[Jans Hendry / EE&IT UGM Indonesia] August 23, 2011

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REAL TIME EDGE DETECTOR of OBJECTS

USING MATLAB

Alright, at this time we’ll talk about how to detect edge of objects in real time video using matlab. In this

article, i’m gonna show you that there is nothing difficult algorithm we have to use. But there is

prerequisites, all articles i wrote related to video processing using matlab (article 1st to 3rd) must have been

read and understood. Because most of syntax i use here, taken from those articles.

Lets take a look at below code:

%% taking camera information caminf = imaqhwinfo; mycam = char(caminf.InstalledAdaptors(end)); mycaminfo = imaqhwinfo(mycam); resolution = char(mycaminfo.DeviceInfo.SupportedFormats(end));

vid = videoinput(mycam, 1, resolution); %%

Piece of program above shows how to acquire webcam information installed in your computer. This

adaptor will be used as object that matlab recognized as peripheral to acquire image frames to process.

Meanwhile, piece below shows you how to set attributs for our webcam.

%% setting camera properties vid.FramesPerTrigger=Inf; set(vid, 'ReturnedColorspace', 'grayscale') vid.FrameGrabInterval=3; framesneeded=300;

We set grayscale because this kind of format will be used as input for processing, rather than rgb. Because

if we set rgb format, we will need 1 more step which is grayscaling images. Also, you have to notice that

framegrabinterval used in this program is the best smallest value. If you have plan to change it smaller, it

makes program will not give best result. In case you dont agree with me, you have right to make any

changes.

Allright, now we are moving to data acquiring and processing.

%% begin acquiring data and processing start(vid) while(vid.FramesAcquired<=framesneeded) data = getsnapshot(vid); % get one picture every frame level=graythresh(data); bw=im2bw(data,level); % bw=medfilt2(bw,[3 3]);

[Jans Hendry / EE&IT UGM Indonesia] August 23, 2011

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You must notice that we use single frame or image in processing. This grayscale image is converted to

binary image because we will threshold its intensity to separate objects from its background. Many

thresholding method we can use instead of one used above. Please make some experiment yourself.

bw=bwareaopen(bw, 45); bw=edge(bw,'roberts'); % please choose other edge filter se=strel('disk',5); % to solid line edge bw=imclose(bw,se); bw=~bw;

I will explain from the upper line:

- Bwareaopen means we are filtering thresholded image by removing objects less than 45 pixels.

- Edge detection using robert method. You can use other method like Susan, Prewitt, Sobel, Canny

or else. Complex method will end up in longer time consuming.

- We make edge line become solid using imclose. This imclose use structure element that defined

by strel command.

- In the last step i change intensity value between edge lines and background. From white color to

black for edge lines.

imshow(bw) title('Object Edge Detection Real Time','fontsize',14); xlabel('Jans Hendry - EE&IT Gadjah Mada University, Indonesia'); flushdata(vid); % to clear memory end stop(vid); % stop process

Ok, above piece is easy to understand. We use imshow because the data is an image in sequences. After

processing data, we must erase it from memory for each. Its important not to burden your memory. For

this purpose, we use flushdata.

%% clearing delete(vid); imaqreset; %%

% originale by jans hendry % Gadjah Mada University, Indonesia %%

Please dont forget to delete object and reset all variabel regarding object by using imaqreset.

[Jans Hendry / EE&IT UGM Indonesia] August 23, 2011

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Here is the result:

After reading this article and probably simulating the codes, i wish you will be able to modify it and make

some powerfull program.

Thats all dude, we’ll continue to discuss about object detection based on its color in next session... CU.

Lets share your knowledge for Indonesia... ☺

~~~ TERIMA KASIH ~~~