Abstract
Attribute filters and extinction filters are connected filters used to simplify greyscale images. The first kind is widely explored in the image processing literature, while the second is not much explored yet. Both kind of filters can be efficiently implemented on the max-tree. In this work, we compare these filters in terms of processing time, simplification of flat zones and reduction of max-tree nodes. We also compare their influence as a pre-processing step before extracting affine regions used in matching and pattern recognition. We perform repeatability tests using extinction filters and attribute filters, set to preserve the same number of extrema, as a pre-processing step before detecting Hessian-Affine and Maximally Stable Extremal Regions (MSER) affine regions. The results indicate that using extinction filters as pre-processing obtain a significantly higher (more than 5% on average) number of correspondences on the repeatability tests than the attribute filters. The results in processing natural images show that preserving 5% of images extrema using extinction filters achieve on average 95% of the number of correspondences compared to applying the affine region detectors directly to the unfiltered images, and the average number of max-tree nodes is reduced by a factor greater than 3. Therefore, we can conclude that extinction filters are better than attribute filters with respect to preserving the number of correspondences found by affine detectors, while simplifying the max-tree structure. The use of extinction filters as a pre-processing step is recommended to accelerate image recognition tasks.
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
Preview
Unable to display preview. Download preview PDF.
References
Salembier, P., Serra, J.: Flat zones filtering, connected operators, and filters by reconstruction. IEEE Transactions on Image Processing 4(8), 1153–1160 (1995), doi:10.1109/83.403422.
Salembier, P., Wilkinson, M.: Connected operators. IEEE Signal Processing Magazine 26(6), 136–157 (2009), doi:10.1109/MSP.2009.934154
Jones, R.: Connected Filtering and Segmentation Using Component Trees. Computer Vision and Image Understanding 75, 215–228 (1999)
Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Transactions on Image Processing 7(4), 555–570 (1998), doi:10.1109/83.663500
Souza, R., Rittner, L., Machado, R., Lotufo, R.: Maximal Max-Tree Simplification. In: 22nd International Conference on Pattern Recognition (ICPR), pp.3132–3137 (August 2014), doi: 10.1109/ICPR.2014.540
Berger, Ch., Geraud, T., Levillain, R., Widynski, N., Baillard, A., Bertin, E.:Effective Component Tree Computation with Application to Pattern Recognition in Astronomical Imaging. In: IEEE International Conference on Image Processing, vol. 4, pp. IV-41–IV-44 (2007), doi: 10.1109/ICIP.2007.4379949
Najman, L., Couprie, M.: Building the Component Tree in Quasi-Linear Time. IEEE Transactions on Image Processing 15(11), 3531–3539 (2006), doi:10.1109/TIP.2006.877518
Wilkinson, M., Gao, H., Hesselink, W., Jonker, J., Meijster, A.: Concurrent Computation of Attribute Filters on Shared Memory Parallel Machines. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1800–1813 (2008), doi:10.1109/TPAMI.2007.70836
Carlinet, E., Geraud, T.: A Comparative Review of Component Tree Computation Algorithms. IEEE Transactions on Image Processing 23(9), 3885–3895 (2014), doi:10.1109/TIP.2014.2336551
Salembier, P., Oliveras, A., Maragos, P., Schafer, R., Butt, M.: Practical extensions of connected operators. In: Proc. Int. Symp. Mathematical Morphology, pp. 97–110 (1996)
Vincent, L.: Morphological Area Opening and Closings for Greyscale Images. In: Proc. Shape in Picture, 92-NATO Workshop (1992)
Vachier, C.: Extinction value: a new measurement of persistence. In: IEEE Workshop on Nonlinear Signal and Image Processing, vol. I, pp. 254–257 (1995)
Grimaud, M.: A new measure of contrast: dynamics. In: Proc. SPIE, vol. 1769, pp. 292–305 (1992)
Silva, A., Lotufo, R.: New Extinction Values from Efficient Construction and Analysis of Extended Attribute Component Tree. In: XXI Brazilian Symposium on Computer Graphics and Image Processing, October 12-15, pp. 204–211 (2008), doi:10.1109/SIBGRAPI.2008.8
Xu, Y., Geraud, T., Najman, L.: Morphological filtering in shape spaces: Applications using tree-based image representations. In: 21st International Conference on Pattern Recognition (ICPR), November 11-15, pp. 485–488 (2012)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, pp. 384–393 (2002)
Donoser, M., Bischof, H.: Efficient Maximally Stable Extremal Region (MSER) Tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 17-22, vol. 1, pp. 553–560 (2006), doi: 10.1109/CVPR.2006.107
Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65 (2005)
Wang, Z., Bovik, C., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)
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
Souza, R., Rittner, L., Machado, R., Lotufo, R. (2015). A Comparison Between Extinction Filters and Attribute Filters. In: Benediktsson, J., Chanussot, J., Najman, L., Talbot, H. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2015. Lecture Notes in Computer Science(), vol 9082. Springer, Cham. https://doi.org/10.1007/978-3-319-18720-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-18720-4_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18719-8
Online ISBN: 978-3-319-18720-4
eBook Packages: Computer ScienceComputer Science (R0)