CSE185
Fall 2008
Lecture 16
Image Transformations and Spatial Filtering
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Image Transformations and Spatial Filtering in Digital Image Processing - Prof. Shawn News, Study notes of Computer Science
A lecture note from cse185, fall 2008, covering the topics of image transformations and spatial filtering in digital image processing. Information on image transformations such as histogram equalization, local histogram processing, and spatial filtering. It also provides details on the mechanics of spatial filtering and linear spatial filtering using a 3x3 neighborhood.
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CSE185Fall 2008Lecture 16
Image Transformations and Spatial Filtering
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Gonzalez & Woods
Today
•^
Image transformations and spatial filtering (Chap3)
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Gonzalez & Woods
Midterm
-^
Midterm on Wed. 11/
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You will have the full 75 minutes
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Open notes/book
-^
Bring scratch paper
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No laptops/PDAs (calculators OK)
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It will cover material through histogram equalization
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Lectures 1-
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Chapters 1, 2 (except 2.6.7) and sections 3.1-3.
-^
Types of questions:–
Multiple choice– Short answer– Computation (similar to HWs)
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Office hour schedule next week
• No office hours on:
- Wed. Nov. 5 (before the midterm)– Thurs. Nov. 6
• I’ll be out of town:
- Nov. 4-
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
•^
Midterm Review: Chapter 2– An Introduction to the Mathematical Tools Used in DIP
- Linear vs. nonlinear operations• Spatial operations• Geometric spatial transformations and image registration• Affine transformations• Probabilistic methods
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
•^
Midterm Review: Chapter 3– Intensity transformations– Image negatives– Log transformations– Piecewise-linear transformation functions– Histogram processing– Histogram equalization
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Chap 3: Image transformations and spatial filtering
•^
Example 3.
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Chap 3: Image transformations and spatial filtering
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Chap 3: Image transformations and spatial filtering
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Chap 3: Image transformations and spatial filtering
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Chap 3: Image transformations and spatial filtering
•^
Histogram equalization can be generalized tohistogram matching (specification)
-^
Produces image with a histogram that matches aspecified PDF – not just a uniform PDF
-^
Section 3.2.
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Chap 3: Image transformations and spatial filtering
•^
Local histogram processing
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So far, only considered global histogram processingin which transform is based on all pixels in image
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Sometimes local statistics of image will havenegligible influence on global transform
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So, instead, compute transformation based onhistogram in a subregion centered on the pixel to betransformed
-^
More expensive computationally but can avoidhaving to re-compute histogram at every location -example
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Chap 3: Image transformations and spatial filtering
•^
Fundamentals of spatial filtering
-^
Used for a broad spectrum of applications– Enhancement (Chap 3)– Edge detection (Chap 10)– Object detection
-^
The name “filter” is borrowed from frequency domainprocessing
Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
www.ImageProcessingPlace.com
Chap 3: Image transformations and spatial filtering
•^
Mechanics of spatial filtering
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Spatial filter consists of1.^
A neighborhood (typically a small rectangle)
A predefined operation perform on the pixels in theneighborhood
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Filtering creates a new pixel (in the outputimage) with coordinates equal to the coordinatesof the center of the neighborhood, and whosevalue is the result of the filtering operation