Object Recognition in Humans and Machines

  • Christian Wallraven
  • Heinrich H. Bülthoff


The question of how humans learn, represent and recognize objects has been one of the core questions in cognitive research. With the advent of the field of computer vision — most notably through the seminal work of David Marr — it seemed that the solution lay in a three-dimensional (3D) reconstruction of the environment (Marr 1982, see also one of the first computer vision systems built by Roberts et al. 1965). The success of this approach, however, was limited both in terms of explaining experimental results emerging from cognitive research as well as in enabling computer systems to recognize objects with a performance similar to humans.


Object Recognition Visual Input Object Representation IEEE Trans Pattern Anal Psychophysical Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aloimonos JY, Weiss I, Bandopadhay A (1987) Active vision. Int J Comput Vis 1:333–356CrossRefGoogle Scholar
  2. Biederman I (1987) Recognition-by-components: a theory of human image understanding. Psychol Rev 94:115–147PubMedCrossRefGoogle Scholar
  3. Blanz V, Vetter T (1999) A morphable model for the synthesis of 3d faces. Proc ACM SIGGRAPH 1999:187–194Google Scholar
  4. Bülthoff HH, Edelman S (1992) Psychophysical support for a 2-d view interpolation theory of object recognition. Proc Natl Acad Sci U S A 89:60–64PubMedCrossRefGoogle Scholar
  5. Foster DH, Gilson SJ (2002) Recognizing novel three-dimensional objects by summing signals from parts and views. Proc R Soc Lond B 269:1939–1947CrossRefGoogle Scholar
  6. Graf M (2002) Form, space and object. Geometrical transformations in object recognition and categorization. Wissenschaftlicher Verlag Berlin, BerlinGoogle Scholar
  7. Kirby M, Sirovich L (1990) Applications of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12:103–108CrossRefGoogle Scholar
  8. Koenderink J, van Doorn A (1979) The internal representation of solid shape with respect to vision. Biol Cybern 32:211–216PubMedCrossRefGoogle Scholar
  9. Krieger G, Rentschler I, Hauske G, Schill K, Zetzsche C (2000) Object and scene analysis by saccadic eye-movements: an investigation with higher-order statistics. Spat Vis 13:201–214PubMedCrossRefGoogle Scholar
  10. Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110CrossRefGoogle Scholar
  11. Marr D (1982) Vision. Freeman Publishers, San FranciscoGoogle Scholar
  12. Metta G, Panerai F, Sandini G (2000) Babybot: a biologically inspired developing robotic agent. Proc. Sixth International Conference on the Simulation of Adaptive Behaviors, 1–10Google Scholar
  13. Miyashita Y (1988) Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature 335:817–820PubMedCrossRefGoogle Scholar
  14. Newell FN, Ernst MO, Tjan BS, Bülthoff HH (2001) Viewpoint dependence in visual and haptic object recognition. Psychol Sci 12:37–42PubMedCrossRefGoogle Scholar
  15. Roberts L (1965) Machine perception of three-dimensional solids. In: Tippett J, Clapp L (Eds) Optical and electro-optical information processing. MIT Press, Cambridge MA, pp 159–197Google Scholar
  16. Schmid C, Mohr R (1997) Local greyvalue invariants for image retrieval. IEEE Trans Pattern Anal Mach Intell 19:530–535CrossRefGoogle Scholar
  17. Stone JV (1999) Object recognition: view-specificity and motion-specificity. Vision Res 39:4032–4044PubMedCrossRefGoogle Scholar
  18. Swain M, Ballard D (1991) Color indexing. Int J Comput Vis 7:11–32CrossRefGoogle Scholar
  19. Tarr M, Bülthoff HH (1998) Object recognition in man, monkey, and machine. MIT Press, Cambridge MAGoogle Scholar
  20. Tomasi C, Kanade T (1991) Detection and tracking of point features. Carnegie-Mellon Tech Report CMU-CS-91-132Google Scholar
  21. Troje NF, Bülthoff HH (1996) Face recognition under varying pose: the role of texture and shape. Vision Res 36:1761–1771PubMedCrossRefGoogle Scholar
  22. Ullman S, Vidal-Naquet M, Sali E (2002) Visual features of intermediate complexity and their use in classification. Nat Neurosci 5:682–687PubMedGoogle Scholar
  23. Wallis GM (2002) The role of object motion in forging long-term representations of objects. Vis Cogn 9:233–247CrossRefGoogle Scholar
  24. Wallis GM, Bülthoff HH (2001) Effects of temporal association on recognition memory. Proc Natl Acad Sci U S A 98:4800–4804PubMedCrossRefGoogle Scholar
  25. Wallraven C, Bülthoff HH (2001) Automatic acquisition of exemplar-based representations for recognition from image sequences. In Proc. CVPR’01 — Workshop on Models versus ExemplarsGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Christian Wallraven
    • 1
  • Heinrich H. Bülthoff
    • 1
  1. 1.Max Planck Institute for Biological CyberneticsTübingenGermany

Personalised recommendations