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Monday, Chapter 4.1.2.3 test.

Introduct students to Chapter 4, Chapter 4 Slides 164-165
Discuss Engineering Design Problem on 185-186
Present Chapter 4 Slides 166-168
Introduct students to Chapter 4.1, Chapter 4 Slide 169
Present Chapter 4 Slides 170-176

Define these terms: Random Noise, Impulsive Noise, Unsharp Masking, Edge
Detection, Change Detection, Image Segmentation, Computer Graphics, Morphing

Discussion questions:
Exercises 4.1 Problems 1-6, p. 195

Homework:
Textbook pp. 196-197

Introduce students to Chapter 4.2, Slide 177
Chapter 4 Slides 178

Define these terms: Matrix, Chromakey, Elements, Scalar
Discuss Key Concept:
o All elements of an image matrix are either positive or zero

Work through example:
4.1 Calculating Pixels and Matrix Elements, p. 197

For additional discussion:
o FAQ # 1 How does a matrix of numbers represent a digital image?
If white and all light colors contain some amount of blue, how can the bluescreen effect (chromakey) work?
o FAQ #2 What happens when we apply a squaring operation to the image, that is, what are the new pixel values when we square the original values?
o FAQ #3 If white and all light colors contain some amount of blue, how can the blue-screen effect (chromakey) work?

Homework:
Textbook pp. 197-205

 

Present Chapter 4 Slides 179-186
Define these terms: Neighborhood Operation, Quantization, Clipping,

Homework:
Complete Exercises 4.2 Problems 5-15 Odd, p. 212-213

 

Present Chapter 4 Slides 192-196

Assign and ask students to complete Lab 4.1 Worksheets:
o L4.1.1 Brightness and Contrast Grayscale.Lst
o L4.1.2 Brightness and Contrast Color.Lst
o L4.1.3 Brightness and Contrast Cascade.Lst

Present Chapter 4 Slides 197-203
Review Lab 4.2 Instructions
Assign and ask students to complete Lab 4.2 Worksheets:
L4.2.1 Threshold and Negation Grayscale Image.Lst
L4.2.2 Threshold and Negation Grayscale Camera.Lst
L4.2.3 Threshold and Negation Color.Lst
L4.2.4 Threshold and Negation Pixel Map Grayscale.Lst
L4.2.5 Threshold and Negation Pixel Map Color.Lst
L4.2.6 Threshold and Negation Pixel Map Color Camera.

Homework:
Textbook pp. 206-211

Discuss or assign these Lab Overview Questions:
o Where have you seen image thresholding in use?
Answer: There are many places...the first one that comes to mind is a
weatherman being placed in front of a map.
o Consider a system using two images. The first is a picture with containing object that you would like to separate from the background and the second picture if only the background with some noise added to it. How could you thresholding to remove the background from the object of interest?
Answer: Here is an idea: subtract the two and threshold just above the level. This results in a mask, which can be used to remove the object of interest. Note that the color that will be left out of the object of interest will be black or white (depending on the order of subtraction).
o Design a system which will separate out your portrait from the background of your school ID. What additional blocks will you require for a colored image? How about separating objects from a video stream?
Answer: Open discussion. For the color image separation, you might use parallel masks for the 3 color planes or use them jointly with an appropriate threshold.

Introduce students to Chapter 4.3, Chapter 4 Slide 204
Present Chapter 4 Slides 205-208

Define this term: Mask
Discuss Key Concepts:
o The sum of two images can be multiplied by 0.5 to keep the pixel values getting too large for the number of allowed bits per pixel.
o When subtracting two images, the absolute value can be used to make negative pixel values positive.

Complete Lab 4.2 Worksheets
o Have students continue to work on and complete worksheets listed in first day of this lab.
Image thresholding is another powerful tool for our use. The idea of removing an object of interest from a different colored background arises in many places in the world of signal processing.

Textbook pp. 213-223.

Assign and complete Lab 4.3 Worksheets:
o L4.3.1 Adding and Subtracting Images.Lst
Class/Group 4.4 Discussion
Discuss or assign these Lab Overview Questions:
o Can you think of a practical real-world application where subtracting images is useful?
Answer: How about a security camera? Image subtraction could be used to tell when the image changed--for example, when an intruder came into the room.
Have the students build this worksheet in VAB?
o Can you think of a practical real-world application where adding images is useful?
Answer: How about special effects? Did you ever wonder how Hollywood movie ghosts were made? Simple, by adding an image to its background.
Summary Since images are just groups of numbers, anything we can do to a group of numbers, we can do to an image. In this lab we added two images, and subtracted two images. These kinds of operations make up a huge chunk of the tools engineers use to manipulate images. There is nothing complex or magical about them, just simple mathematical operations such as adding, subtracting, and multiplying.

Homework: review Chapter 4.3

Present Chapter 4 Slides 214-216
Review Lab 4.4 Instructions
Notes: This lab requires a good knowledge of Lab 4.3 - Adding and
Subtracting Images. Be sure to finish the previous lab before starting this one. Also, you might want to go over the materials on pages 219 to 223 before starting this lab, and especially the contents of Figure 4.29 on page 221.

Discussion
Discuss or assign these Lab Overview Questions:
o Can you think of a practical real-world application where adding a shifted image to itself is useful?
Answer: Sometimes, we want to blur an image to get a cool effect. Adding several shifted but otherwise identical images creates a blurring effect that is quite useful in some scenarios.
o Can you think of a practical real-world application where subtracting a shifted image from itself is useful?
Answer: What about planetary astronomy? In astronomy, we look at energy patterns (light, X-rays, gamma ray, infrared) in the sky and try to see what has changed, if anything, from previous nights. Sometimes, we can't get the picture positioned in an identical place in the sky. By shifting and subtracting images, we can register the two images and figure out what has changed. Maybe we can discover a new comet or asteroid this way!
Summary: This lab has introduced you to an important concept: the shifting of images. Image shifting (also called image translation) is an important processing tool in manipulating images. In later labs, you'll learn how we can use image shifts to empmasize edges in images, create more interesting image blurs, and remove noise from images.

Assign and ask students to complete Lab 4.4 Worksheets:
o L4.4.1 Adding and Subtracting Shifted Images.Lst
o L4.4.2. Adding and Subtracting Shifted Images Camera.Lst

Objectives
Students will:
Present Chapter 4 Slides 223-224

Discuss Key Concepts:
o Smoothing increases blur, reduces the sharpness of edges, and reduces noise.
o Sharpening reduces blur, increases the sharpness of edges, and increases the noise.
o Compute results for a Horizontal Difference Processor
o Gain an understanding of Edge Finding and Image Sharpening
o Discover how a robot uses edge finding and other processes in 4.1 to recognize objects.

Review Lab 4.5 Instructions
Assign and ask students to complete Lab 4.5 Worksheets:
o L4.5.1 Sharpening Images.Lst
Notes: This lab is based on the material on pages 228 to 230 of Engineering Our Digital Future. Be sure to go over the discussion on these pages and the contents of Figure 4.32. The students will be making versions of these pictures in their experiments.

Discussion
Discuss or assign these Lab Overview Questions:
o Why is image sharpening useful? Describe situations where you would want to use image sharpening.
Answer: Besides blurry photos of friends, image sharpening is used in exploration (astronomy), medicine (making X-ray images sharper), and entertainment (giving an enhanced effect to a movie scene).
o What problems might you come across by using image sharpening?
Answer: If the image is noisy, then sharpening the image usually increases the effective noise level. Sometimes, the noisier image is harder to look at or read. In such cases, it is more useful to process parts of the image that need enhancement, especially if these parts are less noisy than others.
Summary: Image sharpening is a useful procedure for making blurry pictures clearer. But beware: it can make noise in images larger, too.

Note: Figure 4.31 on page 225 gives a preview of what students will
do in this lab. Read through Section 4.4 and explain Figure 4.31 to the class before doing this lab.

Review Lab 4.6 Instructions
Assign and ask students to complete Lab 4.6 Worksheets:
o L4.6.1 Edge Detectors Grayscale Image.Lst
o L4.6.2 Edge Detectors Grayscale Camera.Lst
o L4.6.3 Edge Detectors Color Image.Lst
Class/Group Discussion
Discuss or assign these Lab Overview Questions:
o How might edge detection be used in practice?
Answer: It could be used to help a robot pick up and manipulate objects in its environment.
o What defines an edge in a digital image?
Answer: An edge is defined by a change in color or gray scale, usually at the boundaries of objects.
Summary: Edge detection is a basic building block of image processing systems. As you can see, the methods for edge detection are pretty simple and easy to do.

 

Continue and finish chapter 4 labs.

 

 

Begin Chapter 5