Implement Canny edge dectetion, Image Gradient Blending, Image Morphing, Seam Carving, Image Stitching and Optical Flow. (Note: Didn't use any package for all the implementations)
we are going to implement a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform Bone Age Assessments. We first normalize our input images, because they have different sizes and background color. We then try different ways including Image Processing Technology, Single Shot MultiBox Detector (SSD) and Mask R-CNN to remove annotation markers and background border from image to make our data more clearly. We finally feed processed pictures to GoogLeNet to predict the age of bone. As for the result, we can get accuracy of roughly 95% if allowing error within 2 years, while roughly 80% if allowing error within 1 years.
Implement machine perception technology include: logo projection, scale invariant, SIFT matching and pose estimation.