Abstract
The segmentation or the geometric analysis of specular object is known as a difficult problem in the computer vision domain. It is also true for the problem of line detection where the specular reflection implies numerous false positive line detection or missing lines located on the dark parts of the object. This limitation reduces its potential use for concrete industrial applications where metallic objects are frequent. In this work, we propose to overcome this limitation by proposing a new strategy which is not based on the image gradient as usually, but exploits the image intensity profile defined inside a parallel strip primitive. Associated to a digital straight segment recognition algorithm robust to noise, we demonstrate the efficiency of our proposed method with a real industrial application.
N. Aubry—This work was supported by the French National Agence of Research and Technology (ANRT).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
DGtal: Digital Geometry tools and algorithms library. http://libdgtal.org
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Akinlar, C., Topal, C.: EDLines: a real-time line segment detector with a false detection control. Pattern Recogn. Lett. 32(13), 1633–1642 (2011). http://ceng.anadolu.edu.tr/CV/EDLines/demo.aspx
Alvarez, L., Baumela, L., Márquez-Neila, P., Henríquez, P.: A real time morphological snakes algorithm. Image Process. Line 2, 1–7 (2012)
Bhowmick, P., Bhattacharya, B.B.: Fast polygonal approximation of digital curves using relaxed straightness properties. IEEE Trans. Pattern Anal. Mach. Intell. 29(9), 1590–1602 (2007)
Buzer, L.: A simple algorithm for digital line recognition in the general case. Pattern Recogn. 40(6), 1675–1684 (2007)
Chang, J., Raskar, R., Agrawal, A.: 3D pose estimation and segmentation using specular cues. In: CVPR 2009, pp. 1706–1713 (2009)
Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watersheds: a new image segmentation framework extending graph cuts, random walker and optimal spanning forest. In: IEEE 12th International Conference on Computer Vision, pp. 731–738. IEEE (2009)
Cousty, J., Najman, L.: Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 272–283. Springer, Heidelberg (2011)
Cousty, J., Najman, L., Perret, B.: Constructive links between some morphological hierarchies on edge-weighted graphs. In: Hendriks, C.L.L., Borgefors, G., Strand, R. (eds.) ISMM 2013. LNCS, vol. 7883, pp. 86–97. Springer, Heidelberg (2013)
Debled-Rennesson, I., Feschet, F., Rouyer-Degli, J.: Optimal blurred segments decomposition of noisy shapes in linear times. Comput. Graphics 30, 30–36 (2006)
Durou, J.D., Falcone, M., Sagona, M.: Numerical methods for shape-from-shading: a new survey with benchmarks. Comput. Vis. Image Underst. 109(1), 22–43 (2008)
Even, P., Malavaud, A.: Semi-automated edge segment specification for an interactive modelling system of robot environments. Int. Arch. Photogramm. Remote Sens. 33(B5), 222–229 (2000)
Getreuer, P.: Chan-Vese segmentation. Image Process. Line 2, 214–224 (2012)
Grompone von Gioi, R., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a line segment detector. Image Process. Line 2, 35–55 (2012)
Haldo, S., Juan, C.: A review of classic edge detectors. Image Process. Line 5, 90–123 (2015). doi:10.5201/ipol.2015.35
Kerautret, B., Lachaud, J.O.: Curvature estimation along noisy digital contours by approximate global optimization. Pattern Recogn. 42(10), 2265–2278 (2009)
Kerautret, B., Even, P.: Blurred segments in gray level images for interactive line extraction. In: Wiederhold, P., Barneva, R.P. (eds.) IWCIA 2009. LNCS, vol. 5852, pp. 176–186. Springer, Heidelberg (2009)
Naegel, B., Passat, N.: Interactive segmentation based on component-trees. Image Process. Line 4, 89–97 (2014)
Nguyen, T.P., Debled-Rennesson, I.: A discrete geometry approach for dominant point detection. Pattern Recogn. 44(1), 32–44 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Aubry, N., Kerautret, B., Debled-Rennesson, I., Even, P. (2015). Parallel Strip Segment Recognition and Application to Metallic Tubular Object Measure. In: Barneva, R., Bhattacharya, B., Brimkov, V. (eds) Combinatorial Image Analysis. IWCIA 2015. Lecture Notes in Computer Science(), vol 9448. Springer, Cham. https://doi.org/10.1007/978-3-319-26145-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-26145-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26144-7
Online ISBN: 978-3-319-26145-4
eBook Packages: Computer ScienceComputer Science (R0)