I\'m writing a c++ program for a class to do convolution in the frequency domain, and I noticed the final result had error along the corner. So I tried it out in MATLAB and got the exact same results.
I have a 3d array like this. int sz[] = {240, 240, 240}; Mat accumarray(3, sz, CV_32S, Scalar::all(0)) ;
I need an 1D Convolution against 2 big arrays. I\'m using this code in C# but it takes a loooong time to run.
I have a current implementation of Gaussian Blur using regular convolution. It is efficient enough for small kernels, but once the kernels size gets a little bigger, the performance takes a hit. So, I
I\'m pretty new to Image Processing and found out that the FFT convolution speeds up the convolution with large kernel sizes a lot.
Profiling some computational work I\'m doing showed me that one bottleneck in my program was a function that basically did this (np is numpy, sp is scipy):
This algorithm is meant to apply a Roberts operator to an image, and store the result in a new file. Instead, this code outputs the exact same image as the input.
The probability distribution of the sum of two random variables, x and y, is given by the convolution of the individual distributions. I\'m having some trouble doing this numerically. In the following
I need to filter an image using a bank of filters in Matlab.My first attempt was to use a开发者_如何转开发 simple for loop to repeatedly call the \"imfilter\" function for each filter in the bank.
What makes a convolution kernel separable? Ho开发者_StackOverfloww would I be able to tell what those separable parts were in order to do two 1D convolutions instead of a 2D convolution>
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