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- 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.
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