Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Image segmentation 3 morphology by Rumah Belajar 1831 views
- Image segmentation 2 by Rumah Belajar 3254 views
- Point Processing by shankar4244 3023 views
- 06 object measurement by Rumah Belajar 1181 views
- 05 visual quality assessment by Rumah Belajar 803 views
- 02 2d systems matrix by Rumah Belajar 916 views

927 views

836 views

836 views

Published on

No Downloads

Total views

927

On SlideShare

0

From Embeds

0

Number of Embeds

10

Shares

0

Downloads

0

Comments

0

Likes

1

No embeds

No notes for slide

- 1. Point Processing…with most slides shamelessly stolenfrom Steve Seitz and Gonzalez & Woods
- 2. Download di http://rumah-belajar.org
- 3. TodayImage FormationRange Transformations • Point ProcessingReading for this week: • Gonzalez & Woods, Ch. 3 • Forsyth & Ponce, Ch. 7
- 4. Image Formation f(x,y) = reflectance(x,y) * illumination(x,y) Reflectance in [0,1], illumination in [0,inf]
- 5. Sampling and Quantization
- 6. Sampling and Quantization
- 7. What is an image?We can think of an image as a function, f, from R2 to R: • f( x, y ) gives the intensity at position ( x, y ) • Realistically, we expect the image only to be defined over a rectangle, with a finite range: – f: [a,b]x[c,d] [0,1]A color image is just three functions pasted together. We can write this as a “vector-valued” function: r ( x, y ) f ( x, y ) g ( x, y ) b ( x, y )
- 8. Images as functions
- 9. What is a digital image?We usually operate on digital (discrete) images: • Sample the 2D space on a regular grid • Quantize each sample (round to nearest integer)If our samples are apart, we can write this as: f[i ,j] = Quantize{ f(i , j ) }The image can now be represented as a matrix of integer values
- 10. Image processingAn image processing operation typically defines a new image g in terms of an existing image f.We can transform either the range of f.Or the domain of f:What kinds of operations can each perform?
- 11. Point ProcessingThe simplest kind of range transformations are these independent of position x,y: g = t(f)This is called point processing.What can they do?What’s the form of t?Important: every pixel for himself – spatial information completely lost!
- 12. Basic Point Processing
- 13. Negative
- 14. Log
- 15. Power-law transformations
- 16. Gamma Correction Gamma Measuring Applet: http://www.cs.berkeley.edu/~efros/java/gamma/gamma.html
- 17. Image Enhancement
- 18. Contrast Streching
- 19. Image Histograms
- 20. Cumulative Histograms
- 21. Histogram Equalization
- 22. Histogram Matching
- 23. Match-histogram code
- 24. Neighborhood Processing (filtering)Q: What happens if I reshuffle all pixels within the image?A: It’s histogram won’t change. No point processing will be affected…Need spatial information to capture this.
- 25. Programming Assignment #1Easy stuff to get you started with Matlab • James will hold tutorial this weekDistance Functions • SSD • Normalized CorrelationBells and Whistles • Point Processing (color?) • Neighborhood Processing • Using your data (3 copies!) • Using your data (other images)

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment