Goal: Histogram processing for image enhancement

• Know how to enhance image by histogram processing
• Apply look-up table to enhance images
• Learn histogram equalization, histogram backpropagation, and mean shift
• Practice image retrieval by histogram matching.

Readings and sample codes

• OpenCV 3 computer vision application programming cookbook. R. Laganière, Packt Publishing, 2017. [Book URL]
  • Chapter 4 Counting Pixels with Histograms (PDF, code)
• See the README.txt in code for more details of sample codes.
• You can use the provided sample codes in your homework, but you have to replace the images with yours.

Web Report

• Create a web page with description texts and a lot of pictures for the following programs
  • histogram.cpp: (1) Computing the image histogram. (2) Equalize the image. (3) Applying Look-up Tables to Modify Image Appearance. (p.106, p.107, p.112, p.114, p.116, p.117 in PDF)
    • Change the image and the look-up tables to practice this sample code.
  • contentfinder.cpp: Backprojecting a Histogram to Detect Specific Image Content. (p.118, p.120, p.124 in PDF).
    • Change images with blue sky or green grass to get your results.
  • finder.cpp: Using the Mean-shift Algorithm to Find an Object. (p.126, p.128 in PDF).
    • Change the image with persons and use human's face as the target to apply mean-shot algorithm.
  • retrieve.cpp: Retrieving Similar Images using Histogram Comparison (p.132 in PDF)
    • Collect a set of images with "similar colors". Use one image as query image, all the other images as gallary images. Retrieve similar image of the query from the gallery.
  • integral.cpp: Create a binary image by adaptively setting a threshold. (p.140 in PDF)
  • tracking.cpp: Locate an object in the image. (p.144 in PDF)
• You have to give explanations of the codes. And you have to identify how did you modify the source code to get your results.
• Submit your web address by Google Classroom.