pattern-recognition

I'm trying to read a image from a electrocardiography and detect each one of the main waves in it (P wave, QRS complex and T wave). Now I can read the image and get a vector like (4.2; 4.4; 4.9; 4.7; ...) representative of the values in the electrocardiography, what is half of the problem. I need a algorithm that can walk through this vector and detect when each of this waves start and end. Here is a example of one of its graphs: Would be easy if they always had the same size, but it's not like it works, or if I knew how many waves the ecg would have, but it can vary too. Does anyone have some

Assume that I have a grayscale image. Consider a circular neighborhood window around each pixel. I need to get color histogram of circular neighbourhoods around each pixel. How can I efficiently implement circular neighborhood operations for this problem in MatLab? I don't want to give you everything, but I think this should help you out a lot. Well you can make a circle of ones doing something like h = fspecial('disk',rad); h = h>0; Then you can put that anywhere in a larger matrix doing something like h2 = zeros(N,M); h2(c_offset-rad:c_offset+rad,r_offset-rad:r_offset+rad) = h; Now you have

Let's say I have a number with a recurring pattern, i.e. there exists a string of digits that repeat themselves in order to make the number in question. For example, such a number might be 1234123412341234 , created by repeating the digits 1234 . What I would like to do, is find the pattern that repeats itself to create the number. Therefore, given 1234123412341234 , I would like to compute 1234 (and maybe 4 , to indicate that 1234 is repeated 4 times to create 1234123412341234 ) I know that I could do this: def findPattern(num): num = str(num) for i in range(len(num)): patt = num[:i] if (len

I was trying to build a basic Face Recognition system (PCA-Eigenfaces) using OpenCV 2.2 (from Willow Garage). I understand from many of the previous posts on Face Recognition that there is no standard open source library that can provide all the face recognition for you. Instead, I would like to know if someone has used the functions(and integrated them): icvCalcCovarMatrixEx_8u32fR icvCalcEigenObjects_8u32fR icvEigenProjection_8u32fR et.al in the eigenobjects.cpp to form a face recognition system, because the functions seem to provide much of the required functionality along with cvSvd ? I am

I was wondering if anyone has managed to use the OpenCV implementation of Latent SVM Detector ( http://docs.opencv.org/modules/objdetect/doc/latent_svm.html ) successfully. There is a sample code that shows how to utilize the library but the problem is that the sample code uses a ready-made detector model that was generated using MatLab. Can some one guide me through the steps on how to generate my own detector model? The MATLAB implementation of LatSVM by the authors of the paper has a train script called pascal . There is a README with the tarball explaining its usage: Using the learning

I am using shape context histograms as a feature descriptor to encode silhouette images. To assist with debugging, I would like to view the shape context logpolar bins overlaid on a silhouette image (the sample points taken from the edge image). An example of what it should look like for one of the points is as follows: I know how to display the circles (radial bins), but I am having difficulty in producing the angular bins (lines). Given a set of angles, how can I draw line segments similar to those shown in the example image? Doing this: >> figure >> axes >> hold on >> radius = 1; >> theta =

I Have an object with a set of parameters like: var obj = new {Param1 = 100; Param2 = 212; Param3 = 311; param4 = 11; Param5 = 290;} On the other side i have a list of object: var obj1 = new {Param1 = 1221 ; Param2 = 212 ; Param3 = 311 ; param4 = 11 ; Param5 = 290 ; } var obj3 = new {Param1 = 35 ; Param2 = 11 ; Param3 = 319 ; param4 = 211 ; Param5 = 790 ; } var obj4 = new {Param1 = 126 ; Param2 = 218 ; Param3 = 2 ; param4 = 6 ; Param5 = 190 ; } var obj5 = new {Param1 = 213 ; Param2 = 121 ; Param3 = 61 ; param4 = 11 ; Param5 = 29 ; } var obj7 = new {Param1 = 161 ; Param2 = 21 ; Param3 = 71 ;