Description
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Use image called MASK in file chars45.mat to study shape analysis using Hu’s moments for the characters:
A,B,C,D,E,F,G,H,I,K,L,M,N,O,R,S,T,U,V,Y
a,c,d,e,f,g,h,i,k,l,m,n,o,p,r,s,t,u,v,y
0,1,2,3,4,5,6,7,8,9
Use a character from the image as a model, (A1, (A2, (A3, (A4, (A5, (A6), for each charac-ter above. Develop the functions listed below and report performance in terms of overall and per character success.
function Mpq = CS4640_central_moment(pts,p,q)
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CS4640_central_moment – compute a central moment
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Mpq = sum sum (xˆp*yˆq)
% x y
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On input:
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pts (nx2 array): row and cols of points
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p (int): exponent for x
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q (int): exponent for y
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On output:
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Mpq (float): Mpq moment
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Call:
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M00 = CS4640_central_moments([1 1; 2 2; 3 3],0,0);
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Author:
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T. Henderson
1
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UU
function Epq = CS4640_normal_moment(pts,p,q)
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CS4640_normal_moment – compute a central normal moment
% Epq = <pq/M00ˆb where b = 1+(p+q)/2
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On input:
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pts (nx2 array): row and cols of points
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p (int): exponent for x
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q (int): exponent for y
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On output:
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Epq (float): Epq moment
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Call:
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E00 = CS4640_normal_moment([1 1; 2 2; 3 3],0,0);
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Author:
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T. Henderson
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UU
%
function H = CS4640_Hu_moments(pts)
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CS4640_Hu_moments – compute Hu’s 6 moments
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On input:
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pts (nx2 array): row and cols of points
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On output:
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H (6×1 vector): Hu moments
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Call:
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H = CS4640_Hu_moments([1 1; 2 2; 3 3]);
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Author:
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T. Henderson
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UU
%
function H_models = CS4640_Hu_build(templates)
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CS4640_Hu_models – produce Hu models for image templates
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On input:
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templates (n-element vector struct): template images
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(k).im (MxN binary image): image template
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On output:
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H_models (nx7 array): Hu models
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Call:
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Hm = CS4640_Hu_models(templates);
2
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Author:
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T. Henderson
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UU
%
function classes = CS4640_Hu_classify(im,H_models)
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CS4640_Hu_classify – classify characters using Hu models
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On input:
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im (MxN binary image): input image
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H_models (nx7 array): Hu models for n characters
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On output:
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classes (kx2 array): class and distance for each CC
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Call:
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Hm = CS4640_Hu_classify(im,Hm);
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Author:
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T. Henderson
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UU
%
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Implement an eigenchars classification approach similar to the eigenfaces method dis-cussed in the text. Develop a template database of 100 images (2 examples of each charac-ter). Build the models and then report performance on overall and per character success on MASK from chars45.mat. Develop the following functions.
function classes = CS4640_Hu_classify(im,H_models)
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CS4640_Hu_classify – classify characters using Hu models
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On input:
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im (MxN binary image): input image
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H_models (nx7 array): Hu models for n characters
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On output:
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classes (kx2 array): class and distance for each CC
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Call:
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Hm = CS4640_Hu_classify(im,Hm);
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Author:
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T. Henderson
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UU
%
3
function [V,MM,PCA_models] = CS4640_PCA_model(templates)
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CS4640_PCA_model – build PCA model from templates
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On input:
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templates (vector struct): n template images
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(k).im (MxN binary array): template image for character k
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On output:
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V (M*nxM*n array): eigenvectors
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MM (M*nx1 vector): mean vector
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PCA_models (nxk array): weight values for first k eigenvectors
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Call:
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[V,MM,PCA_models] = CS4640_PCA_model(templates);
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Author:
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T. Henderson
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UU
%
function c = CS4640_PCA_classify(im,V,MM,PCA_models)
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CS4640_PCA_classify – classify image using PCA models
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On input:
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im (MxN binary array): input image
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V (M*NxM*N array): eigenvectors
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MM (M*Nx1 vector): mean vector
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PCA_models (nxk array): weight values for first k eigenvectors
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On output:
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c (int): class
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Call:
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[V,MM,PCAm] = CS4640_PCA_model(templates);
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Author:
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T. Henderson
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UU
%
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