Rank: 31 based on 1267 downloads (last 30 days) and 34 files submitted
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Nikolay S.

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http://vision.technion.ac.il/~kolian1/
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32.76499938964844, 35.04999923706055

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B.Sc in E.E. ( Image&signal processing, computer networks, communication systems),
M.Sc in E.E. - Thesis on "The importance of Phase in Image Processing".


 

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Files Posted by Nikolay S. View all
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(last 30 days)
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26 Oct 2014 Screenshot folders sub-folders Returns cell array of folder names located under input list (cell array) of folders. Non recursive! Author: Nikolay S. directories, folders, dir, ls, subdir, path 22 2
05 Sep 2014 Screenshot Local binary patterns Calculates image LBP (Local binary patterns). Author: Nikolay S. lbp, image processing, pattern, local analysis, feature detection, segmentation 379 46
  • 4.2
4.2 | 21 ratings
03 Sep 2014 Screenshot embedVideo2Video.zip This function allows embedding one video into a another Main video, with many options. Author: Nikolay S. video processing, video, photomontage, videomontage, montage 15 0
28 Aug 2014 Screenshot Plot on an image- addMarkerLines2Img Plot a curve line/points on an image Author: Nikolay S. contour, roi, mask, poly2mask, registration, image processing 4 0
28 Aug 2014 Screenshot implot- image plot Add markers to an image Matlab "Plot" style Author: Nikolay S. image processing, image annotation, plot, image editing, video processing 7 0
Comments and Ratings by Nikolay S. View all
Updated File Comments Rating
11 Dec 2014 Local binary patterns Calculates image LBP (Local binary patterns). Author: Nikolay S.

Hi sinanda.
I'll briefly reply here. if you need additional explanations, please email me. I'm not familiar with the concept circular bi-linear interpolation. Regular bi-linear interpolation is basically about calculating value lying between nearest neighbors (interpolation ) values, via weighted average based on their distances to the desired value position (bi-linear). See First Order Hold as well.
In case of LBP, having a central point, and angle, and a radius, usually results in a point "falling" between pixels. This demands calculation of this value via interpolation. bi-linear is the easiest one. Bi-cubic for example is another, more expensive option.
Now again- in LBP image, each pixel value is calculated via sum of neighboring pixels (of when speaking of the whole image via it's shifted versions). As the shift is not always an integer value it demands interpolation. that's about all. See matlab imresize function, or any bi-linear based image zoom method for visualization.
Hope I've made things a bit more clear.

03 Nov 2014 Local binary patterns Calculates image LBP (Local binary patterns). Author: Nikolay S.

Hi Kathrin.
Using 32 neighbors to an image of [nxmxk] dimensions, results in storing an nxmx(kx32) matrix to the memory. Moreover, usage of 32 elements means storing LBP data in uint32, rather then uint8 (for up to 8 neighbors), which means *4 more memory. Altogether, the usage scenario you propose can indeed cause "out of memory" problem.
Currently I do not have a version with fever memory demands, so the only advice I can give you, it to devide the image to patches/windows, and applying the LBP to each such part in turn. You can then concatenate the sub-windows LBP into a single LBP. You can use my image concatenate function for this.
Regarding your grade (3/5)- I find it a bit insulting. Indeed, my implementation fails to deal with the hush scenario of 32 neighbors, especially if the image investigated is of large dimensions.
But you must understand, that some methods are limited, and you cannot apply them to any kind of data expecting it to work. perhaps there is an optimization, besides what I've proposed beforehand, but I have not figured one out so far. You are welcome to propose a better implementation, so I will be able to use and grade it.
Best regards.

28 Oct 2014 Local binary patterns Calculates image LBP (Local binary patterns). Author: Nikolay S.

Hi Amruta.
Assuming I got your question correctly, I'm using bi-linear interpolation, for neighbor elements falling between pixels.

26 Oct 2014 folders sub-folders Returns cell array of folder names located under input list (cell array) of folders. Non recursive! Author: Nikolay S.

Hi Pawel.
You are 100% correct. I've exported the sub-function "folderFullPath", and forgot to add the file to this submission. Please get the updated version, once it is uploaded.
In case of problems, do not hesitate to contact me.
Best regards,
Nikolay

22 Oct 2014 Local binary patterns Calculates image LBP (Local binary patterns). Author: Nikolay S.

Hi hamad mahmood.
I have not used LBP for face detection, but the topic is highly researched. Here is one of most referenced papers: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1717463&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1717463
I've also participated in a project where Vaioa Jones OpenCV LBP implementtation was used: http://docs.opencv.org/modules/contrib/doc/facerec/facerec_tutorial.html, http://docs.opencv.org/modules/contrib/doc/facerec/facerec_api.html#Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors, int grid_x, int grid_y, double threshold). You should give it a glance too.
Best regards,
Nikolay.

Comments and Ratings on Nikolay S.'s Files View all
Updated File Comment by Comments Rating
11 Dec 2014 Local binary patterns Calculates image LBP (Local binary patterns). Author: Nikolay S. Nikolay S.

Hi sinanda.
I'll briefly reply here. if you need additional explanations, please email me. I'm not familiar with the concept circular bi-linear interpolation. Regular bi-linear interpolation is basically about calculating value lying between nearest neighbors (interpolation ) values, via weighted average based on their distances to the desired value position (bi-linear). See First Order Hold as well.
In case of LBP, having a central point, and angle, and a radius, usually results in a point "falling" between pixels. This demands calculation of this value via interpolation. bi-linear is the easiest one. Bi-cubic for example is another, more expensive option.
Now again- in LBP image, each pixel value is calculated via sum of neighboring pixels (of when speaking of the whole image via it's shifted versions). As the shift is not always an integer value it demands interpolation. that's about all. See matlab imresize function, or any bi-linear based image zoom method for visualization.
Hope I've made things a bit more clear.

11 Dec 2014 Local binary patterns Calculates image LBP (Local binary patterns). Author: Nikolay S. sunanda

Hi Nikolay ..
I am very much new to LBP.
I have successfully calculated LBP.
Can you tell me how to calculate circular bi-linear interpolation. I am confused about radius of circular LBP and how circular neighbourhoods are calculated using bilinear interpolation.

Pls help me. I read Ojala's paper , but confused about uniform LBP and circular neighbourhood.
sc312009@gmail.com

04 Nov 2014 Local binary patterns Calculates image LBP (Local binary patterns). Author: Nikolay S. Anju Panicker

Never mind. I fixed it. Thanks for the code.

04 Nov 2014 Active Contours implementation & test platform GUI Implementation and demonstration of several active contours segmentation methods. Author: Nikolay S. Kwstas Tranos

Can anyone help me to implement the equation (27) in Lankton's paper .I compure the a and r for all the contours but i can not understand how to take the max and min in equation (27).If i use matlabs max i take the bigger elements between the two matrices .Is this wright?

03 Nov 2014 Local binary patterns Calculates image LBP (Local binary patterns). Author: Nikolay S. Nikolay S.

Hi Kathrin.
Using 32 neighbors to an image of [nxmxk] dimensions, results in storing an nxmx(kx32) matrix to the memory. Moreover, usage of 32 elements means storing LBP data in uint32, rather then uint8 (for up to 8 neighbors), which means *4 more memory. Altogether, the usage scenario you propose can indeed cause "out of memory" problem.
Currently I do not have a version with fever memory demands, so the only advice I can give you, it to devide the image to patches/windows, and applying the LBP to each such part in turn. You can then concatenate the sub-windows LBP into a single LBP. You can use my image concatenate function for this.
Regarding your grade (3/5)- I find it a bit insulting. Indeed, my implementation fails to deal with the hush scenario of 32 neighbors, especially if the image investigated is of large dimensions.
But you must understand, that some methods are limited, and you cannot apply them to any kind of data expecting it to work. perhaps there is an optimization, besides what I've proposed beforehand, but I have not figured one out so far. You are welcome to propose a better implementation, so I will be able to use and grade it.
Best regards.

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