Automated shape annotation for illicit tablet preparations: a contour angle based classification from digital images.

In order to facilitate forensic intelligence efforts in managing large collections of physical feature data pertaining to illicit tablets, we have developed an automated shape classification method. This approach performs categorical shape annotation for the domain of illicit tablets. It is invariant to scale, rotation and translation and operates on digital images of seized tablets. The approach employs two processing levels. The first (coarse) level is being based on comparing the contour curvature space of tablet pairs. The second (fine) level is a rule based approach, implemented as a classification tree, that exploits characteristic similarities of shape categories. Annotation is demonstrated over a collection of 169 tablets selected for their diverse shapes with an accuracy of 97.6% when 19 shape categories are defined.

Using anisotropic diffusion for efficient extraction of sensor noise in camera identification.

Each digital camera has an intrinsic fingerprint that is unique to each camera. This device fingerprint can be extracted from an image and can be compared with a reference device fingerprint to determine the device origin. The complexity of the filters proposed to accomplish this is increasing. In this note, we use a relatively simple algorithm to extract the sensor noise from images. It has the advantages of being easy to implement and parallelize, and working faster than the wavelet filter that is common for this application. In addition, we compare the performance with a simple median filter and assess whether a previously proposed fingerprint enhancement technique improves results. Experiments are performed on approximately 7500 images originating from 69 cameras, and the results are compared with this often used wavelet filter. Despite the simplicity of the proposed method, the performance exceeds the common wavelet filter and reduces the time needed for the extraction.