On the automation of gestalt perception in remotely sensed data
Michaelsen E.

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Gutleuthausstr. 1, 76275 Ettlingen, Germany

Аннотация:
Gestalt perception, the laws of seeing, and perceptual grouping is rarely addressed in the context of remotely sensed imagery. The paper at hand reviews the corresponding state as well in machine vision as in remote sensing, in particular concerning urban areas. Automatic methods can be separated into three types: 1) knowledge-based inference, which needs machine-readable knowledge, 2) automatic learning methods, which require labeled or un-labeled example images, and 3) perceptual grouping along the lines of the laws of seeing, which should be pre-coded and should work on any kind of imagery, but in particular on urban aerial or satellite data. Perceptual grouping of parts into aggregates is a combinatorial problem. Exhaustive enumeration of all combinations is intractable. The paper at hand presents a constant-false-alarm-rate search rationale. An open problem is the choice of the extraction method for the primitive objects to start with. Here super-pixel-segmentation is used.

Ключевые слова:
perceptual grouping, remote sensing, urban areas.

Цитирование:
Michaelsen E. On the automation of gestalt perception in remotely sensed data. Computer Optics 2018; 42(6): 1008-1014. DOI: 10.18287/2412-6179-2018-42-6-1008-1014
.

Литература:

  1. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S. SLIC Superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 2012; 34(11): 2274-2281. DOI: 10.1109/TPAMI.2012.120.
  2. Desolneux A, Moisan L, Morel JM. From Gestalt theory to image analysis: A probabilistic approach. Berlin: Springer; 2008. ISBN: 978-0-387-72635-9.
  3. Mumford D, Desolneux A. Pattern theory: The stochastic analysis of real-world signals. Natick, Massachusetts: CRC Press; 2010. ISBN: 978-1-56881-579-4.
  4. Pizlo Z, Li Y, Sawada T, Steinman RM. Making a machine that sees like us. Oxford: Oxford University Press; 2014. ISBN: 978-0-19-992254-3.
  5. Leyton M. Symmetry, causality, mind. Oxford: Oxford University Press; 1993. ISBN: 978-0-262-62131-1.
  6. Gruen A, Kuebler O, Agouris P, eds. Automatic extraction of man-made objects from aerial and space images. Basel: Birkhaeuser; 1995. ISBN: 978-3-7643-5264-6.
  7. Gruen A, Baltsavias EP, Henricson O, eds. Automatic extraction of man-made objects from aerial and space images (II). Basel: Birkhaeuser; 1997. ISBN: 978-3-7643-5788-7.
  8. Baltsavias EP, Gruen A, van Gool L, eds. Automatic extraction of man-made objects from aerial and space images (III). Lisse: Balkema Publishers; 2001. ISBN: 90-5809-252-6.
  9. Image understanding workshop: Proceedings of a workshop held at Cambridge. Two Volumes. Cambridge, Massachusetts: Morgan Kaufmann Pub; 1988. ISBN: 978-0-934613-68-2
  10. Wertheimer M. Untersuchungen zur Lehre der Gestalt. II [In German]. Psychologische Forschung 1923; 4(1): 301-350. DOI: 10.1007/BF00410640.
  11. Liu J, Slota G, Zheng G, Wu Z, Park M, Lee S, Rauschert I, Liu Y. Symmetry detection from real world images competition 2013: Summary and results. The IEEE Conference on Computer Vision and Pattern Recognition Workshops 2013: 200-205. DOI: 10.1109/CVPRW.2013.155.
  12. Funk C, Lee S, Oswald MR, Tsokas S, Shen W, Cohen A, Dickinson S, Liu J. 2017 ICCV Challenge: Detecting symmetry in the wild. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2017: 1692-1701. DOI: 10.1109/ICCVW.2017.198.
  13. Stilla U, Michaelsen E. Semantic modelling of man-made objects by production nets. In: Gruen A, Baltsavias EP, Henricsson O, eds. Automatic extraction of man-made objects from aerial and space images (II). Basel: Birkhäuser, 1997: 43-52. DOI: 10.1007/978-3-0348-8906-3_5.
  14. Michaelsen E, Jäger K, Roschkowski D, Doktorski L, Arens M. Object-oriented landmark recognition for UAV-navigation. Pattern Recognit Image Anal 2011; 21(2): 152-155. DOI: 10.1134/S1054661811020763.
  15. Soergel U, Michaelsen E, Thiele A, Cadario E, Thoennessen U. Stereo analysis of high-resolution SAR images for building height estimation in cases of orthogonal aspect directions. ISPRS Journal of Photogrammetry and Remote Sensing 2009; 64(5): 490-500. DOI: 10.1016/j.isprsjprs.2008.10.007.
  16. Michaelsen E, Muench D, Arens M. Recognition of symmetry structure by use of Gestalt algebra. 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops 2013: 206-210. DOI: 10.1109/CVPRW.2013.37.
  17. Michaelsen E, Yashina VV. Simple gestalt algebra. Pattern Recognition and Image Analysis 2014; 24(4): 542-551. DOI: 10.1134/S1054661814040154.
  18. Michaelsen E, Muench D, Arens M. Searching remotely sensed images for meaningful nested gestalten. Int Arch Photogramm Remote Sens Spatial Inf Sci 2016; XLI-B3: 899-903. DOI: 10.5194/isprsarchives-XLI-B3-899-2016.
  19. Michaelsen E. 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 2016; 83(2): 169-177. DOI: 10.1016/j.patrec.2016.06.004.

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