1. A method of keypoint scale-space extraction and description in an image, the method comprising the steps of: a) filtering the image with triangle kernel filters at different scales; b) computing an approximation of a determinant of Hessian at each scale, the approximation at each scale k being calculated as |∂ k xx (i, j)·∂ k yy (i, j)−∂ k xy (i, j) 2 | where ∂ xx is a second horizontal derivative of Gaussian over a filtered image response L(k, i, i) obtained in step a) at scale k at point (i, j), ∂ yy is a second vertical derivative of Gaussian over the filtered image response L(k, i, j), and ∂ xy is the cross derivative of Gaussian over the filtered image response L(k, i, j), using a first design parameter d 1 for computing a second horizontal and vertical derivatives of Gaussian, ∂ xx and ∂ yy , and a second design parameter d 2 for computing a cross derivative of Gaussian ∂ xy , being both the first design parameter d 1 and the second design parameter d 2 proportional to a deviation σ of a second derivative of Gaussian kernel; c) searching for extremum values both within a single scale and along the scale space of the approximation of the determinant of Hessian obtained in step b) and calculating the keypoints from these extrema values; d) for each keypoint, localized at an extremum value, detecting the dominant orientations from gradient information calculated using the filtered image response obtained in step a); and e) calculating for each dominant orientation a keypoint descriptor.