Simple Processing

8/14/2019 Simple Processing

1/19

1 Ellen L. Walker

ImageJ

s Java image processing tool from NIH

q http://rsbweb.nih.gov/ij/

s Reads / writes a large variety of images

s Many image processing operations are implementedq Good rapid prototyping / testing tool

s Includes the ability to write your own plugins

q Convenient way to implement your own algorithms, usingtheir image class and i/o methods
http://rsbweb.nih.gov/ij/http://rsbweb.nih.gov/ij/


2/19

2 Ellen L. Walker

Point Processes

s Pixel by pixel transformation

s Output pixel depends only on corresponding input pixel

s Examples:

q Out(r,c) = In(r,c) * 1.25

q Out(r,c) = In(r,c) + 25

q Out(r,c) = (In(r,c))2


3/19

3 Ellen L. Walker

Linear Transformations

s Out(r,c) = In(r,c) * gain+ bias

q Gaincontrols contrast

q Biascontrols brightness

s Location dependent:

q Out(r,c) = In(r,c) * gain(r,c) + bias(r,c)

q E.g. sky darkening filter

s Linear Blend

q Out(r,c) = (lambda) * In1(r,c) + (1-lambda) * In2(r,c)

q If In1 and In2 are images, a sequence of these from lambda = 0 tolambda=1 is an image dissolve


4/19

4 Ellen L. Walker

Histogram

s An image histogram counts the number of pixels at each brightness.

s Color images have 3 histograms (red, green, blue)

0

1

2

3

4

5

6

7

0 1 2 3

Series2


5/19

5 Ellen L. Walker

Information in Histogram

s Contrast (variation in brightness)

q Are bars spread over the whole range?

s Foreground vs. background color

q Are there two separate peaks with a valley between?


6/19

6 Ellen L. Walker

Applications of Histogram

s Thresholding

q Find a value that separates foreground / backgroundvalues

q Look for a valley between two peaks (may or may not bewhat you need)

s Contrast enhancement

q Histogram equalization spread the data as evenlythrough the histogram as possible

q Goal: wide, flat histogram


7/197 Ellen L. Walker

Example: Histogram Equalization

s Original

s Modified



Algorithm: Histogram Equalization

s Find cumulative distribution

q For each intensity I, c(I) = # pixels



Algorithm: Histogram Equalization

s Use C(I) as a lookup table to determine the final value of

each pixel.

s Since C(I) ranges from 0 to 1 (why?), multiply C(I) by themax pixel value to get the output value

s Code:

For(r=0;r



Locally Adaptive Histogram Equalization

s Instead of doing histogram equalization using the whole

image, compute the distribution for a moving windowaround the given pixel.

s Avoids effect of bright light at one corner washing outeverything in the image.



Image Neighborhoods

s Neighborhoods can be defined for each pixel

s The two most common neighborhoods

q 4-neighborhood

q 8-neighborhood

NW E

S



Applying a Mask

s Mask is a set of relative pixel positions. One is

designated the origin (0,0) - usually at center

s Each mask element is weighted

s To apply the mask, put the origin pixel over the imagepixel and multiply weights by the pixels under them, thenadd up all the values.

s Usually this is repeated for every pixel in the image .

Assumptions must be made for pixels near the edge ofthe image.



Mask application example

s Mask = 1 1 1

s Apply to every pixelin image:

0 0 0 0 1

0 0 0 1 1

0 0 1 1 1

0 1 1 1 1

1 1 1 1 1

s Result is

0 0 0 1 1

0 0 1 2 2

0 1 2 3 2

1 2 3 3 2

2 3 3 3 2

Boundary pixels are gray



Mathematical Representation of Mask Operations

s Equation from Chapter 3

q g is the output image

q f is the input image

q h is the mask (also called kernel)

s Short form (convolution operator)

g(i, j) = f(i + k, j + l)h(k, l)k,l

g = f h



Masks that "blur"

s "Box mask" - every pixel gets the average of its

neighborhood1 1 1 After computing, divide by 9 (mask

sum)

1 1 1 to keep image from getting too bright

1 1 1

s "Weighted average" - divide by 16 after application

1 2 1

2 4 2

1 2 1



Why blur?

s Avoid effects of small random noise (salt and pepper)

s Remove small features to emphasize larger ones

q Bigger masks blur more / remove larger features

q Sequence of masks generates sequence of increasingly

blurred images (useful for some matching algorithms)

s First step in sharpening the image (!)

Sharp(x,y) =

orig(x,y) + gamma (orig(x,y) (blur * orig(x,y)))



Boundary Effects (padding)

Figure 3.12



Common Masks

Figure 3.13


19/19

Median Filtering

s Example of a non-linear filter

s Replace the central pixel of a window with the medianpixel of the window

s Compare to box filter, which replaces with averagevaluein the window

s Tends to preserve edges (why?)

Simple Processing

Documents

Transcript of Simple Processing