Notch-Filter-for-Image-Processing

★ 35 Open GitHub ↗

Implemented Ideal, ButterWorth and Gaussian Notch Filter for Image processing in python (with GUI).

Explore Similar Repositories

Human-Activity-Recognition-with-Neural-Network-using-Gyroscopic-and-Accelerometer-variables : The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.
filtering-stft-and-laplace-transform : Simple demo of filtering signal with an LPF and plotting its Short-Time Fourier Transform (STFT) and Laplace transform, in Python.
// repository documentation