21-05-2012, 10:33 AM
Detecting and Disabling Digital Cameras Using Image Processing
Detecting and Disabling Digital Cameras.doc (Size: 530.5 KB / Downloads: 187)
Abstract
- In this paper we discuss about the device capable of detecting and disabling digital cameras. The system locates the camera and then neutralizes it. Every digital camera has an image sensor known as a CCD, which is retro reflective and sends light back directly to its original source at the same angle. The device shines infrared LED light, which is invisible to the human eye, at a distance of about 20 feet.
1.1 Introduction
The system locates the camera, and then neutralizes it. Every digital camera has an image sensor known as a CCD, which is retro reflective and sends light backing directly to its original source at the same angle. Using this property and algorithms of image processing the camera is detected. Once identified, the device would beam an invisible infrared laser into the camera's lens, in effect overexposing the photo and rendering it useless. Low levels of energy neutralize cameras but are neither a health danger to operators nor a physical risk to cameras.
Camera Detection
3.1 Scanning
The entire area to be protected is scanned by using infrared light. Infrared LED is used for producing them. The circuitry required for producing infrared beams are simple and cheap in nature. The scanning beams sweep through the vertical and horizontal direction of the area, to ensure no camera escapes from the device.
3.2 Wavelength
The infrared beam used here has the centre wavelength of 800-900 nm. This wavelength falls under the near infrared classification. The reason for choosing near infrared are the molar absorptivity in the near IR region is typically quite small and it typically penetrate much farther into a sample than mid infrared radiation so that the retro reflections would be of high intensity. The generation of NIR is achieved using IR LED. Due to the retro reflective property of the ccd the part of the light gets retro reflected by it and the infrared beam does not have any effect on the other objects hit the area other than the ccd.
Test Image Capture
The area being scanned by the infrared beams are simultaneously recorded. The preprocessing image being acquired is called as the test image. It forms the basis of the further steps of the process. The test image is obtained by use of high resolution camcorders. The response of the test image capturer should be very fast in order to sense even a small change of position of the camera. The camcorder should have a wide angle of capture so that it can capture a wide test image to cover the entire area. The retro reflected beams also have the same properties of the near IR. Therefore, they are visible to the camcorders and invisible to human eyes.
5. Image Processing
It is most important aspect of the device. The raw image for image processing is the test image being streamed lively. The detection of the camera is accomplished in this stage only. The image processing for detection can be done in two steps.
We have coded an algorithm in Matlab software to perform the image processing operation.
5.1 Detection Of Retro reflecting Area
The camera is detected by the differentiation of the retro reflecting area from the rest of the test image. The camera lens also appears red in colour and the rest part appears normal. This key point is used for differentiation.
Thresholding
During the thresholding process, individual pixels in an image are marked as “object” pixels if their value is greater than some threshold value (assuming an object to be brighter than the background) and as “background” pixels
otherwise. The separate RGB Components are determined and a threshold value is set.
1. An initial threshold (T) is choosen, this can be done randomly or according to any other method desired.
2. The image is segmented into object and background pixels as described above, creating two sets:
1. G1 = {f(m,n):f(m,n)>T} (object pixels)
2. G2 = {f(m,n):f(m,n)T} (background pixels) (note, f(m,n) is the value of the pixel located in the mth column, nth row)
3. The average of each set is computed.
1. m1 = average value of G1
2. m2 = average value of G2
4. A new threshold is created that is the average of m1 and m2
1. T’ = (m1 + m2)/2
5. Go back to step two, now using the new threshold computed in step four, keep repeating until the new threshold matches the one before it (i.e. until convergence has been reached).
This iterative algorithm is a special one-dimensional case of the k-means clustering algorithm, which has been proven to converge at a local minimum—meaning that a different initial threshold may give a different final result.
5.1.2 Color Segmentation
We need to detect only the red infrared part of the image. This is done by means of colour segmentation. The RGB Components are filtered separately and finally the red area is detected. The following algorithm was used for the purpose
img = imread('sample.jpg');
%imshow(img)
img = imfilter(img,ones(3,3)/9);
%img = imresize(img,0.1);
% Decompose to separate colour components
xr = img(:,:,1);
[N,M] = size(img);
m=4;
w = 1/m;
F = fftshift(fft(double(img)));
for i=1:N
for j=1:M
r2=(i-round(N/2))^2+(j-round(N/2))^2;
if (r2>round((N/2*w)^2)) F(i,j)=0; end;
end;
end;
Idown=real(ifft2(fftshift(F)));
5.2 Lens shape detection using Hough Transform
The simplest case of Hough transform is the linear transform for detecting straight lines. In the image space, the straight line can be described as y = mx + b and can be graphically plotted for each pair of image points (x,y). In the Hough transform, a main idea is to consider the characteristics of the straight line not as image points x or y, but in terms of its parameters, here the slope parameter m and the intercept parameter b. Based on that fact, the straight line y = mx + b can be represented as a point (b, m) in the parameter space. However, one faces the problem that vertical lines give rise to unbounded values of the parameters m and b. For computational reasons, it is therefore better to parameterize the lines in the Hough transform with two other parameters, commonly referred to as r and θ .
Conclusion
The device explained above can prove to be essential to all environments like theatres, lockers, private areas, anti-espionage systems, defense secrecy etc. This technology if developed to a good extent it would be of great help prevent piracy, maintain national secrecy in the future.