Name
Abstract | ||
|---|---|---|
Worldwide, law enforcement agencies are encountering a substantial increase in the number of illicit drug pills being circulated in our society. Identifying the source and manufacturer of these illicit drugs will help deter drug-related crimes. We have developed an automatic system, called Pill-ID to match drug pill images based on several features (i.e., imprint, color, and shape) of the tablet. The color and shape information is encoded as a three-dimensional histogram and invariant moments, respectively. The imprint on the pill is encoded as feature vectors derived from SIFT and MLBP descriptors. Experimental results using a database of drug pill images (1029 illicit drug pill images and 14,002 legal drug pill images) show 73.04% (84.47%) rank-1 (rank-20) retrieval accuracy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.patrec.2011.08.022 | Pattern Recognition Letters |
Keywords | Field | DocType |
shape information,illicit drug pill image,illicit drug pill,drug-related crime,drug pill image,automatic system,mlbp descriptors,illicit drug,legal drug pill image,color histogram,image retrieval | Scale-invariant feature transform,Computer vision,Histogram,Feature vector,Pattern recognition,Color histogram,Pill,Image retrieval,Legal drug,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
33 | 7 | 0167-8655 |
Citations | PageRank | References |
7 | 0.62 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Young-Beom Lee | 1 | 57 | 4.01 |
| Unsang Park | 2 | 815 | 36.32 |
| Anil Jain | 3 | 33507 | 3334.84 |
| Seong-Whan Lee | 4 | 3756 | 343.90 |