Programming Homework

  • Goal: Build OpenCV project and learn to access(read/write) image pixels.
    • Practice some basic image processing functions: add noise, color reduction, image enhancement, image addition.
    • Learn to access pixels by different ways: scanning an image with pointers, scanning an image with iterators.
    • Learn to write efficient image scanning loops.
    • Extra: Study the Video IO and Video Analysis modules of OpenCV.

  • Goal: Histogram processing for image enhancement
    • Know histogram
    • Apply look-up table to enhance images
    • Learn histogram equalization, histogram backpropagation, and mean sift
    • Practice image retrieval by histogram matching.

  • Goal: Learn image filters
    • Remove noise by blurring filters: Gaussian, mean and median filters.
    • Detect edges by directional filters: sobel and laplacian files.

  • Goal: Learn to implement Harris corner detector
    • Practice Harris corner detector by OpenCV
    • Understand the effect of parameters of Harris detector

  • Goal: Applied Gray-Level Co-occurrence Matrix(GLCM) to generate texture feature maps on the given image data with various sizes of windows, various size of bins.

  • Goal: apply k-means and Gaussian mixture model to segment images.

  • Goal: Use the given code and given data to test U-Net model. You will need to write your own performance metrics code, intersection over union (IOU) and dice metric (DM) to conduct the experimental result.