Mathematics in Everyday Life Gilad Lerman Department of

  • Slides: 24
Download presentation
Mathematics in Everyday Life Gilad Lerman Department of Mathematics University of Minnesota Highland park

Mathematics in Everyday Life Gilad Lerman Department of Mathematics University of Minnesota Highland park elementary (6 th graders)

What homework do I give What do mathematicians do? my students? • Example of

What homework do I give What do mathematicians do? my students? • Example of a recent homework: Denoising

What projects do I assign What do mathematicians do? my students? • Example of

What projects do I assign What do mathematicians do? my students? • Example of a recent project: Recognizing Panoramas • Panorama: wide view of a physical space • How to obtain a panorama?

How to obtain a panorama 1. By “rotating line camera” 2. Stitching together multiple

How to obtain a panorama 1. By “rotating line camera” 2. Stitching together multiple images Your camera can do it this way… E. g. Photo. Stitch (Canon Power. Shot SD 600)

Experiment with Photo. Stitch Input: 10 images along a bridge Experiment done by Rebecca

Experiment with Photo. Stitch Input: 10 images along a bridge Experiment done by Rebecca Szarkowski

Experiment continued… Output: Panorama (Photo. Stitch) Output: Panorama (by a more careful mathematical algorithm)

Experiment continued… Output: Panorama (Photo. Stitch) Output: Panorama (by a more careful mathematical algorithm) Experiment done by Rebecca Szarkowski

New Topic: Relation of Imaging What’s math got to do with it? and Mathematics

New Topic: Relation of Imaging What’s math got to do with it? and Mathematics From visual images to numbers (or digital images)

Digital Image Acquisition

Digital Image Acquisition

From Numbers to Images Let us type the following numbers 1 2 3 4

From Numbers to Images Let us type the following numbers 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 We then color them so 1=black, 8=white rest of colors are in between

One more time… Now we’ll try the following numbers 1 1 1 1 2

One more time… Now we’ll try the following numbers 1 1 1 1 2 2 2 2 4 4 4 4 8 8 8 8 16 16 32 32 64 64 128 128 We then color them so 1=black, 128=white rest of colors are in between

Let’s compare 1 2 3 4 5 6 7 8 1 2 3 4

Let’s compare 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 1 1 1 2 2 2 2 4 4 4 4 8 8 8 8 16 16 32 32 64 64 128 128

From an Image to Its Numbers We start with clown image It has 200*320

From an Image to Its Numbers We start with clown image It has 200*320 numbers I can’t show you all… Let’s zoom on eye (~40*50)

Image to Numbers (Continued) We’ll zoom on middle of eye image (10*10)

Image to Numbers (Continued) We’ll zoom on middle of eye image (10*10)

The Numbers (Continued) The middle of eye image (10*10) 80 81 80 80 77

The Numbers (Continued) The middle of eye image (10*10) 80 81 80 80 77 77 81 80 80 80 73 77 70 80 81 80 80 80 79 77 70 54 70 80 80 80 77 70 22 37 22 80 80 77 66 22 2 1 2 80 80 80 77 77 54 57 2 6 2 77 77 37 66 77 66 51 22 2 6 77 37 11 66 80 77 70 37 8 8 37 11 9 6 2 11 66 54 77 80 66 54 51 70 37 22 2 6 8 6 Note the rule: Bright colors – high numbers Dark colors - low numbers

More Relation of Imaging and Math Averaging numbers smoothing images Idea of averaging: take

More Relation of Imaging and Math Averaging numbers smoothing images Idea of averaging: take an image Replace each point by average with its neighbors 80 81 80 80 77 77 81 80 80 80 73 77 70 80 81 80 80 80 79 77 70 54 70 For example, 2 has the neighborhood So replace 2 by 80 80 80 77 70 22 37 22 80 80 77 66 22 2 1 2 80 80 80 77 77 54 57 22 6 2 77 77 37 66 77 66 51 22 22 2 6 70 22 37 22 2 1 57 2 6 77 37 11 66 80 77 70 37 37 8 8 37 9 2 66 77 66 51 37 2 8 11 6 11 54 80 54 70 22 6 6

Example: Smoothing by averaging Original image on top left It is then averaged with

Example: Smoothing by averaging Original image on top left It is then averaged with neighbors of distances 3, 5, 19, 15, 35, 45

Example: Smoothing by averaging And removing wrinkles by both….

Example: Smoothing by averaging And removing wrinkles by both….

More Relation of Imaging and Math Differences of numbers sharpening images On left image

More Relation of Imaging and Math Differences of numbers sharpening images On left image of moon On right its edges (obtained by differences) We can add the two to get a sharpened version of the first

Moon sharpening (continued)

Moon sharpening (continued)

Real Life Applications • Many… • From a Minnesota based company… • Their main

Real Life Applications • Many… • From a Minnesota based company… • Their main job: maintaining railroads • Main concern: Identify cracks in railroads, before too late…

How to detect damaged rails? • Traditionally… drive along the rail (very long) and

How to detect damaged rails? • Traditionally… drive along the rail (very long) and inspect • Very easy to miss defects (falling asleep…) • New technology: getting pictures of rails

Millions of images then collected

Millions of images then collected

How to detect Cracks? • Human observation… • Train a computer… • Recall that

How to detect Cracks? • Human observation… • Train a computer… • Recall that differences detect edges… Work done by Kyle Heuton (high school student at Saint Paul)

Summary • Math is useful (beyond the grocery store) • Images are composed of

Summary • Math is useful (beyond the grocery store) • Images are composed of numbers • Good math ideas good image processing