A new fast efficient non-maximum suppression algorithm based on image segmentation

Oday Jasim Al-Furaiji, Anh Tuan Nguyen, Viktar Yurevich Tsviatkou

Abstract


In this paper, the problem of finding local extrema in grayscale images is considered. The known non-maximum suppression algorithms provide high speed, but only single-pixel extrema are extracted, skipping regions formed by multi-pixel extrema. Morphological algorithms allow to extract all extrema but its maxima and minima are processed separately with high computational complexity by iterative processing based on image reconstruction using image morphological dilation and erosion. In this paper a new fast efficient non-maximum suppression algorithm based on image segmentation and border analysis is proposed. The proposed algorithm considers homogeneous areas, which are formed by multi-pixel extrema and are the local maxima or minima in relation to adjacent areas, eliminating iterative processing of non-extreme pixels and assigning label numbers to local extrema during their search. The proposed algorithm allowed to increase the accuracy of local extremum extraction in comparison with known non-maximum suppression algorithms and reduce the computational complexity and the use of RAM in comparison with the morphological algorithms.


Keywords


Local extrema; Local maxima; Feature point; Non-maximum suppression; Image segmentation; Region growing

References


A. Rosenfeld and A. Kak, Digital Picture Processing. Academic Press, 1976, p. 457.

L. Kitchen and A. Rosenfeld, “Gray-Level Corner Detection,” Pattern Recognition Letters, vol. 1, no. 2. pp. 92–102, 1982.

C. A. Harris and M. Stephens, “Combined corner and edge detector,” Proceeding of the Fourth Alvey Vision Conference, Manchester, 31 August-2 September 1988, pp.147–151.

D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, November 2004.

K. Mikolajczyk and C. Schmid, “Scale and Affine Invariant Interest Point Detectors,” International Journal of Computer Vision, vol. 60, pp. 63–86, 2004.

M. Van Herk, “Fast Algorithm for Local Minimum and Maximum Filters on Rectangular and Octagonal Kernels,” Pattern Recognition Letters, vol. 13, no. 7, pp. 517–521, 1992.

J. Gil and M. Werman, “Computing 2-D Min, Median, and Max Filters,” IEEE Trans. on PAMI, vol. 15, no. 5, pp. 504–507, 1993.

M. Brown, et al., “Multi-Image Matching Using Multi-Scale Oriented Patches,” Proceeding of Computer Vision and Pattern Recognition (CVPR’05) 1 (2005), pp. 510–517.

A. Neubeck and L. Van Gool, “Efficient Non-Maximum Suppression,” Proceeding of ICPR 3, 2006, pp. 850–855.

W. A. Forstner and E. Gulch, “Fast Operator for Detection and Precise Locations of Distinct Points, Corners, and Centres of Circular Features,” Proceeding of Intercommission Conference on Fast Processing of Photogrammetric Data, 1987, pp. 281–305.

Tuan Q. Pham, “Non-Maximum Suppression Using Fewer than 2 Comparisons per Pixel,” Advanced Concepts for Intelligent Vision Systems (ACIVS) 12, Sydney, Australia, December 13-16 (2010), pp. 438–451.

P. Soille, Morphological Image Analysis: Principles and Applications. Springer, 2006.

T. Lindeberg, “Scale Selection Properties of Generalized Scale-Space Interest Point Detectors,” Journal of Mathematical Imaging and Vision, vol. 46, no. 2, pp. 177–210, 2013.

T. Lindeberg, “Image Matching Using Generalized Scale-Space Interest Points,” Journal of Mathematical Imaging and Vision, vol. 52, no. 1, pp. 3–36, 2015.

H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “SURF: Speeded Up Robust Features,” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008.

T. Lindeberg, “Detecting Salient Blob-like Image Structures and their Scales with a Scale-Space Primal Sketch: a Method for Focus-of-Attention,” International Journal of Computer Vision, vol. 11, no. 3,

pp. 283–318, 1993.

T. Lindeberg, Scale-Space Theory in Computer Vision. Springer, 1994, p. 423.

J. Matas, et al., “Robust Wide Baseline Stereo from Maximally Stable Extremal Regions,” Proceedings of the 13th British Machine Vision Conference, UK, 2–5 September 2002. UK, 2002, pp. 384–396.

R. M. Haralick and L. G. Shapiro, “Image Segmentation Techniques,” Computer Vision, Graphics and Image Processing, vol. 29, no. 1, pp. 100–132, 1985.

K. Karu, et al., “Is There Any Texture in the Image?,” Pattern Recognition, vol. 29, no. 9, pp. 1437–1446, 1996.

Qianyu Zhang, et al., “A Performance Analysis for Real-time Flood Monitoring using Image-based Processing,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 17, no. 2, pp. 793-803, Feb 2020.

Shihab Hamad Khaleefah, et al., “Review of Local Binary Pattern Operators in Image Feature Extraction,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 19, no. 1, pp. 23-31, July 2020.

C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of Interest Point Detectors,” International Journal of Computer Vision, vol. 37, no. 2, pp. 151–172, 2000.

Aziah Ali, et al., “Retinal Blood Vessel Segmentation from Retinal Image using B-COSFIRE and Adaptive Thresholding,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 13, no. 3, pp. 1199-1207, March 2019.

M. Brown, et al., “Multi-Image Matching using Multi-Scale Oriented Patches,” Proceeding of Computer Vision and Pattern Recognition (CVPR’05), 2005, pp. 510–517.




DOI: http://doi.org/10.11591/ijeecs.v19.i2.pp%25p
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