Laboratorij za umetne vizualne spoznavne sisteme

Laboratorij se ukvarja s splošnim raziskovanjem na področju umetnih vizualnih spoznavnih sistemov, s poudarkom na vizualnem učenju ter prepoznavanju. Raziskave na področju umetnih vizualnih spoznavnih sistemov se osredotočajo na teorije na različnih nivojih abstrakcije, glede na zahteve, arhitekturo, oblike predstavitev, vrste ontologij in znanja ter vrste mehanizmov, ki so pomembni za integracijo in upravljanje vizualnih sistemov. Vizualni spoznavni sistemi v tem kontekstu obsegajo funkcionalnosti za predstavitev znanja, za učenje, sklepanje o dogodkih in strukturah, prepoznavanje in kategorizacijo ter ciljno specifikacijo. Vse zadevajo semantiko odnosov med vizualnim agentom in njegovim okoljem, kar zahteva multidisciplinarno razumevanje spoznavnih procesov, vključujoč študije na področju spoznavne psihologije, nevroznanosti in filozofije uma.

Uporaba vključuje prepoznavo objektov, prizorov ter aktivnosti pri vizualnih spoznavnih nalogah kot so vizualni nadzor, vizualna lokalizacija v urbanih okoljih ter pri aplikacijah v drugih spoznavnih sistemih, kot so mobilni roboti in spoznavni asistenti.


Sodelavci

ViCoS

Izbrane objave

  • A. Leonardis, A. Gupta, and R. Bajcsy. Segmentation of range images as the search for geometric parametric models. International Journal of Computer Vision, 14: 253–277, 1995.
  • R. Bajcsy, S. Wook Lee, and A. Leonardis. Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation. International Journal of Computer Vision, 17(3): 241–272, 1996.
  • A. Leonardis, A. Jaklič, and F. Solina. Superquadrics for segmentation and modeling range data. IEEE Transactions on Pattern Recognition and Machine Intelligence, 19(11): 1289–1295, November 1997.
  • F. Solina and A. Leonardis. Proper scale for modeling visual data. Image and Vision Computing Journal, 16: 89–98, 1998.
  • H. Bischof and A. Leonardis. Finding optimal neural networks for land use classification. IEEE Transactions on Geoscience and Remote Sensing, 36(1): 337–341, January 1998.
  • A. Leonardis and H. Bischof. An efficient MDL-based construction of RBF networks. Neural Networks, 11(5): 963–973, July 1998.
  • W.G. Kropatsch, A. Leonardis, and H. Bischof. Hierarchical, adaptive, and robust methods for image understanding. Surveys on Mathematics for Industry, 9: 1–47, 1999. Springer-Verlag.
  • H. Bischof, A. Leonardis and Alexander Selb. MDL principle for robust vector quantisation. Pattern analysis and applications, 2(1): 59–72, 1999.
  • A. Leonardis and H. Bischof. Robust recognition using eigenimages. Computer Vision and Image Understanding, 78(1): 99–118, 2000.
  • A. Jaklič, A. Leonardis and F. Solina. Segmentation and Recovery of Superquadrics, volume 20 of Computational imaging and vision. Kluwer, Dordrecth, 2000.
  • A. Leonardis, H. Bischof, and J. Maver. “Multiple Eigenspaces”, Pattern Recognition, 35, no. 11, pp. 2613–2627, 2002. Twenty-Ninth Annual Pattern Recognition Society Award. Selected as the most original manuscript from all 2002 Pattern Recognition issues.
  • T. Werner, T. Pajdla, V. Hlavác, A. Leonardis, M. Matoušek. Selection of reference images for image-based scene representations. Computing, vol. 68, pp. 163–180, 2002.
  • J. Maver, A. Leonardis. Recognizing 2-tone images in grey-level parametric eigenspaces, Pattern recogition. letters, 23, pp. 1631–1640, 2002.
  • M. Jogan, E. Žagar, A. Leonardis. Karhunen-Loeve expansion of a set of rotated templates. IEEE Transactions on image processing, 2003, Vol 12, No 7, pp. 817– 825, 2003.
  • M. Jogan, A. Leonardis. Robust Localization using an Omnidirectional Appearance- based Subspace Model of Environment. Robotics and Autonomous Systems, Volume 45, Issue 1, pp. 51–72, Elsevier Science, 2003.
  • B. Kverh, A. Leonardis. A generalisation of model selection criteria. Pattern analysis and applications, pp. 51–65, 2004.
  • H. Bischof, H. Wildenauer, A. Leonardis. Illumination insensitive recognition using eigenspaces. ComputerVision and Image Understanding, Volume 95, no. 1, pp. 86–104, 2004.
  • D. Skočaj, H. Bischof, A. Leonardis. A robust PCA algorithm for building representations from panoramic images. Computer vision - ECCV 2002: proceedings: part IV, (Lecture notes in computer science, 2353), pp. 761–775, 2002.
  • D. Skočaj, A. Leonardis. Weighted and robust incremental method for subspace learning. Ninth IEEE international conference on computer vision, 13–16 October 2003, Nice, France. Vol. 2., pp. 1494–1501, 2003.
  • M. Peternel, A. Leonardis. Visual learning and recognition of a probabilistic spatio-temporal model of cyclic human locomotion. ICPR 2004, pp. 146–149, 2004.

Kje smo?

Laboratorij za umetne vizualne spoznavne sisteme ima prostore na Večni poti 113 v drugem nadstropju.

 


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