RBE306TC Computer Vision Systems
Lab Manuals and Reports
Lab 1 on Nov. 10th, 2023
Objectives :
• Introducing the image processing capabilities of Matlab with Image Processing Toolbox
• Learn to read and display images
• Learn basic image processing steps
• Learn several image enhancement techniques
Before you dive into this Exercise 1 to Exercise 3, please check the following OpenCV functions in
Python Coding Platform for example:
imread, shape, imshow, imwrite, imnoise, resize, calcHist, equalizeHist, etc.
Some other Python built-in functions, or functions in Scipy package may also be used. Please refer
to online resources.
Hint : read the descriptions about each of the previous functions and any other function you might use. You may find descriptive sections of Algorithms(s) in some of the Python functions.
Task in Lab 1 (20%)
In this lab, we use the monochrome image Lenna (i.e., lenna512.bmp) to conduct the following subtasks. Let’s call the original image Lenna as I 0 .
• (a) I 0 -> down-sampling to I 1 with 1/2 size of I 0 (both horizontally and vertically) using the mean value (implement it by yourself). Display it and compare to the original image. Explain your finding in the report (5%).
• (b) I 1 -> up-sampling to I 1 ’ with the same size of I 0 using nearest neighbour interpolation (implement it by yourself). Display it and compare to the original image. Explain your finding in the report (5%).
• (c) Calculate the PSNR between the original image I 0 and the up-sampled images, i.e., nearest ， bilinear， and bicubic ， respectively ， Compare the results of different interpolation methods.
Explain your finding in the report. (Note: for the bilinear and bicubic interpolation, please use the
Matlab function directly) (10%)
* For the peak value use 255, the PSNR should be calculated via:
Lab 2 on Nov. 17th, 2023
Objectives :
• Learn different image enhancement techniques
• Learn basic morphological operations
Task in Lab 2 (20%)
Feature detection and matching: edge detection, interest points and cornets, local image features, and feature matching
Morphological operation on the image of im_sawtooth (please load the image sawtooth.bmp as im_sawtooth ).
• (a). Extract the boundary of the image, and show it in the report (10%).
• (b). Conduct the operations of erosion, dilation, opening, and closing. Please use the function of strel to create the structuring element with the shape of disk (You can set your preferred radius).
Show the results after each operations and calculate the number of foreground pixel. Write your comments on comparing the results of dilation and closing (10%).
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