Evidential significance of automotive paint trace evidence using a pattern recognition based infrared library search engine for the Paint Data Query Forensic Database

Talanta ◽  
2016 ◽  
Vol 159 ◽  
pp. 317-329 ◽  
Author(s):  
Barry K. Lavine ◽  
Collin G. White ◽  
Matthew D. Allen ◽  
Ayuba Fasasi ◽  
Andrew Weakley
Keyword(s):  
Pattern Recognition ◽  
Search Engine ◽  
Trace Evidence ◽  
Library Search ◽  
Data Query ◽  
Automotive Paint

Pattern Recognition-Assisted Infrared Library Searching of the Paint Data Query Database to Enhance Lead Information from Automotive Paint Trace Evidence

2016 ◽  
Vol 71 (3) ◽  
pp. 480-495 ◽  
Author(s):  
Barry K. Lavine ◽  
Collin G. White ◽  
Matthew D. Allen ◽  
Andrew Weakley
Keyword(s):  
Pattern Recognition ◽  
Ir Spectra ◽  
Search Algorithm ◽  
Paint Layer ◽  
Trace Evidence ◽  
Discrimination Power ◽  
Library Search ◽  
Layer Sequence ◽  
Data Query ◽  
Automotive Paint

Multilayered automotive paint fragments, which are one of the most complex materials encountered in the forensic science laboratory, provide crucial links in criminal investigations and prosecutions. To determine the origin of these paint fragments, forensic automotive paint examiners have turned to the paint data query (PDQ) database, which allows the forensic examiner to compare the layer sequence and color, texture, and composition of the sample to paint systems of the original equipment manufacturer (OEM). However, modern automotive paints have a thin color coat and this layer on a microscopic fragment is often too thin to obtain accurate chemical and topcoat color information. A search engine has been developed for the infrared (IR) spectral libraries of the PDQ database in an effort to improve discrimination capability and permit quantification of discrimination power for OEM automotive paint comparisons. The similarity of IR spectra of the corresponding layers of various records for original finishes in the PDQ database often results in poor discrimination using commercial library search algorithms. A pattern recognition approach employing pre-filters and a cross-correlation library search algorithm that performs both a forward and backward search has been used to significantly improve the discrimination of IR spectra in the PDQ database and thus improve the accuracy of the search. This improvement permits inter-comparison of OEM automotive paint layer systems using the IR spectra alone. Such information can serve to quantify the discrimination power of the original automotive paint encountered in casework and further efforts to succinctly communicate trace evidence to the courts.

scholarly journals CiteSeerX: AI in a Digital Library Search Engine

AI Magazine ◽  
2015 ◽  
Vol 36 (3) ◽  
pp. 35-48 ◽  
Author(s):  
Jian Wu ◽  
Kyle Mark Williams ◽  
Hung-Hsuan Chen ◽  
Madian Khabsa ◽  
Cornelia Caragea ◽  
...  
Keyword(s):  
Search Engine ◽  
Digital Library ◽  
Digital Libraries ◽  
Library Search ◽  
Design Concepts ◽  
Metadata Extraction ◽  
Group Members ◽  
Maintenance Requirements ◽  
Search Modes ◽  
Algorithm Search

CiteSeerX is a digital library search engine providing access to more than five million scholarly documents with nearly a million users and millions of hits per day. We present key AI technologies used in the following components: document classification and de-duplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5–6 years. We show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. We also present AI technologies implemented in table and algorithm search, which are special search modes in CiteSeerX. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.

scholarly journals Library Search Prefilters for Vehicle Manufacturers to Assist in the Forensic Examination of Automotive Paints

2017 ◽  
Vol 72 (3) ◽  
pp. 476-488 ◽  
Author(s):  
Barry K. Lavine ◽  
Collin G. White ◽  
Tao Ding
Keyword(s):  
Pattern Recognition ◽  
Ir Spectra ◽  
General Motors ◽  
Discrete Wavelet ◽  
Vehicle Manufacturer ◽  
Clear Coat ◽  
Discrimination Power ◽  
Paint Systems ◽  
Automotive Paint ◽  
Spectral Libraries

Pattern recognition techniques have been applied to the infrared (IR) spectral libraries of the Paint Data Query (PDQ) database to differentiate between nonidentical but similar IR spectra of automotive paints. To tackle the problem of library searching, search prefilters were developed to identify the vehicle make from IR spectra of the clear coat, surfacer–primer, and e-coat layers. To develop these search prefilters with the appropriate degree of accuracy, IR spectra from the PDQ database were preprocessed using the discrete wavelet transform to enhance subtle but significant features in the IR spectral data. Wavelet coefficients characteristic of vehicle make were identified using a genetic algorithm for pattern recognition and feature selection. Search prefilters to identify automotive manufacturer through IR spectra obtained from a paint chip recovered at a crime scene were developed using 1596 original manufacturer’s paint systems spanning six makes (General Motors, Chrysler, Ford, Honda, Nissan, and Toyota) within a limited production year range (2000–2006). Search prefilters for vehicle manufacturer that were developed as part of this study were successfully validated using IR spectra obtained directly from the PDQ database. Information obtained from these search prefilters can serve to quantify the discrimination power of original automotive paint encountered in casework and further efforts to succinctly communicate trace evidential significance to the courts.

The Library Search Engine

2011 ◽  
pp. 267-276
Author(s):  
Karin Herm ◽  
Sibylle Volz
Keyword(s):  
Search Engine ◽  
Library Search

Comparison of SIMCA pattern recognition and library search identification of hazardous compounds from mass spectra

1992 ◽  
Vol 257 (2) ◽  
pp. 229-238 ◽  
Author(s):  
M. Sarker ◽  
W. Graham Glen ◽  
Long-Biao Yin ◽  
W.J. Dunn ◽  
Donald R. Scott ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Mass Spectra ◽  
Library Search ◽  
Hazardous Compounds

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

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