satellite images. In: XXIst ISPRS Congress, vol. XXXVII, Part B8, pp. 61–65 (2008)Google Scholar
  30. 30.
    Li, Y., Shapiro, L.G.: Consistent line clusters for building recognition in CBIR. In: 16th International Conference on Pattern Recognition (ICPR’2002), vol. 3, pp. 952–956 (2002)Google Scholar
  31. 31.
    Jia, W., Zhang, J., Yang, J.: SAR image and optical image registration based on contour and similarity measures. In: Geo-spatial Solutions for Emergency Management (GSEM’2009), pp. 1–5 (2009)Google Scholar
  32. 32.
    Song, Y., Yuan, X., Xu, H.: A multi-temporal image registration method based on edge matching and maximum likelihood estimation sample consensus. Int. Arch. PRSSI Sci. XXXVII(Part B3b), pp. 61–66 (2008)Google Scholar
  33. 33.
    Bergevin, R., Bernier, J.F.: Detection of unexpected multi-part objects from segmented contour maps. Pattern Recognit. 42(11), 2403–2420 (2009)CrossRefMATHGoogle Scholar
  34. 34.
    Kim, S.K., Ranganah, H.S.: Efficient algorithms to extract geometric features of edge images. In: Image Process, Computer Vision, and Pattern Recognition (IPCV’2010), Part II, pp. 519–525 (2010)Google Scholar
  35. 35.
    Brown, M., Hua, G., Winder, S.: Discriminative learning of local image descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 1–14 (2011)CrossRefGoogle Scholar
  36. 36.
    Venkateswar, V., Chellappa, R.: Extraction of straight lines in aerial images. IEEE Trans. Pattern Anal. Mach. Intell. 14(11), 1111–1114 (1992)CrossRefGoogle Scholar
  37. 37.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(24), 509–521 (2002)CrossRefGoogle Scholar
  38. 38.
    Cao, F., Muse, P., Sur, F.: Extracting meaningful curves from images. J. Math. Imaging Vis. 22(2–3), 159–181 (2005)CrossRefMathSciNetGoogle Scholar
  39. 39.
    Bernstein, E.J., Amit, Y.: Part-based statistical models for object classification and detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’2005), vol. 2, pp. 734–740 (2005)Google Scholar
  40. 40.
    Volkov, V., Germer, R.: Straight edge segments localization on noisy images. In: International Conference on Image Process, Computer Vision and Pattern Recognition (IPCV’2010), vol. II, pp. 512–518 (2010)Google Scholar
  41. 41.
    Lu, X., Yaoy, J., Li, K., Li, L.: Cannylines: a parameter-free line segment detector. In: IEEE International Conference on Image Processing (ICIP’2015), pp. 507–511 (2015)Google Scholar
  42. 42.
    Liu, Zh., Wang, J., Liu, W.P.: Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS’2005), pp. 2250–2253 (2005)Google Scholar
  43. 43.
    Volkov, V., Germer, R., Oneshko, A., Oralov, D.: Object selection by grouping of straight edge segments in digital images. In: International Conference on Image Process, Computer Vision and Pattern Recognition (IPCV’2013), pp. 321–327 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.The Bonch-Bruevich State Telecommunications UniversitySt. PetersburgRussian Federation
  2. 2.State University of Aerospace InstrumentationSt. PetersburgRussian Federation

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