Back to 2007..... The California drone story began this year and, while others researchers used 3D techniques to show how (especially the 'Raj' drone) had possibly faked shadows, I thought that another good technique could be to try to discriminate in an irrefutable way Computer Generated Images from real Photographies.
This technique, theoretically, could serves the whole Ufology as well, that's why I thought that take again my study (began almost two years ago on OMF
) and try to finish it could be very important.
Everyone who have any Java knowledge is welcome to help me.
In order to try to accomplish this, I began to search if any technical paper was already made about this. With a simple Google search, an interesting paper popped up:Detecting Differences between CGI and Photographs
This paper was presented by their three authors, Jie Wu, Markad V. Kamath and Skip Poehlman (from the Department of Computing and Software and Department of Medicine, MacMaster University, Hamilton, Ontario, Canada) at the 24th IASTED International Conference of Signal Processing, Pattern Recognition and Applications, Innsbruck Austria, in February 15/17 2006.
Here what the paper says (my preliminary comments are in blue
With the development of computer graphic rendering software and the appearance of more and more photorealistic pictures, the need for automatically distinguish Computer Generated Images from real photographs has become of particular interest to criminal and forensic science investigators.
Previous studies have been based on wavelet statistics, while in our study we examined several visual features derived from colour, edge, saturation and texture feature extrated with the Gabor filter.
Based on the feature extraction, we examined these commonly-used classifiers: non-linear SVM, Weighted k
-nearest neighbors and Fuzzy k
-nearest neighbors with 1.044 Computer Generated Images and 1.114 photographs (It's their control image basis)
downloaded from open sources.
Finally, we report on the comparative analysis of the results of these automatic classifications: Gabor filter based feature shows very promising results (99% for photo and 91.5% for CGI) while visual features show some abilities to perform differentiation.