TY - GEN
T1 - Automated pollen identification system for forensic geo-historical location applications
AU - Hwang, Grace M.
AU - Riley, Kim C.
AU - Christou, Carol T.
AU - Jacyna, Garry M.
AU - Woodard, Jeffrey P.
AU - Ryan, Regina M.
AU - Bush, Mark B.
AU - Valencia, Bryan G.
AU - McMichael, Crystal N.H.
AU - Punyasena, Surangi W.
AU - Masters, David L.
PY - 2013
Y1 - 2013
N2 - The use of pollen grain analysis for forensic geo-historical location has been explored for several decades, yet it is not widely adopted in the United States. We confirmed significant improvement in geographic precision, i.e., from 2.5×107 to 1.2×105 km2, by simultaneously applying flowering plant data from four different taxa at the genus and species levels. Moreover, when we calculated precision using collected pollen data, we found that co-occurring, pairwise genus-level distinctions based on expert-provided indicator taxa resulted in average precision values of 4° and 4.5° in latitude and longitude, respectively - corresponding to roughly 1.8×105 km2. We also applied computer vision techniques to identify morphologically similar pollen grains, which resulted in grain-identification error rates of 2.18% and 6.24% at the genus and species levels, respectively, surpassing previously published records. Collectively, our results demonstrate that algorithmic identification of species-specific pollen morphology, founded on established computer vision techniques, when combined with species-level pollen distribution, has the potential to revolutionize the scope, accuracy, and precision of forensic geographic attribution.
AB - The use of pollen grain analysis for forensic geo-historical location has been explored for several decades, yet it is not widely adopted in the United States. We confirmed significant improvement in geographic precision, i.e., from 2.5×107 to 1.2×105 km2, by simultaneously applying flowering plant data from four different taxa at the genus and species levels. Moreover, when we calculated precision using collected pollen data, we found that co-occurring, pairwise genus-level distinctions based on expert-provided indicator taxa resulted in average precision values of 4° and 4.5° in latitude and longitude, respectively - corresponding to roughly 1.8×105 km2. We also applied computer vision techniques to identify morphologically similar pollen grains, which resulted in grain-identification error rates of 2.18% and 6.24% at the genus and species levels, respectively, surpassing previously published records. Collectively, our results demonstrate that algorithmic identification of species-specific pollen morphology, founded on established computer vision techniques, when combined with species-level pollen distribution, has the potential to revolutionize the scope, accuracy, and precision of forensic geographic attribution.
KW - Bayesian methods
KW - computer vision
KW - GBIF
KW - geo-historical location
KW - geographic attribution
KW - machine learning
KW - plant taxa
KW - pollen forensics
UR - http://www.scopus.com/inward/record.url?scp=84893222355&partnerID=8YFLogxK
U2 - 10.1109/THS.2013.6699018
DO - 10.1109/THS.2013.6699018
M3 - Contribución a la conferencia
AN - SCOPUS:84893222355
SN - 9781479915354
T3 - 2013 IEEE International Conference on Technologies for Homeland Security, HST 2013
SP - 297
EP - 303
BT - 2013 IEEE International Conference on Technologies for Homeland Security, HST 2013
T2 - 2013 13th IEEE International Conference on Technologies for Homeland Security, HST 2013
Y2 - 12 November 2013 through 14 November 2013
ER -