Image processing

[By Hany Farid via Scientific American]

This story is a supplement to the feature "Digital Forensics: How Experts Uncover Doctored Images" which was printed in the June 2008 issue ofScientific American.

Lighting
Composite images made of pieces from different photographs can display subtle differences in the lighting conditions under which each person or object was originally photographed. Such discrepancies will often go unnoticed by the naked eye.

Siemens knows that driving is really hard with a phone, PDA, GPS and stereo to worry about, so they've invented a system to read signs for you.

A camera scans the field ahead while an onboard computer searches for patterns. In the case that a speed limit sign is detected, a windshield display will place the new speed limit next to your current rate of travel. The system can also be configured to automatically adjust your cruise control accordingly if you are a "slowpoke".

Look for it in 2008 - after you're too poor from moving violations to ever afford it. – Mark Wilson

 Finger print recognition, the most prominent details extraction method, uses a simple but elegant algorithm.

Following the BMP standard [1], file size, data size and data start, height (in pixels), width (in pixel), depth (bits) and compression were read from the header of the file. An odd behavior was noted. A file may contain extra bits at the end of each line, which is not included in width information. Hence to find offsets it is better to use (size – start) / height to find the actual length of the information of a row.

Another peculiar feature of BMP file is when height is specified in positive figure (which is the most common) the last row of the image is written first and the first row at the end. i.e. an inverted mirror image is stored. Hence to print the first row, it had to be read from (start + ( height -1) row_length )

Further, in order to support both the endiness, a bit reversal operation needed, which was implemented in a macro, instead of a for loop or function to save the additional over head that would have occurred otherwise [2]

Principle Component Analysis

Is an orthogonal linear transformation, that transforms the data to a new system, where it is stacked according the variance, for instance highest variance in the first column and second highest variance in the second column, and so on. PCA is theoretically the optimum transform for a given data in least square terms [1] and fundamental of Face recognition. In pattern recognition it is primarily used to reduce the dimensions in a data set by retaining the characteristics that are prominent. It is essentially used, either directly or indirectly in Face recognition. However Eigenfaces is what encompasses and supersedes it.