1. Where \ mu is the second-moment matrix as defined above. 2. Assume M ( \ vec X ) is a fully swept moment matrix , 3. Then, the multivariate normal distribution can be equivalently represented as a moment matrix : 4. Suppose X and Y are two vectors of normal variables with the joint moment matrix : 5. The second-moment matrix \ mathbf { \ mu } is defined more generally for anisotropic regions: 6. The difference is that the measure for thresholding is computed from the Hessian matrix instead of a second-moment matrix . 7. The adaptation of the moment matrix also differs very much as compared to " the evolution in the brain " above. 8. The Harris affine detector relies on interest points detected at multiple scales using the Harris corner measure on the second-moment matrix . 9. By using the fully swept moment matrix , we represent the vacuous linear belief functions as a zero matrix in the swept form follows: 10. Although this may look significantly different from the second-moment matrix in the Harris-Laplace detector; it is in fact, identical.
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