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A Brief Survey on 2D and 3D Feature Extraction

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Reconstruction and Analysis of 3D Scenes

Abstract

The detection, description, and comparison of data contents is of great importance for a variety of research domains. In this chapter, we intend to enlighten these issues in the context of 2D imagery and 3D point cloud data. Hence, we address fundamental ideas by focusing on several questions: How can we detect an object? How can we describe an object? What makes an object memorable? According to which criteria can we recognize the same object or similar objects? How could similarity be defined? In order to answer such questions which, in turn, allow us to infer a deeper understanding of the respective scene, we describe how distinctive characteristics contained in respective 2D or 3D data may be represented as features, and we thereby categorize features according to different feature types. For this purpose, we first derive a general definition for characterizing a feature. Subsequently, we focus on feature extraction from 2D imagery as well as feature extraction from 3D point cloud data. Based on these findings, we discuss the motivation for involving specific features in the scope of our work and, finally, we provide concluding remarks.

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Notes

  1. 1.

    Considering the function of rotation in depth of a plane away from a viewer, some of these features are reported to be stable up to a change of \(50^{\circ }\) in viewpoint [23].

References

  1. Ahmed N, Natarajan T, Rao KR (1974) Discrete cosine transform. IEEE Trans Comput C–23(1):90–93

    Article  MathSciNet  MATH  Google Scholar 

  2. Al-Manasir K, Fraser CS (2006) Registration of terrestrial laser scanner data using imagery. Photogramm Rec 21(115):255–268

    Article  Google Scholar 

  3. Aristotle (384–322 B.C.) Metaphysica

    Google Scholar 

  4. Attneave F (1954) Some informational aspects of visual perception. Psychol Rev 61(3):183–193

    Article  Google Scholar 

  5. Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit 13(2):111–122

    Article  MATH  Google Scholar 

  6. Braund MJ (2008) The structures of perception: an ecological perspective. Kritike 2(1):123–144

    Article  Google Scholar 

  7. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  8. Caputo B, Hayman E, Fritz M, Eklundh J-O (2010) Classifying materials in the real world. Image Vis Comput 28(1):150–163

    Article  Google Scholar 

  9. Chen CH, Pau LF, Wang PSP (1998) Handbook of pattern recognition and computer vision, 2nd edn. World Scientific Publishing, Singapore

    MATH  Google Scholar 

  10. Dahl AL, Aanæs H, Pedersen KS (2011) Finding the best feature detector-descriptor combination. In: Proceedings of the IEEE international conference on robotics and automation, pp 318–325

    Google Scholar 

  11. Designs Act (2003) Office of legislative drafting and publishing. Attorney-General’s Department, Canberra, Australia

    Google Scholar 

  12. Flusser J, Suk T, Zitova B (2009) Moments and moment invariants in pattern recognition. Wiley, Chichester

    Book  MATH  Google Scholar 

  13. Freeman W, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906

    Article  Google Scholar 

  14. Gibson JJ (1950) The perception of the visual world. Houghton Mifflin, Boston

    Google Scholar 

  15. Gibson JJ (1961) Ecological optics. Vis Res 1(3–4):253–262

    Article  Google Scholar 

  16. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804

    Article  Google Scholar 

  17. Hough PVC (1962) Method and means for recognizing complex patterns. US Patent 3069654

    Google Scholar 

  18. Hu M-K (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179–187

    Article  MATH  Google Scholar 

  19. Isola P, Xiao J, Torralba A, Oliva A (2011) What makes an image memorable? In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 145–152

    Google Scholar 

  20. Koffka K (1935) Principles of gestalt psychology. Harcourt, Brace & World, New York

    Google Scholar 

  21. Leung T, Malik J (1999) Recognizing surfaces using three-dimensional textons. In: Proceedings of the IEEE international conference on computer vision, vol 2, pp 1010–1017

    Google Scholar 

  22. Li J, Allinson NM (2008) A comprehensive review of current local features for computer vision. Neurocomputing 71(10–12):1771–1787

    Article  Google Scholar 

  23. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  24. Ma Y, Soatto S, Košecká J, Sastry SS (2005) An invitation to 3-D vision: from images to geometric models. Springer, New York

    MATH  Google Scholar 

  25. Nixon MS, Aguado AS (2008) Feature extraction and image processing, 2nd edn. Academic Press, Oxford

    Google Scholar 

  26. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  27. Prewitt JMS, Mendelsohn ML (1966) The analysis of cell images. Ann NY Acad Sci 128(3):1035–1053

    Article  Google Scholar 

  28. Roberts LG (1965) Machine perception of three-dimensional solids. In: Tippett J, Berkowitz D, Clapp L, Koester C, Vanderburgh A (eds) Optical and electro-optical information processing. MIT Press, Cambridge, pp 159–197

    Google Scholar 

  29. Schmid C (2001) Constructing models for content-based image retrieval. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, vol 2, pp 39–45

    Google Scholar 

  30. Sheng Y, Shen L (1994) Orthogonal Fourier–Mellin moments for invariant pattern recognition. J Opt Soc Am A 11(6):1748–1757

    Article  Google Scholar 

  31. Smuts J (1926) Holism and evolution. The MacMillan Company, New York

    Google Scholar 

  32. Sobel IE (1970) Camera models and machine perception. PhD thesis, Stanford University, Stanford, USA

    Google Scholar 

  33. Teague MR (1980) Image analysis via the general theory of moments. J Opt Soc Am 70(8):920–930

    Article  MathSciNet  Google Scholar 

  34. Teh C-H, Chin RT (1988) On image analysis by the methods of moments. IEEE Trans Pattern Anal Mach Intell 10(4):496–513

    Article  MATH  Google Scholar 

  35. Turner MR (1986) Texture discrimination by Gabor functions. Biol Cybern 55(2):71–82

    Google Scholar 

  36. Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: a survey. Found Trends Comput Graph Vis 3(3):177–280

    Article  Google Scholar 

  37. Van Gool L, Dewaele P, Oosterlinck A (1985) Texture analysis Anno 1983. Comput Vis Graph Image Process 29(3):336–357

    Article  Google Scholar 

  38. Varma M, Zisserman A (2002) Classifying images of materials: achieving viewpoint and illumination independence. In: Proceedings of the European conference on computer vision, vol 3, pp 255–271

    Google Scholar 

  39. von Ehrenfels C (1890) Über Gestaltqualitäten. Vierteljahrsschrift für wissenschaftliche Philosophie 14:249–292

    Google Scholar 

  40. Weidner U (2005) Remote sensing systems—An overview focussing on environmental applications. In: Proceedings of the EnviroInfo 2005 workshop on tools for emergencies and disaster management, pp 1–8

    Google Scholar 

  41. Weinmann M (2013) Visual features—From early concepts to modern computer vision. In: Farinella GM, Battiato S, Cipolla R (eds) Advanced topics in computer vision. Advances in computer vision and pattern recognition. Springer, London, pp 1–34

    Google Scholar 

  42. Weinmann M (2016) Feature extraction from images and point clouds: fundamentals, advances and recent trends. Whittles Publishing, Dunbeath (to appear)

    Google Scholar 

  43. Weinmann Mi, Klein R (2015) A short survey on optical material recognition. In: Proceedings of the eurographics workshop on material appearance modeling: issues and acquisition, pp 35–42

    Google Scholar 

  44. Weinmann Mi, Klein R (2015) Advances in geometry and reflectance acquisition (course notes). In: Proceedings of the SIGGRAPH Asia 2015 courses, pp 1:1–1:71

    Google Scholar 

  45. Weinmann M, Hoegner L, Leitloff J, Stilla U, Hinz S, Jutzi B (2012) Fusing passive and active sensed images to gain infrared-textured 3D models. Int Arch Photogramm Remote Sens Spat Inf Sci XXXIX-B1:71–76

    Google Scholar 

  46. Weinmann M, Leitloff J, Hoegner L, Jutzi B, Stilla U, Hinz S (2014) Thermal 3D mapping for object detection in dynamic scenes. ISPRS Ann Photogramm Remote Sens Spat Inf Sci II-1:53–60

    Google Scholar 

  47. Weinmann Mi, Gall J, Klein R (2014) Material classification based on training data synthesized using a BTF database. In: Proceedings of the European conference on computer vision, vol III, pp 156–171

    Google Scholar 

  48. Wertheimer M (1912) Experimentelle Studien über das Sehen von Bewegung. Zeitschrift für Psychologie 61(1):161–265

    Google Scholar 

  49. Wertheimer M (1923) Untersuchungen zur Lehre von der Gestalt. Psychologische Forschung 4:301–350

    Article  Google Scholar 

  50. Westheimer G (1999) Gestalt theory reconfigured: Max Wertheimer’s anticipation of recent developments in visual neuroscience. Perception 28(1):5–15

    Article  Google Scholar 

  51. Yu T-H, Woodford OJ, Cipolla R (2013) A performance evaluation of volumetric 3D interest point detectors. Int J Comput Vis 102(1–3):180–197

    Article  Google Scholar 

  52. Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recognit 37(1):1–19

    Article  Google Scholar 

  53. Zhang J, Tan T (2002) Brief review of invariant texture analysis methods. Pattern Recognit 35(3):735–747

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Martin Weinmann

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  1. Martin Weinmann
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Weinmann, M. (2016). A Brief Survey on 2D and 3D Feature Extraction. In: Reconstruction and Analysis of 3D Scenes. Springer, Cham. https://doi.org/10.1007/978-3-319-29246-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-29246-5_3

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