Q:

How to do perspective correction in Matlab from known Intrinsic and Extrinsic parameters?

I'm using Matlab for camera calibration using Jean-
Yves Bouget's Camera Calibration Toolbox. I have all the camera
parameters from the calibration procedure. When I use a new image not
in the calibration set, I can get its transformation equation e.g.
Xc=R*X+T, where X is the 3D point of the calibration rig (planar) in
the world frame, and Xc its coordinates in the camera frame. In other
words, I have everything (both extrinsic and intrinsic parameters).
What I want to do is to perform perspective correction on this image
i.e. I want it to remove any perspective and see the calibration rig
undistorted (its a checkerboard). 
Matlab's new Computer Vision toolbox has an object that performs a perspective transformation on an
image, given a 3X3 matrix H. The problem is, I can't compute this
matrix from the known intrinsic and extrinsic parameters! 

A:

To all who are still interested in this after so many months, i've managed to get the correct homography matrix using Kovesi's code (http://www.csse.uwa.edu.au/~pk/research/matlabfns), and especially the homography2d.m function. You will need however the pixel values of the four corners of the rig. If the camera is steady fixed, then you will need to do this once. See example code below:
%get corner pixel coords from base image
p1=[33;150;1];
p2=[316;136;1];
p3=[274;22;1];
p4=[63;34;1];
por=[p1 p2 p3 p4];
por=[0 1 0;1 0 0;0 0 1]*por;    %swap x-y <--------------------

%calculate target image coordinates in world frame
% rig is 9x7 (X,Y) with 27.5mm box edges
XXw=[[0;0;0] [0;27.5*9;0] [27.5*7;27.5*9;0] [27.5*7;0;0]];
Rtarget=[0 1 0;1 0 0;0 0 -1]; %Rotation matrix of target camera (vertical pose)
XXc=Rtarget*XXw+Tc_ext*ones(1,4); %go from world frame to camera frame
xn=XXc./[XXc(3,:);XXc(3,:);XXc(3,:)]; %calculate normalized coords
xpp=KK*xn;  %calculate target pixel coords

% get homography matrix from original to target image
HH=homography2d(por,xpp);
%do perspective transformation to validate homography
pnew=HH*por./[HH(3,:)*por;HH(3,:)*por;HH(3,:)*por]; 

That should do the trick. Note that Matlab defines the x axis in an image ans the rows index and y as the columns. Thus one must swap x-y in the equations (as you'll probably see in the code above). Furthermore, i had managed to compute the homography matrix from the parameters solely, but the result was slightly off (maybe roundoff errors in the calibration toolbox). The best way to do this is the above.
If you want to use just the camera parameters (that is, don't use Kovesi's code), then the Homography matrix is H=KK*Rmat*inv_KK. In this case the code is,
% corner coords in pixels
p1=[33;150;1];
p2=[316;136;1];
p3=[274;22;1];
p4=[63;34;1];
pmat=[p1 p2 p3 p4];
pmat=[0 1 0;1 0 0;0 0 1]*pmat; %swap x-y

R=[0 1 0;1 0 0;0 0 1];  %rotation matrix of final camera pose
Rmat=Rc_ext'*R;  %rotation from original pose to final pose
H=KK*Rmat*inv_KK; %homography matrix
pnew=H*pmat./[H(3,:)*pmat;H(3,:)*pmat;H(3,:)*pmat]; %do perspective transformation

H2=[0 1 0;-1 0 0;0 0 1]*H;  %swap x-y in the homography matrix to apply in image

