## Current Status; Representing haar features in the integral image

About NBS, Dr. Jeff explained to me a basic point I have missed that is while representing the eigen vector as the linear combination of the haar features, we have to convert the haar-feature-basis vectors also to the integral domain.

For example let b1 be representing the haar feature having ones from (1,1) to (10,10). Then its integral representation would be a having

- 1 at (1,1) & (10,10) and
- -1 at (1,10) & (10, 1)

In this representation there would be zero every where except on these 4 locations.

Thus in this way the phi (NBS representation of eigen vector) will be summation of integral-represented basis vectors multiplied by the ci’s. Thus phi will also have zeros on many locations making is sparse. Thus when the image is projected over this phi the coefficient will be calculated by small number of additions.