convolution

I have a large image as an 2D array (let's assume that it is a 500 by 1000 pixels gray scale image). And I have one small image (let's say is it 15 by 15 pixels). I would like to slide the small image over the large one and for a given position of the small image I would like to calculate a measure of similarity between the small image and the underling part of the big image. I would like to be flexible in choosing a measure of similarity. For example I might want to calculate mean squared deviation or mean absolute deviation or something else (just some operation that takes two matrices of

Here is my code which I used for checking the correctness of convolve2d import numpy as np from scipy.signal import convolve2d X = np.random.randint(5, size=(10,10)) K = np.random.randint(5, size=(3,3)) print "Input's top-left corner:" print X[:3,:3] print 'Kernel:' print K print 'Hardcording the calculation of a valid convolution (top-left)' print (X[:3,:3]*K) print 'Sums to' print (X[:3,:3]*K).sum() print 'However the top-left value of the convolve2d result' Y = convolve2d(X, K, 'valid') print Y[0,0] On my computer this results in the following: Input's top-left (3x3) corner: [[0 0 0] [1 1 2

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 know, i know! FFT convolutions is very fast. But in this project i CAN'T use it. It is a constraint of the project to not use FFT (please don't ask why :/). This is my code in C# (ported from matlab, by the way): var result = new double[input.Length + filter.Length - 1]; for (var i = 0; i < input.Length; i++) { for (var j = 0; j < filter.Length; j++) { result[i + j] += input[i] * filter[j]; } } So, anyone knows any fast convolution algorithm widthout FFT? Convolution is numerically

I am trying to perform a 2d convolution in python using numpy I have a 2d array as follows with kernel H_r for the rows and H_c for the columns data = np.zeros((nr, nc), dtype=np.float32) #fill array with some data here then convolve for r in range(nr): data[r,:] = np.convolve(data[r,:], H_r, 'same') for c in range(nc): data[:,c] = np.convolve(data[:,c], H_c, 'same') data = data.astype(np.uint8); It does not produce the output that I was expecting, does this code look OK, I think the problem is with the casting from float32 to 8bit. Whats the best way to do this Thanks Since you already have

I have been following the tutorials on DeepLearning.net to learn how to implement a convolutional neural network that extracts features from images. The tutorial are well explained, easy to understand and follow. I want to extend the same CNN to extract multi-modal features from videos (images + audio) at the same time. I understand that video input is nothing but a sequence of images (pixel intensities) displayed in a period of time (ex. 30 FPS) associated with audio. However, I don't really understand what audio is, how it works, or how it is broken down to be feed into the network. I have

When calculating a simple moving average, numpy.convolve appears to do the job. Question: How is the calculation done when you use np.convolve(values, weights, 'valid') ? When the docs mentioned convolution product is only given for points where the signals overlap completely , what are the 2 signals referring to? If any explanations can include examples and illustrations, it will be extremely useful. window = 10 weights = np.repeat(1.0, window)/window smas = np.convolve(values, weights, 'valid') Convolution is a mathematical operator primarily used in signal processing. Numpy simply uses this

I am trying to understand the strides argument in tf.nn.avg_pool, tf.nn.max_pool, tf.nn.conv2d. The documentation repeatedly says strides: A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor. My questions are: What do each of the 4+ integers represent? Why must they have strides[0] = strides[3] = 1 for convnets? In this example we see tf.reshape(_X,shape=[-1, 28, 28, 1]) . Why -1? Sadly the examples in the docs for reshape using -1 don't translate too well to this scenario. dga The pooling and convolutional ops slide a "window" across

Is there an elegant way in Matlab to get the output of normxcorr2 cropped to the size of the image or cropped only to the part of the matrix that does not use zero padded edges in computation? To understand what I mean, consider the conv2 command. There is an optional parameter called shape that can be set to same or valid . C = conv2(A,B,'same'); C = conv2(A,B,'valid'); For example: size( conv2( rand(50,50) , rand(6,6), 'valid') ) ans = 45 45 size( conv2( rand(50,50) , rand(6,6), 'same') ) ans = 50 50 size( conv2( rand(50,50) , rand(6,6)) ) ans = 55 55 Currently I wrote my own function, that