From: nethaji anandhavalli on 4 Mar 2010 23:06 "Roger Stafford" <ellieandrogerxyzzy(a)mindspring.com.invalid> wrote in message <gso56i$d88$1(a)fred.mathworks.com>... > "Jade Forest" <sllinios(a)pchome.com.tw> wrote in message <gsmajj$72g$1(a)fred.mathworks.com>... > > I am now study the SIFT(scale invariant feature transformation) and try to implement in Matlab. > > However, I am stuck on the step: "Localization", this include a 3D curve fitting technique to find the local max & min in an image. > > I tried to find books and documents that talk about this subject, but didn't find any. > > The 3D quadratic Taylor Expansion is like below: > > D(x) = D + (∂D[T]/∂X)X + (1/2)X[T](∂2D[T]/∂X2)X > > Except to refer you to websites such as > > http://en.wikipedia.org/wiki/Scale-invariant_feature_transform > > I cannot help you with the main thrust of your question. I am not familiar with the subject. > > For an interpretation of the Taylor expansion you quote if that puzzles you, I refer you to the site: > > http://en.wikipedia.org/wiki/Taylor_expansion > > in the section called "Taylor series in several variables", (in your case three variables.) What you write as (dD[T]/dX)X stands for a row vector multiplied by a column vector: > > [Dx,Dy,Dz]*[x;y;z] > > where Dx, Dy, and Dz are the first partial derivatives of D(x,y,z) with respect to x, y, and z, respectively, evaluated at some "keypoint" and x, y, and z are the three coordinate differences from these keypoint values. The next term > > (1/2)X[T](d2D[T]/dX2)X > > signifies half the product of a row vector, a 3 x 3 matrix, and a column vector: > > 1/2 * [x,y,z] * [Dxx,Dxy,Dxz;Dyx,Dyy,Dyz;Dzx,Dzy,Dzz] * [x;y;z] > > where Dxx, Dxy, Dxz, Dyx, etc. signify the second partial derivatives of D with respect to x and x, with respect to x then y, etc., all evaluated at the keypoint, and again x, y, and z are coordinate differences from those of the keypoint. That is the second order Taylor expansion in three variables about the point called here the keypoint. > > Roger Stafford
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