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Search found 58 results on 3 pages for 'gaussian'.

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  • How to Save Filters with PNG in Inkscape

    - by Uri Herrera
    I have created a graphic with multiple layers in Inkscape. One of the layers is some text. I have applied a drop shadow filter to the text. When I save the file as PNG, the drop shadow is not saved. I have also tried applying a gaussian blur to the text layer and to the text layer after converting it to an object. The blur is not applied. How can I save the file as PNG with the drop shadow intact?

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  • Ubuntu 12.04 desktop crash [closed]

    - by David Mannock
    12.04 is stable under a light load, but not under intensive use of Gaussian 09 vB1. Looks like a heating issue, but psensor says that all is well on 32-cores @56C. Similar results for 64 cores. Machine shuts down after 2-3h. Syslog shows shut down. Whoopsie crash reporter sends in report. After 259 updates on the weekend, I am left wondering what the heck is wrong with this release? My answer would be "EVERYTHING!". Can someone help me do some systematic checks on this OS and hardware.

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  • Where can I find "magic numbers" for classic game play mechanics?

    - by MrDatabase
    I'd like to find some "magic numbers" for the classic helicopter game. For example the numbers that determine how fast the helicopter accelerates up and down. Also perhaps the "randomness" of the obstacles (uniformly distributed? Gaussian?). Where can I find these numbers? p.s. I don't care about the particular platform... Flash on the desktop browser is just as good as some implementation on a mobile device.

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  • Where to find algorithms work?

    - by Misha
    The funnest parts of my projects have been the back-end algorithms work. I have worked on projects where I implemented Gaussian Mixture models, a Remez algorithm and a few Monte Carlo schemes. I loved figuring out how these processes worked and tuning them when they didn't. I recently graduated and my problem lies in the work I was able to find. The only jobs I have found, with my Electrical Engineering degree, are for writing user applications. Tasks such as fashioning web interfaces or front-ends for hardware devices. When I speak with potential employers about my interests they say they have no work of the sort. Where does one find work that involves implementing these kind of schemes?

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  • Math behind multivariate testing for website optimization

    - by corkjack
    I am looking for theoretical resources (books, tutorials, etc.) to learn about making sound statistical inferences given (plenty of) multivariate website conversion data. I'm after the math involved, and cannot find any good non-marketing stuff on the web. The sort of questions I want to answer: how much impact does a single variable (e.g. color of text) have? what is the correlation between variables? what type of distribution is used for modelling (Gaussian, Binomial, etc.)? When using statistics to analyze results - what should be considered as a random variable - the web-page element that gets different variations or the binary conversion-or-no-conversion outcome of an impression? There's plenty of information about different website optimization testing methods and their benefits\pitfalls, plenty of information about multivariate statistics in general, do you guys know of resources that discuss statistics in this specific context of website optimization ? Thanks for any info!

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  • Seeking a simultaneous fade and blur effect using JQuery or Javascript

    - by Heath Waller
    Can anyone think of a way to simulate the fade/blur flash effect used in the following website: http://www.frenchlaundry.com/ (image fades and blurs on hover, while text fades in simultaneously) using JQuery? I am looking to have this whole chain of effects happen on load or when the DOM is ready (instead of on hover). And by blur, I mean a gaussian-type of blur - possibly using Pixastic (?) I am really new at this, so please be gentle :) Thank you.

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  • How to load photoshop action with JavaScript?

    - by Elena
    Hello! How do I load photoshop's action using its javascript scripting language? Mostly curious in this action steps: Add Noise Distribution: gaussian Percent: 2% With Monochromatic Texturizer Texture Type: Canvas Scaling: 100 Relief: 3 Without Invert Texture Light Direction: Top Left

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  • Convolve a column vector

    - by Geoff
    This is an OpenCV2 question. I have a matrix: cv::Mat_<Point3f> points; representing some space curve. I want to smooth it (using, for example a Gaussian kernel). I have tried using: cv::Mat_<Point3f> result; cv::GaussianBlur(points, result, cv::Size(4 * sigma, 1), sigma, sigma, cv::BORDER_WRAP); But I get the error: Assertion failed (columnBorderType != BORDER_WRAP)

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  • Evaluating and graphing functions in Matlab

    - by thiol3
    New to programming, I am trying to graph the following Gaussian function in Matlab (should graph in 3 dimensions) but am making some mistakes somewhere. What is wrong? sigma = 1 for i = 1:20 for j = 1:20 z(i,j) = (1/(2*pi*sigma^2))*exp(-(i^2+j^2)/(2*sigma^2)); end end surf(z)

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  • Analyzing data for noisy arrays

    - by jimbo
    Using MATLAB I filtered a very noisy m x n array with a low-pass Gaussian filter, cleaned it up pretty well but still not well enough to analyze my data. What would the next step be? I'm thinking that signal enhancement, but am not sure how to go about this.

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  • Optimum number of threads while multitasking

    - by Gun Deniz
    I know similar questions have been asked but I think my case is a little bit diffrent. Let's say I have a computer with 8 cores and infinite memory with a Linux OS. I have a calculation software called Gaussian that can take advantage of multithreading. So I set its thread count to 8 for a single calculation for maximum speed. However I really can't decide what to do when I need to do run for instance 8 calculations simultaneously. In that case should I set the thread count to 1(total 8 threads spawned in 8 processes) or keep it 8(total 64 threads spawned in 8 processes) for each job? Does it really matter much? A related question is does the OS automatically does the core-parking to diffrent cores for each thread?

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  • Inverse Logistic Function / Reverse Sigmoid Function

    - by Chanq
    I am currently coding up a fuzzy logic library in java. I have found the equations for all the standard functions - Grade, inverseGrade, Triangle, Trapezoid, Gaussian. However, I can't find the inverse of the sigmoid/ logistic function. The way I have written the logistic function is java is : //f(x) = 1/(1+e(-x)) public double logistic(double x){ return (1/(1+(Math.exp(-x))); } But I can't work out or find the inverse anywhere. My algebraic/calculus abilities are fairly limited, hence why I haven't been able to work out the inverse of the function. Any hints or pointers would be a big help. Thanks

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  • Generate n-dimensional random numbers in Python

    - by Magsol
    I'm trying to generate random numbers from a gaussian distribution. Python has the very useful random.gauss() method, but this is only a one-dimensional random variable. How could I programmatically generate random numbers from this distribution in n-dimensions? For example, in two dimensions, the return value of this method is essentially distance from the mean, so I would still need (x,y) coordinates to determine an actual data point. I suppose I could generate two more random numbers, but I'm not sure how to set up the constraints. I appreciate any insights. Thanks!

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  • Problem with averaging corrupted images to eliminate the noise in MATLAB

    - by Mertie Pertie
    I want to average some .jpg images which are corrupted by zero-mean Gaussian additive noise. After searching around, I figured out to add the image matrices and divide the sum by the number of matrices. However, the resultant image is totally black. Normally when the number of image increases then the resultant image gets better. But when I use more images it gets darker. I am using 800x600 black and white .jpg images. Here is the script I used: image1 = imread ('PIC1.jpg'); image2 = imread ('PIC2.jpg'); image3 = imread ('PIC3.jpg'); image4 = imread ('PIC4.jpg'); sum = image1 + image2 + image3 + image4; av = sum / 4; imshow(av);

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  • Averaging corrupted images to eliminate the noise

    - by Mertie Pertie
    Hi all As you can get it from the title, I want to average some .jpg images which are corrupted by zero-mean Gaussian additive. After searching over internet, I figured out to add image matrices and divide the sum by the # of matrices. However the resultant image is totally black. Normally when the number of image increases then the resultant image gets better. But When I use more images it gets darker. I am using 800x600 black and white images with .jpg ext Here is the script I used; image1 = imread ('PIC1.jpg'); image2 = imread ('PIC2.jpg'); image3 = imread ('PIC3.jpg'); image4 = imread ('PIC4.jpg'); sum = image1 + image2 + image3 + image4; av = sum / 4; imshow(av); Thanks in advance

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  • Averaging corrupted images to eliminate the noise in Matlab

    - by Mertie Pertie
    Hi all As you can get it from the title, I want to average some .jpg images which are corrupted by zero-mean Gaussian additive. After searching over internet, I figured out to add image matrices and divide the sum by the # of matrices. However the resultant image is totally black. Normally when the number of image increases then the resultant image gets better. But When I use more images it gets darker. I am using 800x600 black and white images with .jpg ext Here is the script I used; image1 = imread ('PIC1.jpg'); image2 = imread ('PIC2.jpg'); image3 = imread ('PIC3.jpg'); image4 = imread ('PIC4.jpg'); sum = image1 + image2 + image3 + image4; av = sum / 4; imshow(av); Thanks in advance

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  • Easy framework for OpenGL Shaders in C/C++

    - by Nils
    I just wanted to try out some shaders on a flat image. Turns out that writing a C program, which just takes a picture as a texture and applies, let's say a gaussian blur, as a fragment shader on it is not that easy: You have to initialize OpenGL which are like 100 lines of code, then understanding the GLBuffers, etc.. Also to communicate with the windowing system one has to use GLUT which is another framework.. Turns out that Nvidia's Fx composer is nice to play with shaders.. But I still would like to have a simple C or C++ program which just applies a given fragment shader to an image and displays the result. Does anybody have an example or is there a framework?

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  • Graphing special functions in Matlab (2D Bessel)

    - by favala
    I'm trying to essentially get something like this where I can see clear ripples at the base but otherwise it's like a Gaussian: This is kind of unsatisfactory because the ripples aren't very noticeable, it has a very gritty quality that obscures the image a bit, and if you move the graph so that it's just in 2D (so it looks like a circle) I'm not even sure if it's quite like how it should be (the concentric circles seem to be more evenly spaced in the real thing). So, is there a better way to do this? a = 2*pi; [X Y] = meshgrid(-1:0.01:1,-1:0.01:1); R = sqrt(X.^2+Y.^2); f = (2*besselj(1,a*R(:))./R(:)).^2; mesh(X,Y,reshape(f,size(X))); axis vis3d;

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  • vectorizing loops in Matlab - performance issues

    - by Gacek
    This question is related to these two: http://stackoverflow.com/questions/2867901/introduction-to-vectorizing-in-matlab-any-good-tutorials http://stackoverflow.com/questions/2561617/filter-that-uses-elements-from-two-arrays-at-the-same-time Basing on the tutorials I read, I was trying to vectorize some procedure that takes really a lot of time. I've rewritten this: function B = bfltGray(A,w,sigma_r) dim = size(A); B = zeros(dim); for i = 1:dim(1) for j = 1:dim(2) % Extract local region. iMin = max(i-w,1); iMax = min(i+w,dim(1)); jMin = max(j-w,1); jMax = min(j+w,dim(2)); I = A(iMin:iMax,jMin:jMax); % Compute Gaussian intensity weights. F = exp(-0.5*(abs(I-A(i,j))/sigma_r).^2); B(i,j) = sum(F(:).*I(:))/sum(F(:)); end end into this: function B = rngVect(A, w, sigma) W = 2*w+1; I = padarray(A, [w,w],'symmetric'); I = im2col(I, [W,W]); H = exp(-0.5*(abs(I-repmat(A(:)', size(I,1),1))/sigma).^2); B = reshape(sum(H.*I,1)./sum(H,1), size(A, 1), []); But this version seems to be as slow as the first one, but in addition it uses a lot of memory and sometimes causes memory problems. I suppose I've made something wrong. Probably some logic mistake regarding vectorizing. Well, in fact I'm not surprised - this method creates really big matrices and probably the computations are proportionally longer. I have also tried to write it using nlfilter (similar to the second solution given by Jonas) but it seems to be hard since I use Matlab 6.5 (R13) (there are no sophisticated function handles available). So once again, I'm asking not for ready solution, but for some ideas that would help me to solve this in reasonable time. Maybe you will point me what I did wrong.

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  • Using Matlab to find maxima for data with a lot of noise

    - by jimbo
    I have noisy data set with three peaks in Matlab and want to do some image processing on it. The peaks are about 5-9 pixels wide at the base, in a 50 x 50 array. How do I locate the peaks? Matlab is very new to me. Here is what I have so far... For my original image, let's call it "array", I tried J = fspecial('gaussian',[5 5], 1.5); C = imfilter(array, J) peaks = imregionalmax(C); but there is still some noise along the baseline between the peaks so I end up getting a ton of local max that are really just noise values. (I tried playing with the size of the filter, but that didn't help.) I also tried peaks = imextendedmax(C,threshold); where the threshold was determined visually... which works but is definitely not a good way to do it since it's not that robust obviously. So, how do I locate these peaks in a robust way? Thanks.

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  • Setting pixel values in Nvidia NPP ImageCPU objects?

    - by solvingPuzzles
    In the Nvidia Performance Primitives (NPP) image processing examples in the CUDA SDK distribution, images are typically stored on the CPU as ImageCPU objects, and images are stored on the GPU as ImageNPP objects. boxFilterNPP.cpp is an example from the CUDA SDK that uses these ImageCPU and ImageNPP objects. When using a filter (convolution) function like nppiFilter, it makes sense to define a filter as an ImageCPU object. However, I see no clear way setting the values of an ImageCPU object. npp::ImageCPU_32f_C1 hostKernel(3,3); //allocate space for 3x3 convolution kernel //want to set hostKernel to [-1 0 1; -1 0 1; -1 0 1] hostKernel[0][0] = -1; //this doesn't compile hostKernel(0,0) = -1; //this doesn't compile hostKernel.at(0,0) = -1; //this doesn't compile How can I manually put values into an ImageCPU object? Notes: I didn't actually use nppiFilter in the code snippet; I'm just mentioning nppiFilter as a motivating example for writing values into an ImageCPU object. The boxFilterNPP.cpp example doesn't involve writing directly to an ImageCPU object, because nppiFilterBox is a special case of nppiFilter that uses a built-in gaussian smoothing filter (probably something like [1 1 1; 1 1 1; 1 1 1]).

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  • R glm standard error estimate differences to SAS PROC GENMOD

    - by Michelle
    I am converting a SAS PROC GENMOD example into R, using glm in R. The SAS code was: proc genmod data=data0 namelen=30; model boxcoxy=boxcoxxy ~ AGEGRP4 + AGEGRP5 + AGEGRP6 + AGEGRP7 + AGEGRP8 + RACE1 + RACE3 + WEEKEND + SEQ/dist=normal; FREQ REPLICATE_VAR; run; My R code is: parmsg2 <- glm(boxcoxxy ~ AGEGRP4 + AGEGRP5 + AGEGRP6 + AGEGRP7 + AGEGRP8 + RACE1 + RACE3 + WEEKEND + SEQ , data=data0, family=gaussian, weights = REPLICATE_VAR) When I use summary(parmsg2) I get the same coefficient estimates as in SAS, but my standard errors are wildly different. The summary output from SAS is: Name df Estimate StdErr LowerWaldCL UpperWaldCL ChiSq ProbChiSq Intercept 1 6.5007436 .00078884 6.4991975 6.5022897 67911982 0 agegrp4 1 .64607262 .00105425 .64400633 .64813891 375556.79 0 agegrp5 1 .4191395 .00089722 .41738099 .42089802 218233.76 0 agegrp6 1 -.22518765 .00083118 -.22681672 -.22355857 73401.113 0 agegrp7 1 -1.7445189 .00087569 -1.7462352 -1.7428026 3968762.2 0 agegrp8 1 -2.2908855 .00109766 -2.2930369 -2.2887342 4355849.4 0 race1 1 -.13454883 .00080672 -.13612997 -.13296769 27817.29 0 race3 1 -.20607036 .00070966 -.20746127 -.20467944 84319.131 0 weekend 1 .0327884 .00044731 .0319117 .03366511 5373.1931 0 seq2 1 -.47509583 .00047337 -.47602363 -.47416804 1007291.3 0 Scale 1 2.9328613 .00015586 2.9325559 2.9331668 -127 The summary output from R is: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.50074 0.10354 62.785 < 2e-16 AGEGRP4 0.64607 0.13838 4.669 3.07e-06 AGEGRP5 0.41914 0.11776 3.559 0.000374 AGEGRP6 -0.22519 0.10910 -2.064 0.039031 AGEGRP7 -1.74452 0.11494 -15.178 < 2e-16 AGEGRP8 -2.29089 0.14407 -15.901 < 2e-16 RACE1 -0.13455 0.10589 -1.271 0.203865 RACE3 -0.20607 0.09315 -2.212 0.026967 WEEKEND 0.03279 0.05871 0.558 0.576535 SEQ -0.47510 0.06213 -7.646 2.25e-14 The importance of the difference in the standard errors is that the SAS coefficients are all statistically significant, but the RACE1 and WEEKEND coefficients in the R output are not. I have found a formula to calculate the Wald confidence intervals in R, but this is pointless given the difference in the standard errors, as I will not get the same results. Apparently SAS uses a ridge-stabilized Newton-Raphson algorithm for its estimates, which are ML. The information I read about the glm function in R is that the results should be equivalent to ML. What can I do to change my estimation procedure in R so that I get the equivalent coefficents and standard error estimates that were produced in SAS? To update, thanks to Spacedman's answer, I used weights because the data are from individuals in a dietary survey, and REPLICATE_VAR is a balanced repeated replication weight, that is an integer (and quite large, in the order of 1000s or 10000s). The website that describes the weight is here. I don't know why the FREQ rather than the WEIGHT command was used in SAS. I will now test by expanding the number of observations using REPLICATE_VAR and rerunning the analysis.

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