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Computational Vision at CB, CSC, KTH

What are the properties of visual patterns that make it possible to see? The intensity or colour of the light distribution that reaches the fovea or a camera sensor is strongly dependent on both the geometric relations between the objects in the world vs. the observer and on the usually unknown external illumination. Nevertheless, we perceive the world as stable and use visual perception based on brightness patterns for inferring properties of objects in the surrounding world.

At our lab we perform basic research on theories and methods for computing features from image data and of using such features for deriving properties of objects or spatio-temporal events in the world. Our work concerns both the development of algorithms and concepts for computer vision and of using computational theories for modelling and explaining properties of biological vision.

A main theme of our work is image representations in terms of receptive fields, which can be modelled and characterized by scale-space theory.

Invariant receptive fields under natural image transformations

Figure 2 from Lindeberg (2013) 'A computational theory of visual receptive fields, Biological Cybernetics, 107(6): 589-635, doi:10.1007/s00422-013-0569-z.

Image based matching and recognition

Figure 2 from Lindeberg (2013) 'Scale selection properties of generalized scale-space interest point detectors', Journal of Mathematical Imaging and Vision, volume 46, number 2, pages 177-210, doi:10.1007/s10851-012-0378-3.

Figure 2 from Lindeberg (2013) 'Image matching using generalized scale-space interest points', Scale-Space and Variational Methods in Computer Vision, Springer Lecture Notes in Computer Science, volume 7893, pages 355-367, 10.1007/978-3-642-38267-3_30.

Scale-space theory for visual operations

Figure 11 from Lindeberg (2013) 'Invariance of visual operations at the level of receptive fields', PLOS ONE 8(7): e66990, pages 1-33, doi:10.1371/journal.pone.0066990.

Normative theory for auditory receptive fields

Figure 15 from Lindeberg and Friberg (2015) 'Idealized computational models of auditory receptive fields, PLOS ONE, 10(3):e0119032, pages 1-58. Figure 20 from Lindeberg and Friberg (2015) 'Idealized computational models of auditory receptive fields, PLOS ONE, 10(3):e0119032, pages 1-58.

Popular article about our research on visual recognition and receptive fields in International Innovation

Published by: Tony Lindeberg <>
Updated 2014-01-09