Assistive Technology and OpenCV

Assistive technology (AT) or Adaptive technology refers to technology or devices that assist people with disabilities.  AT provides for a greater degree of freedom and independence by enabling disabled people to perform tasks that they were formerly unable to perform.

Examples of assistive technology include large computer keyboards for visually impaired, Braille buttons in elevators, hearing aids etc. People with learning abilities, for e.g dyslexics find text –to –speech (TTS) technology is useful.

In the context of Assistive technology, OpenCV can be used in multiple ways. OpenCV (Open Source Computer Vision) is a set of powerful APIs that can perform real time computer image processing. OpenCV is being used by many organizations in complex application like biometrics for recognizing fingerprints, face, to medical imaging for detection of tumors and cancerous cells. Applications of OpenCV have also been developed in office security for the detection of intruders to terrain mapping by spy planes and drones. OpenCV is truly a powerful tool for performing complex image processing operation.

One such application of OpenCV is the area of gesture detection and recognition. A successful implementation of gesture detection and recognition can have significant implications. It can be used to interpret sign language, the language of the deaf, and those with motor and speech disabilities.

Clearly the ability to recognize gestures is no easy task. The software has to be trained to initially recognize different gestures of sign language. An image processing tool that can recognize the symbols of sign language will be a boon to those with severe motor disabilities that they can use the keyboard for typing.

Imagine somebody who has motor disabilities being able to use the PC for browsing, accessing social networks all through sign language. Such a tool will really allow such people to live life in a much more regular way.  Assuming the software is powerful enough the disable person will also be able to create documents through sign language.

If the power of OpenCV can be harnessed for gesture detection and interpretation the applications can be manifold. One such application would be sign language which would be a boon for many disabled people.

Added as an afterthought
While the post is about using OpenCV for recognition of sign language to empower disabled people, I think gesture recognition would be really welcome for everybody. Imagine being able to browse websites using gestures, scrolling up/down, going back/forward and pointing and clicking on links by simply waving the hand appropriately. If we could use gestures to flip through TV channels by snapping our fingers or waving left to right or right to left, that would be really cool. The possibilities are endless…

Do read my futuristic short story – The Anomaly for an interesting application of the above technology

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Computer Vision: Getting started with OpenCV

 OpenCV (Open Source Computer Vision) is a library of APIs aimed at enabling real time computer vision. OpenCV had Intel as  the champion during its early developmental stages from 1999 to its first formal release in 2006. This post looks at the steps    involved in installing OpenCV on your Linux machine and getting started with some simple programs. For the steps for installing OpenCV in Windows look at my post “Installing and using OpenCV with Visual Studio 2010 express

As a first step download the tarball  OpenCV-2.3.1a.tar.bz2 from the link below to directory


Unzip and and untar the bzipped file using

$ tar jxvf OpenCV-2.3.1a.tar.bz2


$ cd OpenCV-2.3.1

You have to run cmake to configure the directories before running make. I personally found it

easier to run the cmake wizard using

$ cmake -i

Follow through all the prompts that the cmake wizard gives and make appropriate choices.

Once this complete


$ make

Login as root

$ su – root

password: *******


$ make install

This should install all the appropriate files and libraries in /usr/local/lib

Now assuming that everything is fine you should be good to go.

Start Eclipse, open a new C project.

Under Project->Properties->Settings->GCC Compiler ->Directories include the following 2 include paths




Project->Properties->Settings->GCC Linker ->Libraries in the library search path

include /usr/local/lib

Under libraries include the following

opencv_highgui , opencv_core , opencv_imgproc , opencv_highgui, opencv_ml, opencv_video, opencv_features2d, opencv_calib3d

, opencv_objdetect, opencv_contrib, opencv_legacy, opencv_flann

(To get the list of libraries you could also run the following command)

pkg-config –libs /usr/local/lib/pkgconfig/opencv.pc

-L/usr/local/lib -lopencv_core -lopencv_imgproc -lopencv_highgui -lopencv_ml -lopencv_video -lopencv_features2d -lopencv_calib3d -lopencv_objdetect -lopencv_contrib -lopencv_legacy -lopencv_flann

Now you are ready to create your first OpenCV program

The one below will convert an image to test.png

#include “highgui.h”

int main( int argc, char** argv ) {

IplImage* img = cvLoadImage( argv[1],1);

cvSaveImage( “test.png”, img, 0);

cvReleaseImage( &img );

return 0;


If you get a runtime error cannot find shared library

“ cannot open shared object file: No such file or directory”

then you need to ensure that the linker knows the paths of the libraries.

The commands are as follows

$vi /etc/



and save file

Now execute

$ldconfig /etc/

You can check if everything is fine by running

$[root@localhost mycode]# ldconfig -v | grep open

ldconfig: /etc/ duplicate hwcap 0 nosegneg -> -> -> -> ->


Now re-build the code and everything should be fine.

Here’s a second program to run various transformations to an image

IplImage* img = cvLoadImage( argv[1],1);

// create a window. Window name is determined by a supplied argument

cvNamedWindow( argv[1], CV_WINDOW_AUTOSIZE );

// Apply Gaussian smooth

//cvSmooth( img, img, CV_GAUSSIAN, 9, 9, 0, 0 );

cvErode (img,img,NULL,2);

// Display an image inside and window.

cvShowImage( argv[1], img );

//Save image

cvSaveImage( “/home/ganesh/Desktop/baby2.png“, img, 0);


There are many samples also downloaded along with the installation. You can try them out. I found the facedetect.cpp sample interesting. It is based on Haar cacades and works really well. Compile facedetect.cpp under samples/c

Check it out. Including facedetect.cpp detecting my face in real time …

Have fun with OpenCV.

Get going! Get hooked!

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