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tuw_cv's Introduction

tuw_cv

Parts (click on Task to see .ipynb results):

Task 0: Aligning 3 images based on normalized cross-correlation (NCC).:

  • Implementation of NCC in numpy
  • aligning images based on correlation

Task 1: Scale-Invariant Blob Detection:

  • implementation of scale-normalized Laplacian of Gaussian (LoG) operator
  • building LoG pyramid
  • finding blobs from local maxes

Task 2: Image Stitching:

  • detecting keypoints using SIFT
  • estimation of putative matches between local descriptors of 2 images
  • usage of RANSAC (because of false-matches) to estimate transformation between images
  • join image based on transformation matrices

Task 3: Classical ML approach to image classification:

  • extract features from images
  • create clusters of image features (use KMeans to create bag-of-words model)
  • use k-NN to predict class based on manually extracted features

Task 4: Deep Learning approach to image classification:

  • Implementation of PyTorch model to classify images
    • Dataset (augmentation, loading)
    • Trainer (losses, optimizers, training in general)
    • Classifier (NN architecture)

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