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]
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.
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 OpenCV 3 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 OpenCV 3 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 OpenCV 3 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 OpenCV 3 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 OpenCV 3 PDF)
tracking.cpp: Locate an object in the image. (p.144 in OpenCV 3 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.