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Advanced Computer Vision - PowerPoint PPT Presentation

Lecture 08 Roger S. Gaborski. Advanced Computer Vision. Histograms. H istogram is nothing more than mapping the pixels in a 2 dimensional matrix into a vector

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Lecture 08

Roger S. Gaborski

Roger S. Gaborski

Histograms
• Histogram is nothing more than mapping the pixels in a 2 dimensional matrix into a vector
• Each component in the vector is a bin (range of gray level values) and the corresponding value is the number of pixels with that gray level value
Similarity between Histograms
• Similarity between histogram bins:
• Assuming both histograms have ∑nj j=1…B pixels

M. Swain and D. Ballard. “Color indexing,”International Journal of Computer Vision, 7(1):11–32, 1991.

Histogram Intersection
• A simple example:
• g = [ 17, 23, 45, 61, 15]; (histogram bins)
• h = [ 15, 21, 42, 51, 17];
• in=sum(min(h,g))/min( sum(h),sum(g))
• in =

0.9863

>> g = [17,23,45,61,15];

>> h = [15,21,42,51,17];

>> min(g,h)

ans=

15 21 42 51 15

>> N = sum(min(g,h))

N =

144

>> D=min(sum(h),sum(g))

D =

146

>> intersection = N/D

intersection =

0.9863

Roger S. Gaborski

If Histograms Identical
• g = 15 21 42 51 17
• h = 15 21 42 51 17
• >> in=sum(min(h,g))/min( sum(h),sum(g))
• in =

1

Different Histograms
• h = 15 21 42 51 17
• g = 57 83 15 11 1
• >> in=sum(min(h,g))/min( sum(h),sum(g))
• in =

0.4315

Region and Histogram

Similarity with itself:

>>h = hist(q(:),256);

>> g=h;

>> in=sum(min(h,g))/min( sum(h),sum(g))

in = 1

>> r=236;c=236;

>> g=im(1:r,1:c);

>> g= hist(g(:),256);

>> in=sum(min(h,g))/min( sum(h),sum(g))

in =

0.5474

Partial Matches

>> g= hist(g(:),256);

>> in=sum(min(h,g))/min( sum(h),sum(g))

in =

0.8014

in=sum(min(h,g))/min( sum(h),sum(g))

in =

0.8566

Lack of Spatial Information
• Different patches may have similar histograms

For

Howard

Ross

Roger S. Gaborski