convolution

问题 I want to do 2D convolution of an image with a Gaussian kernel which is not centre originated given by equation: h(x-x', y-y') = exp(-((x-x')^2+(y-y'))/2*sigma) Lets say the centre of kernel is (1,1) instead of (0,0). How should I change my following code for generation of kernel and for the convolution? int krowhalf=krow/2, kcolhalf=kcol/2; int sigma=1 // sum is for normalization float sum = 0.0; // generate kernel for (int x = -krowhalf; x <= krowhalf; x++) { for(int y = -kcolhalf; y <=

问题 I am trying to compute Deconvolution using Python. I have a signal let say f(t) which is the convoluted by the window function say g(t). Is there some direct way to compute the deconvolution so I can get back the original signal? For instance f(t) = exp(-t**2/3); Gaussian function and g(t) = Trapezoidal function Thanks in advance for your kind suggestion. 回答1: Is this an analytical or numerical problem? If it's numerical, use scipy.signal.devconvolve: http://docs.scipy.org/doc/scipy/reference

问题 Did a quick search and couldn't find much about this. Say I have a 2D matrix and a 1D 'response function'. I want to convolve each row of the 2D matrix with the response function. I can do this by: for i=1:numrows answer(:,i) = conv(2dmatrix(:,i),response_function,'same'); end but it's super slow! Any tips to accelerate this? Thanks 回答1: This code reproduces your results on randomly generated matrices: conv2(response_function,1,2dmatrix,'same') conv2 allows you to convolute along rows and

问题 Assume the following matrix acts as both an image and a kernel in a matrix convolution operation: 0 1 2 3 4 5 6 7 8 To calculate the neighbour pixel index you would use the following formula: neighbourColumn = imageColumn + (maskColumn - centerMaskColumn); neighbourRow = imageRow + (maskRow - centerMaskRow); Thus the output of convolution would be: output1 = {0,1,3,4} x {4,5,7,8} = 58 output2 = {0,1,2,3,4,5} x {3,4,5,6,7,8} = 100 output2 = {1,2,4,5} x {3,4,6,7} = 70 output3 = {0,1,3,4,6,7} x

问题 I modify the FCN net and design a new net,in which I use two ImageData Layer as input param and hope the net produces a picture as output. here is the train_val.prototxt and the deploy.prototxt the original picture and the label are both gray scale pics and sizes are 224*224. I've trained a caffemodel and use infer.py to use the caffemodel to do a segmentation,but meet the error: F0505 06:15:08.072602 30713 net.cpp:767] Check failed: target_blobs.size() == source_layer.blobs_size() (2 vs. 1)

问题 Here is an example of convolution given: I have two questions here: Why is the vector 𝑥 padded with two 0s on each side? As, the length of kernel ℎ is 3. If 𝑥 is padded with one 0 on each side, the middle element of convolution output would be within the range of the length of 𝑥 , why not one 0 on each side? Explain the following output to me: >> x = [1, 2, 1, 3]; >> h = [2, 0, 1]; >> y = conv(x, h, 'valid') y = 3 8 >> What is valid doing here in the context of the previously shown

问题 Although there are many resources about how to calculate the receptive field (RF) of CNNs (ex: http://fomoro.com/tools/receptive-fields), I didn't find anything regarding skip connections. In [1] they mention that skip connections make the effective RF smaller, but what happens to the theoretical RF? At the end of the day, I would like to know how to calculate the receptive field of a network comprising many residual blocks . Thanks, Daniel 回答1: TL;DR compute the receptive field ignoring all

问题 I have a CNN model that is trained using a set of 120 pictures. The images are converted in TFR record and labeled with this method def write_records_file(dataset, record_location): """ dataset : dict(list) Dictionary with each key being a label for the list of image filenames of its value. record_location : str Location to store the TFRecord output. """ writer = None # Enumerating the dataset because the current index is used to breakup the files if they get over 100 current_index = 0 for

问题 I am dealing with this kind of image (upper is post-processed) (lower is raw) So, first I converted the grayscale image into pure black and white binary image. I am interested in detecting the white blobs, and want to get rid of the arc-like smears in the corners. How can I do that? I general, I know that my targets are almost circular in shape, not too big, but I want to encode something that automatically gets rid of everything else, like the lighter arcs in the upper left and right corners

问题 I am using Keras library to build this deep learning model: INPUT(depth=1, height=15, width=27) -> CONV[depth=8](height=4, width=27) -> POOL(height=2, width=1) -> (Regression) output. I expect the ouput shape from convolution2d_1 to be (None, 8, 12, 1) and thence, the ouput shape from pooling2d_1 to be (None, 8, 6, 1); while I am getting (None, 8, 15, 27) and (None, 8, 7, 27) respectively. What am I doing or interpreting wrong here? P.S.: Also, this setting gives a Baseline Error: 99.23%!