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| 2. |
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| 3. |
- Andersson, Thord, et al.
(författare)
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WITAS Project at Computer Vision Laboratory; A status report (Jan 1998)
- 1998
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Ingår i: Proceedings of the SSAB symposium on image analysis. ; , s. 113-116
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Konferensbidrag (refereegranskat)abstract
- WITAS will be engaged in goal-directed basic research in the area of intelligent autonomous vehicles and other autonomous systems. In this paper an overview of the project is given together with a presentation of our research interests in the project. The current status of our part in the project is also given.
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| 4. |
- Bigun, Josef, et al.
(författare)
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Central Symmetry Modelling
- 1986
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Ingår i: Proceedings of EUSIPCO-86, Third European Signal Processing Conference. - Linköping : Linköping University Electronic Press. ; , s. 883-886
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Konferensbidrag (refereegranskat)abstract
- A definition of central symmetry for local neighborhoods of 2-D images is given. A complete ON-set of centrally symmetric basis functions is proposed. The local neighborhoods are expanded in this basis. The behavior of coefficient spectrum obtained by this expansion is proposed to be the foundation of central symmetry parameters of the neighbqrhoods. Specifically examination of two such behaviors are proposed: Point concentration and line concentration of the energy spectrum. Moreover, the study of these types of behaviors of the spectrum are shown to be possible to do in the spatial domain.
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| 5. |
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| 6. |
- Bigun, Josef, et al.
(författare)
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Multidimensional orientation estimation with applications to texture analysis and optical flow
- 1991
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539. ; 13:8, s. 775-790
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Tidskriftsartikel (refereegranskat)abstract
- The problem of detection of orientation in finite dimensional Euclidean spaces is solved in the least squares sense. In particular, the theory is developed for the case when such orientation computations are necessary at all local neighborhoods of the n-dimensional Euclidean space. Detection of orientation is shown to correspond to fitting an axis or a plane to the Fourier transform of an n-dimensional structure. The solution of this problem is related to the solution of a well-known matrix eigenvalue problem. Moreover, it is shown that the necessary computations can be performed in the spatial domain without actually doing a Fourier transformation. Along with the orientation estimate, a certainty measure, based on the error of the fit, is proposed. Two applications in image analysis are considered: texture segmentation and optical flow. An implementation for 2-D (texture features) as well as 3-D (optical flow) is presented. In the case of 2-D, the method exploits the properties of the complex number field to by-pass the eigenvalue analysis, improving the speed and the numerical stability of the method. The theory is verified by experiments which confirm accurate orientation estimates and reliable certainty measures in the presence of noise. The comparative results indicate that the proposed theory produces algorithms computing robust texture features as well as optical flow. The computations are highly parallelizable and can be used in realtime image analysis since they utilize only elementary functions in a closed form (up to dimension 4) and Cartesian separable convolutions.
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| 8. |
- Bigun, Josef, et al.
(författare)
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Optimal Orientation Detection of Linear Symmetry
- 1987
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Ingår i: Proceedings of the IEEE First International Conference on Computer Vision. - Linköping : Linköping University Electronic Press. ; , s. 433-438
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Konferensbidrag (refereegranskat)abstract
- The problem of optimal detection of orientation in arbitrary neighborhoods is solved in the least squares sense. It is shown that this corresponds to fitting an axis in the Fourier domain of the n-dimensional neighborhood, the solution of which is a well known solution of a matrix eigenvalue problem. The eigenvalues are the variance or inertia with respect to the axes given by their respective eigen vectors. The orientation is taken as the axis given by the least eigenvalue. Moreover it is shown that the necessary computations can be pursued in the spatial domain without doing a Fourier transformation. An implementation for 2-D is presented. Two certainty measures are given corresponding to the orientation estimate. These are the relative or the absolute distances between the two eigenvalues, revealing whether the fitted axis is much better than an axis orthogonal to it. The result of the implementation is verified by experiments which confirm an accurate orientation estimation and reliable certainty measure in the presence of additive noise at high level as well as low levels.
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