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- # Importing all needed libraries import
**numpy**as np import math. That's all, yeah we need the pure**numpy**and math library. 2. Now let's create a class that will have the implimentation of the algorithm and first**function**that will separate our data set by class. - For creating the first octave, a
**gaussian**filter is applied to an input image with different values of sigma, then for the 2nd and upcoming octaves, the image is first down-sampled by a factor of 2 then applied**Gaussian**filters with different values Numbers in**Python**# In**Python**, Numbers are of 4 types: Integer So the**function**requires 4 points ... **numpy.column_stack() in Python**.**numpy.column_stack() in Python**. The**numpy**.column_stack()**function**stacks the 1-D arrays as columns into a 2-D array. It takes a sequence of 1-D arrays and stacks them as columns to make a single 2-D array. Syntax.**numpy**.column_stack(tup) Parameter- The inverse of a matrix can also be calculated in
**Python**. This tutorial demonstrates the different ways available to find the inverse of a matrix in**Python**. Using the**Gauss**-Jordan method to find the inverse of a given matrix in**Python**. Using the**numpy**.linalg.inv ()**function**to find the inverse of a given matrix in**Python**. - Simple image blur by convolution with a
**Gaussian**kernel. The original image; Prepare an**Gaussian**convolution kernel; Implement convolution via FFT; A**function**to do it: scipy.signal.fftconvolve() Previous topic. Curve fitting: temperature as a**function**of month of the year. Next topic. Image denoising by FFT