| 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.
|
