# Connected-Component Labeling and Set Operations

As discussed in L5, this project considers the application of a sequence of simple image-processing operations to an image, such as connected-component labeling and logical (set) operations.

**Bright-Region Extraction**- Consider the gray-scale image, “lake.gif.” Experimentally choose a threshold so that
*>*4 distinct “bright” components obviously appear. Save this thresholded image, “fthresh,” in a form that makes the thresholded objects visible on the screen. - Find the connected components of the thresholded image “fthresh.” You can use the MATLAB function bwlabel for this purpose. The following MATLAB call creates a labeled image called ”flabel” from the input thresholded (binary valued!) image ”fthresh”:

- Consider the gray-scale image, “lake.gif.” Experimentally choose a threshold so that

[flabel, num] = bwlabel(fthresh, 8) where 8-connectivity is assumed for the components and num is the number of connected components labeled in “fthresh”. To display this labeled image with colored components, you can use fRGB = label2rgb(flabel);

And then use imshow(fRGB) to see the colored labeled image.

- Save the 2 largest components of your labeled image and delete the other components by setting their constituent pixels to 0. You will need to write a function to do this.
- Be sure to give output images for all steps above.

**Logical (Set) Operations**

Note: you are to write your own functions for the operations in this part of the project — you may NOT

use built-in Matlab functions.

- Write Matlab functions for the AND, OR and XOR binary-image operators and NOT unary-image operator, using
**A**and**B**as input images. What are the quantities**A**AND**B**,**A**OR**B**,**A**XOR**B**, and NOT(**A**) in terms of set union, intersection, and complement? - Let
**A**be the “match1” image and**B**be the “match2” image. Compute the following images:**A**AND

**B **, **A **OR **B**, **A **XOR **B**, and NOT(**A**)

- Build the minimum operator and compute
**E**= min(**C***,***D**)*,*where image**C**is “mandrill gray” and image**D**is “cameraman.” For each pair of pixels (*x,y*) in the two input images, the minimum operator assigns the minimum of the two values (**C**(*x,y*) and**D**(*x,y*)) to the output**E**(*x,y*). As we will see later during our discussion of Morphological Image Processing (G&W Ch. 9), the minimum operator is a gray-scale analog of set intersection (AND) and is sometimes called “erosion.”

- Write a report in the standard format. Be sure to describe all of your methods, including 1(c) and other parts.

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