Abstract
There is an important algorithmic step between the input in the form of images in pixel raster formats and, possibly hierarchical, perceptual grouping: the extraction of primitive Gestalten to start with. Often a failure in recognizing a symmetry, which is present in an image either as evident to human observers, or as given by some ground-truth, cannot be blamed on the Gestalt operations. Instead, too much information is lost already in the extraction of the primitives. This chapter lists a set of possibilities, including very simple and fast methods such as threshold segmentation as well as sophisticated automatic feature extraction methods such as scale-invariant feature transform (SIFT), or the maximally stable extremal regions (MSER). It is of course also possible to use machine learning methods for primitive extraction, and the chapter includes some discussion on this topic as well. In particular self-organizing maps are proposed for color images and hyper-spectral images.
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
Notes
- 1.
Some changes are necessary as compared to standard image segmentation, because the self-organizing map has torus topology.
References
Loy G, Eklundh J (2006) Detecting symmetry and symmetric constellations of features. In: European conference on computer vision (ECCV), pp 508–521
Pătrăucean V, von Gioi RG, Ovsjanikov M (2013) Detection of mirror-symmetric image patches. In: 2013 IEEE conference on computer vision and pattern recognition workshops, pp 211–216
Kondra S, Petrosino A, Iodice S (2013) Multi-scale kernel operations for reflection and rotation symmetry: further achievements. In: CVPR 2013 competition on symmetry detection
Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel, methods. Trans Pattern Anal Mach Intell 34(11):2274–2281
Michaelsen E, Arens M (2017) Hierarchical grouping using gestalt assessments. In: CVPR 2017, workshops, detecting symmetry in the wild
Matas J, Chum O, Urban M, Pajdla T (2002) Robust wide baseline stereo from maximally stable extremal regions. In: British machine vision conference BMVC 2002, pp 384–396
Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the international conference on computer vision (ICCV ’99), pp 1150–1157
Michaelsen E (2014) Gestalt algebra—a proposal for the formalization of gestalt perception and rendering. Symmetry 6(3):566–577
Krüger N, Lappe M, Wörgötter F (2004) Biologically motivated multi-modal processing of visual primitives. Interdisc J Artif Intell Simul Behav 1(5):417–427
Felsberg M, Sommer G (2001) The monogenic signal. IEEE Trans. Signal Process 49(12):3136–3144
Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69
Michaelsen E (2016) Self-organizing maps and gestalt organization as components of an advanced system for remotely sensed data: an example with thermal hyper-spectra. Pattern Recogn Lett 83(2):169–177
2018 ieee grss data fusion contest (2014). http://www.grss-ieee.org/community/technical-committees/data-fusion/2014-ieee-grss-data-fusion-contest
Sörgel U (ed) (1990) Radar remote sensing of urban areas. Springer
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Michaelsen, E., Meidow, J. (2019). Primitive Extraction. In: Hierarchical Perceptual Grouping for Object Recognition. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-04040-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-04040-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04039-0
Online ISBN: 978-3-030-04040-6
eBook Packages: Computer ScienceComputer Science (R0)