convolution

Intuitive understanding of 1D, 2D, and 3D Convolutions in Convolutional Neural Networks

由 爷，独闯天下 提交于 2019-11-27 04:55:19
问题 Can anyone please clearly explain the difference between 1D, 2D, and 3D convolutions in CNN (Deep Learning) with examples? 回答1: I want to explain with picture from C3D. In a nutshell, convolutional direction & output shape is important! ↑↑↑↑↑ 1D Convolutions - Basic ↑↑↑↑↑ just 1 -direction (time-axis) to calculate conv input = [W], filter = [k], output = [W] ex) input = [1,1,1,1,1], filter = [0.25,0.5,0.25], output = [1,1,1,1,1] output-shape is 1D array example) graph smoothing tf.nn.conv1d

Android: fast bitmap blur?

由 丶灬走出姿态 提交于 2019-11-27 02:43:30
I've been searching the past three days for a built-in, hardware-accelerated way of bluring a bitmap with android. I stumbled upon certain work-arounds like shrinking the bitmap and scaling it up again, but this method produced low quality results which were not suitable for my image recognition requirements. I also read that implementing convolution with shaders or JNI is a good way to go, but I cannot believe that there is no built-in solution in the Android framework for this very common purpose. Currently I've ended up with a self-written convolution implementation in Java, but it is

Improving Numpy Performance

由 非 Y 不嫁゛ 提交于 2019-11-27 01:08:41
问题 I'd like to improve the performance of convolution using python, and was hoping for some insight on how to best go about improving performance. I am currently using scipy to perform the convolution, using code somewhat like the snippet below: import numpy import scipy import scipy.signal import timeit a=numpy.array ( [ range(1000000) ] ) a.reshape(1000,1000) filt=numpy.array( [ [ 1, 1, 1 ], [1, -8, 1], [1,1,1] ] ) def convolve(): global a, filt scipy.signal.convolve2d ( a, filt, mode="same" )

Dealing with Boundary conditions / Halo regions in CUDA

由 岁酱吖の 提交于 2019-11-26 20:29:31
问题 I'm working on image processing with CUDA and i've a doubt about pixel processing. What is often done with the boundary pixels of an image when applying a m x m convolution filter? In a 3 x 3 convolution kernel, ignoring the 1 pixel boundary of the image is easier to deal with, especially when the code is improved with shared memory. Indeed, in this case, one does not need to check if a given pixel has all the neigbourhood available (i.e. pixel at coord (0, 0) has not left, left-upper, upper

2-D convolution as a matrix-matrix multiplication

由 两盒软妹~` 提交于 2019-11-26 18:58:27
问题 I know that, in the 1D case, the convolution between two vectors, a and b , can be computed as conv(a, b) , but also as the product between the T_a and b , where T_a is the corresponding Toeplitz matrix for a . Is it possible to extend this idea to 2D? Given a = [5 1 3; 1 1 2; 2 1 3] and b=[4 3; 1 2] , is it possible to convert a in a Toeplitz matrix and compute the matrix-matrix product between T_a and b as in the 1-D case? 回答1: Yes, it is possible and you should also use a doubly block

Image convolution in frequency domain

由 北战南征 提交于 2019-11-26 18:43:07
问题 I want to Convolve Lena with itself in the Frequency Domain. Here is an excerpt from a book. which suggests how should the output of the convolution be: I have written the following application to achieve the Convolution of two images in the frequency domain. The steps I followed are as follows: Convert Lena into a matrix of complex numbers. Apply FFT to obtain a complex matrix. Multiply two complex matrices element by element (if that is the definition of Convolution). Apply IFFT to the

Convolving image with kernel in Fourier domain

由 本小妞迷上赌 提交于 2019-11-26 17:21:12
问题 I'm using zero padding around my image and convolution kernel, converting them to the Fourier domain, and inverting them back to get the convolved image, see code below. The result, however, is wrong. I was expecting a blurred image, but the output is four shifted quarters. Why is the output wrong, and how can I fix the code? Input image: Result of convolution: from PIL import Image,ImageDraw,ImageOps,ImageFilter import numpy as np from scipy import fftpack from copy import deepcopy import

2D Convolution in Python similar to Matlab's conv2

由 不羁的心 提交于 2019-11-26 16:54:15
问题 I have been trying to do Convolution of a 2D Matrix using SciPy, and Numpy but have failed. For SciPy I tried, sepfir2d and scipy.signal.convolve and Convolve2D for Numpy. Is there a simple function like conv2 in Matlab for Python? Here is an example: A= [ 5 4 5 4; 3 2 3 2; 5 4 5 4; 3 2 3 2 ] I want to convolve it with [0.707 0.707] And the result as by conv2 from Matlab is 3.5350 6.3630 6.3630 6.3630 2.8280 2.1210 3.5350 3.5350 3.5350 1.4140 3.5350 6.3630 6.3630 6.3630 2.8280 2.1210 3.5350 3

Verify the convolution theorem

由 陌路散爱 提交于 2019-11-26 16:48:36
问题 My main goal is to show that the convolution theorem works (just a reminder: the convolution theorem means that idft(dft(im) .* dft(mask)) = conv(im, mask) ). I'm trying to program that. Here is my code: function displayTransform( im ) % This routine displays the Fourier spectrum of an image. % % Input: im - a grayscale image (values in [0,255]) % % Method: Computes the Fourier transform of im and displays its spectrum, % (if F(u,v) = a+ib, displays sqrt(a^2+b^2)). % Uses display techniques

Use convolution to find a reference audio sample in a continuous stream of sound

由 痞子三分冷 提交于 2019-11-26 16:39:02
问题 in my previous question on finding a reference audio sample in a bigger audio sample, it was proposed, that I should use convolution. Using DSPUtil, I was able to do this. I played a little with it and tried different combinations of audio samples, to see what the result was. To visualize the data, I just dumped the raw audio as numbers to Excel and created a chart using this numbers. A peak is visible, but I don't really know how this helps me. I have these problems: I don't know, how to
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