Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
