Computer Vision 106-2

Programming Homework HWP2:

Counting Pixels with Histograms

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 2 computer vision application programming cookbook. R. Laganière, Packt Publishing, 2011. [Book URL]
  - Chapter 4 Counting Pixels with Histograms (PDF, code)
- OpenCV 3 computer vision application programming cookbook. R. Laganière, Packt Publishing, 2017. [Book URL]
  - Chapter 4 Counting Pixels with Histograms (code)
- Please see the README.txt 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.
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.93, p.98, p. 100, p.101, p.102 in OpenCV2 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.104, p.107 in OpenCV2 PDF).
    - Change the image with a new image of blue sky or green grass.
  - finder.cpp: Using the Mean-shift Algorithm to Find an Object. (p.111 in OpenCV2 PDF).
    - Change the image with a new image of persons and use human's face as the target to apply mean-shift algorithm.
  - retrieve.cpp: Retrieving Similar Images using Histogram Comparison (p.114 in OpenCV2 PDF)
    - Collect a set of images with "similar colors". Use one image as query image, and all the other images as gallery images. Retrieve similar images of the query from the gallery.

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