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- Knutsson, Hans, 1950-, et al.
(författare)
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Automated generation of representations in vision
- 2000
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Ingår i: International Conference on Pattern Recognition ICPR,2000. - Barcelona, Spain : IEEE. - 0769507506 ; , s. 59-66 vol.3
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Konferensbidrag (refereegranskat)abstract
- This paper presents a general strategy for automated generation of efficient representations in vision. The approach is highly task oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of situations that are dependent through the chosen feature but are otherwise independent. Particularly important concepts in the work are mutual information and canonical correlation. How visual operators and representations can be generated from examples are presented for a number of features, e.g. local orientation, disparity and motion. Interesting similarities to biological vision functions are observed. The results clearly demonstrates the potential of combining advanced filtering techniques and learning strategies based on canonical correlation analysis (CCA).
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- Knutsson, Hans, 1950-, et al.
(författare)
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Motion artifact reduction in MRI through generalized DFT
- 2004
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Ingår i: Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on. - : IEEE. - 0780383885 ; , s. 896-899 vol.1
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method that dramatically reduces artifacts caused by respiratory (and similar types of) patient motion in magnetic resonance imaging (MRI). The basis for the method is the observation that affine deformations of an object will correspond to a different but unique affine coordinate transform (plus phase shift) of the Fourier representation of the object. The resulting sample points will be irregularly distributed prohibiting the use of standard IFFT to reconstruct the object. The object can however be reconstructed through the use of a weighted regularized pseudo inverse. A standard pseudo inverse is, however, not possible due to excessive computational demands. For this reason a novel fast sequential pseudo inverse algorithm is also presented. Significantly improved results are obtained on both synthetic and clinical data.
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