OpenCV was instrumental in making this project work. It provided many useful utilities such as reading and writing images, modifying images for the downsampling method, displaying images, and most importantly extracting keypoints and descriptors using SIFT, SURF, and ORB.
Scikit-learn provided K Means clustering and AdaBoost
Numpy provided easy matrix math
Scipy provided an implementation of the FFT algorithm
PyWavelets provided an implementation of the discrete wavelet transform
Kaggle ASL Alphabet dataset as our data source for training and testing data
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“FAST Algorithm for Corner Detection.” OpenCV. Available: opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_fast/py_fast.html#goal.
Galarnyk, Michael. “PCA Using Python (Scikit-Learn).” Medium, Towards Data Science, 4 Nov. 2019. Available: towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60.
Kakarwal, Sangeeta & Deshmukh, Ratnadeep. (2010). Wavelet Transform based Feature Extraction for Face Recognition. International Journal of Bioinformatics Research and Applications. I. 100-104.
“Rescale, Resize, and Downscale.” Rescale, Resize, and Downscale - Skimage v0.17.dev0 Docs. Available: scikit-image.org/docs/dev/auto_examples/transform/plot_rescale.html.
Rosten E., Drummond T. (2006) Machine Learning for High-Speed Corner Detection. In: Leonardis A., Bischof H., Pinz A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3951. Springer, Berlin, Heidelberg
University of Michigan EECS 442 Lecture Slides. Jason Corso. Fall 2018
University of Michigan EECS 445 Lecture Slides. Sindhu Kutty. Winter 2020