For my gesture recognition uni project I have developed simple code that removes background from the image. For it to work there has to be an image of background scene and an image with an object that partially covers same background scene (a hand with a background of a room scene for example).
function [outIm] = makeMask(bg, im, tol) %bg - background image %im - input image %tol - tolerance
[h,w] = size(bg); outIm = false(h, w); for ch = 1:h for cw = 1:w imPix = im(ch,cw); bgPix = bg(ch,cw); if ((bgPix == imPix) || (bgPix > imPix && bgPix <= imPix + tol) || (bgPix < imPix && bgPix >= imPix - tol)) outIm(ch, cw) = 0; else outIm(ch, cw) = 1; end end end end
As You see, code checks every single pixel and decides if it belongs to background scene. All this works well, however I want to ask is there more efficient way to achieve the same?
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You can try vectorizing that code, but that will be most likely destroy any short-circuiting you have in there.
Just by the top of my head, following code should be able to replace your for-loops:
brighterThanBg = (im > bg + tol); darkerThanBg = (im < bg - tol); outIm = (brighterThanBg | darkerThanBg);
That will also check every pixel but it will be faster as MATLAB is quite slow on loops. Also, by inverting your logic (you check for background, split into 3 regions, 2 of which require 2 conditions to be satisfied), while this codes only takes care of 2 regions (pixels too bright to be background, pixels too dark to be background), each only requiring a single condition.
function [outIm] = makeMask2(bg, im, tol) outIm = ((im > bg + tol) | (im < bg - tol)); end
This function does the job so much faster, have a look at these figs:
>> tic; m1 = makeMask(bgg, ig, 20); toc; tic; m2 = makeMask2(bgg, ig, 20); toc; Elapsed time is 0.004686 seconds. Elapsed time is 0.001663 seconds.
>> tic; m1 = makeMask(bgg, ig, 20); toc; tic; m2 = makeMask2(bgg, ig, 20); toc; Elapsed time is 0.004764 seconds. Elapsed time is 0.001655 seconds.
>> tic; m1 = makeMask(bgg, ig, 20); toc; tic; m2 = makeMask2(bgg, ig, 20); toc; Elapsed time is 0.005040 seconds. Elapsed time is 0.001660 seconds.
Thanks for the tip Egon Geerardyn.
I've interested do you have background detection algorithm on an image = object + background w/o having background apriori? I am currently developing such algorithm ( background detection by special histogram processing ). Have you tried to find centroid of an image histogram ( y coordinate )? If somebody have code of computing centroid of an area under 2D discrete function ( histogram ) will appreciate to look at. Thanks.
I am doing project on fruit defect detection can anyone help to subtract background from image if i dont have separate background image and how to extrcat specified color as defected area from fruit...
this is my original image
below is the image after applying median filtering
i want to perform another 2 steps,pls can anyone help in coding?? for step1: background cancellation step2:image segmentation pls answer as soon as possible