I decided to use the method I knew best, Cross Correlation. The idea is to cross correlate the template of the seat (i.e., an image of a single seat) with every pixel in the airline layout image. The template (coordinate origin is its center) is moved over to a particular pixel in the image. The cross correlation coefficient is calculated and the value is used as the intensity of a new image. This is repeated by moving the template to every pixel in the airline layout image. The pixels for which the template matches perfectly with the airline layout image, will have the correlation coefficient close to 1.

Above: A hard to see template of the seat.

The results of the cross correlation is shown below (click to view the bigger image.)

The bright spots in this image are the points with the highest correlation. It then becomes a simple process of segmenting the high intensity pixel.

To perform these operations, my natural choice was Matlab and its Image processing toolbox.

im = imread('airline_seating.jpg');

im = rgb2gray(im);

im_template = imread('template.jpg');

im_template= rgb2gray(im_template);

C = normxcorr2(im_template, im);

C1 = C>0.7;

stat = regionprops(C1);

noofseats = size(stat,1);

disp(['Number of seats = ',num2str(noofseats)]);

In the first 4 lines of code, we read the airline layout image and the template image. To obtain the correlation image, I did not have to write my own correlation function instead Matlab has one already ready to be used. This function, normxcorr2 needs the airline layout and the template matrix. Once the correlation image is obtained, we segment it based on the logic that any pixel with value more than 0.7 is considered as pixels corresponding to the center of seat. Since the center of the seat did not segment as a single pixel, I could not count the number of pixels as the number of seats. Instead I calculated the number of regions using regionprops and store it as a structure. The number of elements of the structure is the number of seats.