Problem 1 images:
b=imread('5_67-gray.jpg');
stats(b) % gather statistics
c=extract435(b,193,182,300,250);
stats(c) % gather statistics
figure(1),imshow(c)
% Now find the mode...
[counts,x]=imhist(c);
% determine histogram info
[m,index]=max(counts); % index = the index of the max value of the histogram
% the grayscale value that appears most would then be index-1 (since MATLAB
% indices start at 1, but grayscale values start at 0
d=uint8(zeros(size(c))); % create an image of zeros the same size as c
d(c>200)=1;,d(c<75)=1; % set some of the values in D to 1
figure(2),imshow(d,[])
sum(d(:)) % this sum will be the total number of 1s in the image
Problem 2: Given Decision Boundary
Problem 2: My improved decision boundary:
load USNK_hand.dat
s=USNK_hand;
USf1=s(1:500,3);,USf2=s(1:500,4);
NKf1=s(501:end,3);,NKf2=s(501:end,4);
figure(3),plot(USf1,USf2,'ro',NKf1,NKf2,'b+')
xlabel('Feature 1: largest extent of hand (cm)')
ylabel('Feature 2: area of palm (cm^2)')
grid on, legend('US','NK'),title('Scatter Plot: Area of Palm vs. Largest Extent
of Hand'),hold on
% now add the given decision boundary
f1=6.5:0.1:9;
b= -15*f1 + 152.5;
plot(f1,b,'k','linewidth',2)
hold off
axis([6.5 10 25 45])
% Test of the given decision boundary
for k=1:775
testval = s(k,4) + 15* s(k,3) -152.5;
if testval >= 0 % indicates US
s(k,2)=1;
else
s(k,2)=2; % indicates NK
end
end
errors = length(find(s(:,1) ~= s(:,2)))
accuracy=(775-errors)/775 * 100
% now test my improved decision boundary
s=USNK_hand;
for k=1:775
testval = s(k,4)-33;
if testval >= 0
s(k,2)=1;
else
s(k,2)=2;
end
end
errors = length(find(s(:,1) ~= s(:,2)))
accuracy=(775-errors)/775 * 100
figure(4),plot(USf1,USf2,'ro',NKf1,NKf2,'b+')
xlabel('Feature 1: largest extent of hand (cm)')
ylabel('Feature 2: area of palm (cm^2)')
grid on, legend('US','NK'),title('Scatter Plot: Area of Palm vs. Largest Extent
of Hand'),hold on
% put my new decision boundary on the scatter plot
f1=6.5:0.1:10;
b= 33*ones(size(f1));
plot(f1,b,'k','linewidth',2)
hold off