％BIQPBOX Bisection reflective line search for sqpbox
％ [nx,nsig,alpha] = BIQPBOX(s,c,strg,x,y,sigma,l,u,...
％ oval,po,normg,DS,mtxmpy,data,H)
％ returns the new feasible point nx, the corresponding sign vector nsig,
％ and the step size of the unreflected step, alpha.
％ Copyright (c) 1990-98 by The MathWorks, Inc.
％ $Revision: 1.2 $ $Date:
The CUBA library provides new implementation of four general-purpose multidimensional integration algorithms: Vegas, Suave, Divonne, and
Cuhre. Suave is a new algorithm, Divonne is a known algorithm to which important details have been added, and Vegas and Cuhre are new
implementations of existing algorithms with only few improvements over the original versions. All four algorithms can integrate vector integrands
and have very similar Fortran, C/C++, and Mathematica interfaces.
So you wanted to add a forms editor to your application? A dialog editor? Something that allows drawing of HTML <div>s? Here is a feature rich skeleton (!) to get you started. CDiagramEditor is a package that gives you a basic visual editor intended for vector objects. Although perhaps not sufficient to create a CAD-application, you ll indeed be able to create a dialog editor. The editor itself is derived
So you wanted to add a forms editor to your application? A dialog editor? Something that allows drawing of HTML <div>s? Here is a feature rich skeleton (!) to get you started. CDiagramEditor is a package that gives you a basic visual editor intended for vector objects. Although perhaps not sufficient to create a CAD-application, you ll indeed be able to create a dialog editor. The editor itself is derived
圖像處理的關(guān)于Snakes : Active Contour Models算法和水平集以及GVF的幾篇文章，文章列表為：
[1]Snakes Active Contour Models.pdf
[2]Multiscale Active Contours.pdf
[3]Snakes, shapes, and gradient vector flow.pdf
[4]Motion of level sets by mean curvature I.pdf
[5]Spectral Stability of Local Deformations Spectral Stability of Local Deformations.pdf
[6]An active contour model for object tracking using the previous contour.pdf
[7]Volumetric Segmentation of Brain Images Using Parallel Genetic AlgorithmsI.pdf
[8]Segmentation in echocardiographic sequences using shape-based snake model.pdf
[9]Active Contours Without Edges.pdf
學(xué)習(xí)圖像處理的人必看的幾篇文章
ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points.
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model - matrix with model points,
data - matrix with data points,
OUTPUT:
R - rotation matrix and
T - translation vector accordingly
so
newdata = R*data + T .
newdata are transformed data points to fit model
see help icp for more information
％ because we do not truncate and shift the convolved input
％ sequence, the delay of the desired output sequence wrt
％ the convolved input sequence need only be the delay
％ introduced by the ideal weight vector centred at n=5
