??? Eyes Brain (Inside) Conclusion: Ideally Suited for Image Processing.

???

Eyes

Brain(Inside)

Conclusion: Ideally Suited for Image Processing

May Look Ideally Suited for Image Processing……But They’re Not

Filtering Images

• Creates new image• Each pixel is based on the corresponding pixel

and its neighbors in the old image• Filters can be used to clean images

“Noisy” Picture

Average Filter:Each pixel in new image will be the

average (mean) of a region of pixels in the

old image. Cleaned Picture

Median Filter:Each pixel in new image will be the

median of a region of pixels in the old

image.

Feature Detection

• What are features?• A feature is something that catches our eye in

an image

Laplacian Filter

• Laplacian filter is a filter looking like this:• The Laplacian filter detects points (or

areas) that are different from their surrounding.

• Us humans see the world• Through Laplacian filter

Feature detection in action

narrow filtersmall features

wide filterlarge features

The Problem of Scale

• The computer can easily fill in small gaps in the image to clean up noise.

• There are problems with larger gaps.

• Solution: Work on different scales.

Filter

Picture With Larger Bad Piece Just Filtering is Not Effective!

Gaussian Pyramids

• G0 = Original Image

• GN, N > 0 = Reduced Image

G0 G1 G2 …

Expand

Expand

Low DetailMuch Higher Detail

Much Higher Detail

Using Filters As Pyramids

• Filters can accomplish the same blurring as Gaussian pyramids.

• Gaussian filters create this blurring effect by emphasizing the corresponding pixel’s neighbors more than the corresponding pixel

Apply Large, Strong Gaussian Filter

Lower Detail

Apply Small, Weak Gaussian Filter

Approximations

• G0s of similar images = quite different

• GNs of similar images are closer than G0s

Find GNs with Large N

Very Slightly Similar Slightly More Similar

Image Completion

• Method for Image Completion– Repeat with N from a large number to 0

• Obtain a filtered version of GN, enlarged to the original size (Using filters or a Gaussian pyramid)

• Reintroduce the good pixels from the incomplete image

Incomplete Image Mask (Marks Valid Pixels) Complete Image

Another Example

• Can you see the Einstein in 100 random lines?

Incomplete Image Mask (Marks Valid Pixels) Complete Image

Limitations

• This method does not work as well on drawings because drawings can have more unpredictable changes in color.

Incomplete Image Mask (Marks Valid Pixels) Complete Image

Resizing Images

• Our task was making images smaller.• Why?• One reason is to transmit the image over the

internet faster.

But how do you resize an image?

• There a few methods to resize images and to reduce their number of pixels:

• The simplest reduce method is to use the ‘uniform grid’

Adaptive Sub-sampling

• To keep more pixels where details are finer

• Using Feature Detection to sample (take) more pixels near features

• Non-uniform grid

No questions please