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@Article{Tong2014,
  author  = {Mingsi Tong and John Song and Wei Chu and Robert M Thompson},
  title   = {Fired Cartridge Case Identification Using Optical Images and the {Congruent Matching Cells (CMC)} Method},
  journal = {Journal of Research of the National Institute of Standards and Technology},
  year    = {2014},
  volume  = {119},
  pages   = {575-582},
}

@Article{AFTE1992,
  author  = {{AFTE Criteria for Identification Committee}},
  title   = {Theory of Identification, Range of Striae Comparison Reports, and Modified Glossary Definitions -- An {AFTE} Criteria for Identification Committee Report},
  journal = {AFTE Journal},
  year    = {1992},
  volume  = {24},
  number  = {2},
  pages   = {336-340},
}

@Book{PCAST2016,
  title     = {Report to the President on Forensic Science in Criminal Courts: Ensuring Scientific Validity of Feature-Comparison Methods},
  publisher = {Executive Office of the President},
  year      = {2016},
  author    = {{President's Council of Advisors on Science and Technology}},
  author-short = {{PCAST}},
  address   = {Washington, D.C.},
  month     = {sep},
}

@Article{Song2012,
  author   = {J Song and W Chu and T V Vorburger and R Thompson and T B Renegar and A Zheng and J Yen and R Silver and M Ols},
  title    = {Development of ballistics identification--from image comparison to topography measurement in surface metrology},
  journal  = {Measurement Science and Technology},
  year     = {2012},
  volume   = {23},
  number   = {5},
  pages    = {054010},
  abstract = {Fired bullets and ejected cartridge cases have unique ballistics signatures left by the firearm. By analyzing the ballistics signatures, forensic examiners can trace these bullets and cartridge cases to the firearm used in a crime scene. Current automated ballistics identification systems are primarily based on image comparisons using optical microscopy. The correlation accuracy depends on image quality which is largely affected by lighting conditions. Because ballistics signatures are geometrical micro-topographies by nature, direct measurement and correlation of the surface topography is being investigated for ballistics identification. A Two-dimensional and Three-dimensional Topography Measurement and Correlation System was developed at the National Institute of Standards and Technology for certification of Standard Reference Material 2460/2461 bullets and cartridge cases. Based on this system, a prototype system for bullet signature measurement and correlation has been developed for bullet signature identifications, and has demonstrated superior correlation results.},
  comment  = {URL: http://stacks.iop.org/0957-0233/23/i=5/a=054010},
}
﻿

@Article{Weller2012,
  author    = {Weller, Todd J. and Zheng, Alan and Thompson, Robert and Tulleners, Fred},
  title     = {Confocal Microscopy Analysis of Breech Face Marks on Fired Cartridge Cases from 10 Consecutively Manufactured Pistol Slides},
  journal   = {Journal of Forensic Sciences},
  year      = {2012},
  volume    = {57},
  number    = {4},
  pages     = {912--917},
  issn      = {1556-4029},
  comment   = {URL: http://dx.doi.org/10.1111/j.1556-4029.2012.02072.x},
  doi       = {10.1111/j.1556-4029.2012.02072.x},
  keywords  = {forensic science, firearm identification, confocal microscopy, consecutive manufacture, breech face, subclass marks, individual marks},
  publisher = {Blackwell Publishing Ltd},
}

@Article{Song2015,
  author  = {John Song},
  title   = {Proposed ``{Congruent} {Matching} {Cells}{(CMC)}'' Method for Ballistic Identification and Error Rate Estimation},
  journal = {AFTE Journal},
  year    = {2015},
  volume  = {47},
  number  = {3},
  pages   = {177-185},
}

@TechReport{Vorburger2007,
  author      = {T. Vorburger and J. Yen and B. Bachrach and T. Renegar and J. Filliben and L. Ma and H. Rhee and A. Zheng and J. Song and M. Riley and C. Foreman and S. Ballou},
  title       = {Surface topography analysis for a feasibility assessment of a {National Ballistics Imaging Database}},
  institution = {National Institute of Standards and Technology},
  year        = {2007},
  number      = {NISTIR 7362},
  address     = {Gaithersburg, MD},
}
﻿

@Article{Riva2014,
  author   = {Riva, Fabiano and Champod, Christophe},
  title    = {Automatic Comparison and Evaluation of Impressions Left by a Firearm on Fired Cartridge Cases},
  journal  = {Journal of Forensic Sciences},
  year     = {2014},
  volume   = {59},
  number   = {3},
  pages    = {637--647},
  issn     = {1556-4029},
  comment  = {URL: http://dx.doi.org/10.1111/1556-4029.12382},
  doi      = {10.1111/1556-4029.12382},
  keywords = {forensic science, firearms identification, likelihood ratio, cartridge cases, 3D topographies, evaluation},
}

@InProceedings{Roth2015,
  author    = {J. Roth and A. Carriveau and X. Liu and A. K. Jain},
  title     = {Learning-based ballistic breech face impression image matching},
  booktitle = {2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS)},
  year      = {2015},
  pages     = {1-8},
  address   = {Arlington, VA},
  month     = {Sept},
  note      = {\url{http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7358774&isnumber=7358743} (accessed April 28, 2017)},
  doi       = {10.1109/BTAS.2015.7358774},
  keywords  = {ballistics;face recognition;feature extraction;forensic science;image matching;police data processing;projectiles;weapons;Michigan State Forensics Laboratory;NIST database;OFL;ballistic imagery matching;ballistic images;bullet;cartridge case;cross-correlation function;distinct fingerprints;firearm;forensic examination;gentleBoost-based learning scheme;global registration;image processing techniques;learning-based ballistic breech face impression image matching;matching accuracy;operational database;polar coordinate;robust feature extraction;supervised learning schemes;Databases;Face;Forensics;Image matching;Microscopy;NIST;Surface topography},
}

@Article{Song2013,
  author  = {John Song},
  title   = {Proposed ``{NIST} Ballistics Identification System {(NBIS)}'' Based on {3D} Topography Measurements on Correlation Cells},
  journal = {AFTE Journal},
  year    = {2013},
  volume  = {45},
  number  = {2},
  pages   = {184-194},
}

@Book{Zeifman2017,
  title     = {A New Parametric Model for the Point Spread Function (PSF) and Its Application to Hubble Space Telescope Data [dissertation]},
  publisher = {Carnegie Mellon University},
  year      = {2014},
  author    = {Lubov E. Zeifman},
  address   = {Pittsburgh, PA},
  school    = {Carnegie Mellon University},
}

@Article{Lightstone2010,
  author  = {Laura Lightstone},
  title   = {The Potential for and Persistence of Subclass Characteristics on the Breech Faces of {SW40VE} {Smith} \& {Wesson} {Sigma} Pistols},
  journal = {AFTE Journal},
  year    = {2010},
  volume  = {42},
  number  = {4},
  pages   = {308-322},
}

@Misc{DErrico2004,
  author       = {John D'Errico},
  title        = {inpaint\_nans},
  howpublished = {MATLAB Central File Exchange, \url{https://www.mathworks.com/matlabcentral/fileexchange/4551-inpaint-nans} (accessed April 28, 2017)},
  year         = {2004},
}

@Book{Committee2008,
  title     = {Ballistic Imaging},
  publisher = {National Academies Press},
  year      = {2008},
  author    = {{National Research Council, Division on Engineering and Physical Sci, National Materials Advisory Board}},
  editor    = {Daniel L. Cork and John E. Rolph and Eugene S. Meieran and Carol V. Petrie},
  isbn      = {978-0-309-11724-1},
  date      = {2008-10-11},
  ean       = {9780309117241},
  pagetotal = {322},
  url       = {http://www.ebook.de/de/product/7390977/national_research_council_division_on_engineering_and_physical_sci_national_materials_advisory_board_ballistic_imaging.html},
}

@Article{Ott2017,
  author   = {Daniel Ott and Robert Thompson and Junfeng Song},
  title    = {Applying {3D} measurements and computer matching algorithms to two firearm examination proficiency tests},
  journal  = {Forensic Science International},
  year     = {2017},
  volume   = {271},
  pages    = {98 - 106},
  issn     = {0379-0738},
  abstract = {Abstract In order for a crime laboratory to assess a firearms examiner’s training, skills, experience, and aptitude, it is necessary for the examiner to participate in proficiency testing. As computer algorithms for comparisons of pattern evidence become more prevalent, it is of interest to test algorithm performance as well, using these same proficiency examinations. This article demonstrates the use of the Congruent Matching Cell (CMC) algorithm to compare 3D topography measurements of breech face impressions and firing pin impressions from a previously distributed firearms proficiency test. In addition, the algorithm is used to analyze the distribution of many comparisons from a collection of cartridge cases used to construct another recent set of proficiency tests. These results are provided along with visualizations that help to relate the features used in optical comparisons by examiners to the features used by computer comparison algorithms. },
  doi      = {http://dx.doi.org/10.1016/j.forsciint.2016.12.014},
  keywords = {Firearms identification, Pattern recognition, Proficiency testing, Congruent matching cells },
  url      = {http://www.sciencedirect.com/science/article/pii/S0379073816305461},
}

@Article{canny1986computational,
  author    = {Canny, John},
  title     = {A computational approach to edge detection},
  journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year      = {1986},
  number    = {6},
  pages     = {679--698},
  publisher = {IEEE},
}

@Article{George2004,
  author   = {Wm George},
  title    = {The Validation of the Brasscatcher Portion of the {NIBIN/IBIS} System Part Two: Fingerprinting Firearms Reality or Fantasy},
  journal  = {AFTE Journal},
  year     = {2004},
  volume   = {36},
  number   = {4},
  pages    = {289 thru 296},
  note     = {St. Louis County Police, Crime Laboratory, Firearm \& Tool Mark Identification Section, St. Louis County, Missouri.},
  abstract = {A study of the Brasscatcher portion of the NIBIN/IBIS system was conducted using a database of 850 cartridge cases fired in Smith & Wesson, .40 S&W caliber pistols. Correlations were generated for entries from Federal, Winchester and Remington brand ammunition and a study to locate the placement of matching cartridge cases initiated. Forensic Technology was able to open the entire database for viewing instead of the normal user field of 20 %. This study provided a real test of the ability of Brasscatcher to identify cartridge cases fired from similar firearms, and addresses the concept of fingerprinting firearms for use in criminal investigations. During this study an additional advantage regarding the second breech face impression image was revealed. The second image is not used for correlation purposes.},
  keywords = {Brasscatcher, Fingerprinting Firearms},
}

@Article{Kinder2004,
  author   = {De Kinder, Jan and Frederic Tulleners and Hugues Thiebaut},
  title    = {Reference ballistic imaging database performance},
  journal  = {Forensic Science International},
  year     = {2004},
  volume   = {140},
  number   = {2–3},
  pages    = {207 - 215},
  issn     = {0379-0738},
  abstract = {Ballistic imaging databases allow law enforcement to link recovered cartridge cases to other crime scenes and to firearms. The success of these databases has led many to propose that all firearms in circulation be entered into a reference ballistic image database (RBID). To assess the performance of an RBID, we fired 4200 cartridge cases from 600 9 mm Para Sig Sauer model \{P226\} series pistols. Each pistol fired two Remington cartridges, one of which was imaged in the RBID, and five additional cartridges, consisting of Federal, Speer, Winchester, Wolf, and \{CCI\} brands. Randomly selected samples from the second series of Remington cartridge cases and from the five additional brands were then correlated against the RBID. Of the 32 cartridges of the same make correlated against the RBID, 72% ranked in the top 10 positions. Likewise, of the 160 cartridges of the five different brands correlated against the database, 21% ranked in the top 10 positions. Generally, the ranking position increased as the size of the \{RBID\} increased. We obtained similar results when we expanded the \{RBID\} to include firearms with the same class characteristics for breech face marks, firing pin impressions, and extractor marks. The results of our six queries against the \{RBID\} indicate that a reference ballistics image database of new guns is currently fraught with too many difficulties to be an effective and efficient law enforcement tool.},
  doi      = {http://dx.doi.org/10.1016/j.forsciint.2003.12.002},
  keywords = {Forensics, Firearms identification, Ballistic fingerprinting, Imaging, Automated comparisons },
  url      = {http://www.sciencedirect.com/science/article/pii/S0379073803005371},
}
﻿

@Book{Heard2009,
  title     = {Firearms - Bullet and Cartridge Case Identification: A Brief History of Firearms Identification},
  publisher = {John Wiley \& Sons, Ltd},
  year      = {2009},
  author    = {Heard, Brian J.},
  address   = {Chichester, UK},
  isbn      = {9780470061589},
  booktitle = {Wiley Encyclopedia of Forensic Science},
  comment   = {URL: http://dx.doi.org/10.1002/9780470061589.fsa340.pub2},
  doi       = {10.1002/9780470061589.fsa340.pub2},
  keywords  = {bullet and cartridge case identification, class and individual characteristics, accidental mismatches and manufacturing marks, problematical striating matches, the Daubert trilogy, the concept of consecutively matching stria (CMS)},
  pages     = {145-147},
}

@InProceedings{Gionis1999,
  author    = {Gionis, Aristides and Indyk, Piotr and Motwani, Rajeev},
  title     = {Similarity Search in High Dimensions via Hashing},
  booktitle = {Proceedings of the 25th International Conference on Very Large Data Bases},
  year      = {1999},
  series    = {VLDB '99},
  pages     = {518--529},
  address   = {San Francisco, CA, USA},
  publisher = {Morgan Kaufmann Publishers Inc.},
  acmid     = {671516},
  isbn      = {1-55860-615-7},
  numpages  = {12},
  url       = {http://dl.acm.org/citation.cfm?id=645925.671516},
}

@Article{Cleveland1979,
  author    = {William S. Cleveland},
  title     = {Robust Locally Weighted Regression and Smoothing Scatterplots},
  journal   = {Journal of the American Statistical Association},
  year      = {1979},
  volume    = {74},
  number    = {368},
  pages     = {829-836},
  issn      = {01621459},
  abstract  = {The visual information on a scatterplot can be greatly enhanced, with little additional cost, by computing and plotting smoothed points. Robust locally weighted regression is a method for smoothing a scatterplot, (x<sub>i</sub>, y<sub>i</sub>), i = 1, ⋯, n, in which the fitted value at x<sub>k</sub> is the value of a polynomial fit to the data using weighted least squares, where the weight for (x<sub>i</sub>, y<sub>i</sub>) is large if x<sub>i</sub> is close to x<sub>k</sub> and small if it is not. A robust fitting procedure is used that guards against deviant points distorting the smoothed points. Visual, computational, and statistical issues of robust locally weighted regression are discussed. Several examples, including data on lead intoxication, are used to illustrate the methodology.},
  publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
  url       = {http://www.jstor.org/stable/2286407},
}
﻿

@Article{Tai2017,
  author   = {Tai, Xiao Hui and Eddy, William F.},
  title    = {A Fully Automatic Method for Comparing Cartridge Case Images},
  journal  = {Journal of Forensic Sciences},
  year     = {2018},
  pages    = {n/a--n/a},
  issn     = {1556-4029},
  doi      = {10.1111/1556-4029.13577},
  keywords = {forensic science, firearms identification, cartridge cases, breechface marks, circular symmetry, cross-correlation function},
  url      = {http://dx.doi.org/10.1111/1556-4029.13577},
}

@Article{Gerules2013,
  author   = {George Gerules and Sanjiv K. Bhatia and Daniel E. Jackson},
  title    = {A survey of image processing techniques and statistics for ballistic specimens in forensic science},
  journal  = {Science \& Justice},
  year     = {2013},
  volume   = {53},
  number   = {2},
  pages    = {236 - 250},
  issn     = {1355-0306},
  abstract = {This paper provides a review of recent investigations on the image processing techniques used to match spent bullets and cartridge cases. It is also, to a lesser extent, a review of the statistical methods that are used to judge the uniqueness of fired bullets and spent cartridge cases. We review 2D and 3D imaging techniques as well as many of the algorithms used to match these images. We also provide a discussion of the strengths and weaknesses of these methods for both image matching and statistical uniqueness. The goal of this paper is to be a reference for investigators and scientists working in this field.},
  doi      = {http://dx.doi.org/10.1016/j.scijus.2012.07.002},
  keywords = {Forensic image processing, Matching bullets, Matching cartridges, Statistical validation},
  url      = {http://www.sciencedirect.com/science/article/pii/S1355030612001050},
}

@InProceedings{Li2003,
  author    = {D. G. Li},
  title     = {Image processing for the positive identification of forensic ballistics specimens},
  booktitle = {Sixth International Conference of Information Fusion, 2003. Proceedings of the},
  year      = {2003},
  volume    = {2},
  pages     = {1494-1498},
  month     = {July},
  abstract  = {Not Available},
  doi       = {10.1109/ICIF.2003.177417},
  keywords  = {Australia;Feature extraction;Forensics;Image databases;Image processing;Image segmentation;Layout;Projectiles;Shape;Testing},
}

@Article{Zhou2001,
  author   = {Zhou, Jie and Xin, Le-ping and Rong, Gang and Zhang, David},
  title    = {Algorithm of automatic cartridge identification},
  journal  = {Optical Engineering},
  year     = {2001},
  volume   = {40},
  number   = {12},
  pages    = {2860-2865},
  abstract = {An effective algorithm for automatic cartridge identification is proposed. The ejector impression is used to calibrate the cartridge image. Features of the firing pin impression and the breach face impression are extracted using active snake and orientation analysis, respectively. These different features are then integrated to make a final decision by using a support vector machine. Experimental results illustrate the effectiveness of our algorithm. © 2001 Society of Photo-Optical Instrumentation Engineers.},
  doi      = {10.1117/1.1417497},
  isbn     = {0091-3286},
  url      = { http://dx.doi.org/10.1117/1.1417497},
}

@InProceedings{Brein2005,
  author    = {{Brein}, C.},
  title     = {{Segmentation of cartridge cases based on illumination and focus series}},
  booktitle = {Image and Video Communications and Processing 2005},
  year      = {2005},
  editor    = {{Said}, A. and {Apostolopoulos}, J.~G.},
  volume    = {5685},
  series    = {\procspie},
  pages     = {228-238},
  month     = mar,
  adsnote   = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl    = {http://adsabs.harvard.edu/abs/2005SPIE.5685..228B},
  doi       = {10.1117/12.585763},
}

@InProceedings{Kamalakannan2011,
  author    = {{Kamalakannan}, S. and {Mann}, C.~J. and {Bingham}, P.~R. and {Karnowski}, T.~P. and {Gleason}, S.~S.},
  title     = {{Automatic firearm class identification from cartridge cases}},
  booktitle = {Image Processing: Machine Vision Applications IV},
  year      = {2011},
  volume    = {7877},
  series    = {\procspie},
  pages     = {78770P},
  month     = mar,
  adsnote   = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl    = {http://adsabs.harvard.edu/abs/2011SPIE.7877E..0PK},
  doi       = {10.1117/12.872414},
  eid       = {78770P},
}

@Misc{Tunali2009,
  author    = {Tunali, E. and Leloglu, U.M. and Sakarya, U.},
  title     = {Method for automatic region segmentation on cartridge case base and selection of the best mark region for cartridge case comparison},
  month     = oct #{~29},
  year      = {2009},
  note      = {WO Patent App. PCT/IB2009/051,609},
  publisher = {Google Patents},
  url       = {http://www.google.com.na/patents/WO2009130651A1?cl=en},
}

@InProceedings{Thumwarin2008,
  author    = {P. Thumwarin},
  title     = {An Automatic System for Firearm Identification},
  booktitle = {2008 International Symposium on Communications and Information Technologies},
  year      = {2008},
  pages     = {100-103},
  month     = {Oct},
  abstract  = {This research proposes an automatic system for firearm identification. In the proposed method, firearm can be identified by using the head of cartridge case image. The unique feature of the individual gun can be extracted by using the absolute value of Fourier coefficients of polar image of cartridge case on circles with different radii. The obtained absolute value of Fourier coefficients is considered as the rotation invariance feature of the cartridge case image. Finally, the Fisher's linear discriminant function is used to identify the gun. The result successfully revel the effectiveness of the method of implementation for some extent.},
  doi       = {10.1109/ISCIT.2008.4700162},
  keywords  = {Fourier analysis;feature extraction;image recognition;image representation;police data processing;weapons;Fisher linear discriminant function;Fourier approximation;automatic firearm identification system;crime investigation;feature extraction;fired cartridge case image representation;gun;polar image;rotation invariance feature;Digital cameras;Eyes;Feature extraction;Focusing;Head;Humans;Information technology;Layout;Linear discriminant analysis;Pixel},
}

@Proceeding{Geradts1999,
  author   = {Geradts, Zeno J. and Bijhold, Jurrien and Hermsen, Rob},
  title    = {Pattern recognition in a database of cartridge cases},
  year     = {1999},
  abstract = {Several systems exist for collecting spent ammunition for forensic investigation. These databases store images of cartridge cases and the marks on them. The research in this paper is focused on the different methods of feature selection and pattern recognition that can be used for comparison. For automatic comparison of these images it is necessary to extract firstly the useful parts of the images. On databases of 3800 images several processing steps have been tested and compared. The results and methods, which have been implemented, are presented. The usual correlation methods based on gray values of all relevant image data have been tested. They were useful in the database. Further invariant image descriptors and the a trous wavelet transform have been implemented. These methods are promising, however more investigation is needed for the use of these methods.},
  doi      = {10.1117/12.334520},
  journal  = {Proc. SPIE},
  pages    = {104-115},
  url      = { http://dx.doi.org/10.1117/12.334520},
  volume   = {3576},
}

@Article{Geradts2001,
  author    = {Geradts, Zeno J and Bijhold, Jurrien and Hermsen, Rob and Murtagh, Fionn},
  title     = {Image matching algorithms for breech face marks and firing pins in a database of spent cartridge cases of firearms},
  journal   = {Forensic Science International},
  year      = {2001},
  volume    = {119},
  number    = {1},
  pages     = {97--106},
  publisher = {Elsevier},
}

@Article{Daugman2004,
  author   = {J. Daugman},
  title    = {How iris recognition works},
  journal  = {IEEE Transactions on Circuits and Systems for Video Technology},
  year     = {2004},
  volume   = {14},
  number   = {1},
  pages    = {21-30},
  month    = {Jan},
  issn     = {1051-8215},
  abstract = {Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm2 over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.},
  doi      = {10.1109/TCSVT.2003.818350},
  keywords = {biometrics (access control);decision theory;entropy;image matching;statistical analysis;transform coding;wavelet transforms;biometrics;decision theory;discrimination entropy;iris pattern recognition;iris phase structure;iris recognition;multi-scale quadrature wavelet coding;one-to-many identification mode;one-to-one identification mode;personal identity;statistical independence;verification;Demodulation;Entropy;Image databases;Iris recognition;Ligaments;Lighting;Pattern matching;Pattern recognition;Pigmentation;Testing},
}

@TechReport{Baldwin2014,
  author      = {Baldwin, David P and Bajic, Stanley J and Morris, Max and Zamzow, Daniel},
  title       = {A study of false-positive and false-negative error rates in cartridge case comparisons},
  institution = {AMES LAB IA},
  year        = {2014},
}

@Article{Hare2016,
  author        = {{Hare}, E. and {Hofmann}, H. and {Carriquiry}, A.},
  title         = {{Automatic Matching of Bullet Land Impressions}},
  journal       = {ArXiv e-prints},
  year          = {2016},
  month         = jan,
  adsnote       = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl        = {http://adsabs.harvard.edu/abs/2016arXiv160105788H},
  archiveprefix = {arXiv},
  eprint        = {1601.05788},
  keywords      = {Statistics - Applications},
  primaryclass  = {stat.AP},
}

@Article{Roberge2006,
  author    = {Roberge, Danny and Beauchamp, Alain},
  title     = {The use of {BulletTRAX-3D} in a study of consecutively manufactured barrels},
  journal   = {AFTE Journal},
  year      = {2006},
  volume    = {38},
  number    = {2},
  pages     = {166},
  publisher = {ASSOCIATION OF FIREARMS AND TOOL MARF EXAMINERS},
}

@Article{Champod2016,
  author    = {Champod, Christophe and Biedermann, Alex and Vuille, Joelle and Willis, Sheila and De Kinder, Jan},
  title     = {{ENFSI} Guideline for Evaluative Reporting in Forensic Science, A Primer for Legal Practitioners},
  year      = {2016},
  volume    = {180},
  pages     = {189-193},
  month     = {01},
  booktitle = {Criminal Law \& Justice Weekly},
}

@Misc{CadreForensics,
  author       = {{Cadre Forensics}},
  howpublished = {\url{http://www.cadreforensics.com/technology.html}, accessed 2017-08-20},
  year         = {2017},
}

@Article{McNamee2001,
  author    = {McNamee, Rebecca L. and Eddy, William F.},
  title     = {Visual analysis of variance: A tool for quantitative assessment of {fMRI} data processing and analysis},
  journal   = {Magnetic Resonance in Medicine},
  year      = {2001},
  volume    = {46},
  number    = {6},
  pages     = {1202--1208},
  issn      = {1522-2594},
  doi       = {10.1002/mrm.1317},
  keywords  = {fMRI, ANOVA, VANOVA, fMRI data processing, fMRI noise},
  publisher = {John Wiley \& Sons, Inc.},
  url       = {http://dx.doi.org/10.1002/mrm.1317},
}

@Misc{UEFT,
  author       = {{Ultra Electronics Forensic Technology}},
  howpublished = {\url{https://cdn2.hubspot.net/hubfs/71705/IBIS%20TRAX-HD3D%20EN%20Brochure%20Nov%2029%202016%20web.pdf}, accessed 2017-08-31},
  year         = {2017},
}

@Book{Ghosh2007,
  title     = {Duplicate Image Detection in Large Scale Databases},
  publisher = {Book Chapter in Platinum Jubilee Volume},
  year      = {2007},
  author    = {Pratim Ghosh and E. Drelie Gelasca and K.R. Ramakrisnan and B.S. Manjunath},
  editor    = {Indian Statistical Institute, Kolkata},
  month     = {Oct},
  url       = {https://vision.ece.ucsb.edu/sites/vision.ece.ucsb.edu/files/publications/pratim_2007_book.pdf},
}

@Article{Baker2004,
  author        = {Baker, Simon and Matthews, Iain},
  title         = {Lucas-Kanade 20 Years On: A Unifying Framework},
  journal       = {International Journal of Computer Vision},
  year          = {2004},
  volume        = {56},
  number        = {3},
  pages         = {221--255},
  month         = {Feb},
  issn          = {1573-1405},
  __markedentry = {[xtai:]},
  abstract      = {Since the Lucas-Kanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision. Applications range from optical flow and tracking to layered motion, mosaic construction, and face coding. Numerous algorithms have been proposed and a wide variety of extensions have been made to the original formulation. We present an overview of image alignment, describing most of the algorithms and their extensions in a consistent framework. We concentrate on the inverse compositional algorithm, an efficient algorithm that we recently proposed. We examine which of the extensions to Lucas-Kanade can be used with the inverse compositional algorithm without any significant loss of efficiency, and which cannot. In this paper, Part 1 in a series of papers, we cover the quantity approximated, the warp update rule, and the gradient descent approximation. In future papers, we will cover the choice of the error function, how to allow linear appearance variation, and how to impose priors on the parameters.},
  day           = {01},
  doi           = {10.1023/B:VISI.0000011205.11775.fd},
  url           = {https://doi.org/10.1023/B:VISI.0000011205.11775.fd},
}

@Article{Tai2018,
  author        = {Xiao Hui Tai and William F. Eddy},
  title         = {A Fully Automatic Method for Comparing Cartridge Case Images,},
  journal       = {Journal of Forensic Sciences},
  year          = {2018},
  volume        = {63},
  number        = {2},
  pages         = {440-448},
  __markedentry = {[xtai:6]},
  abstract      = {Abstract When a gun is fired, it leaves marks on cartridge cases that are thought to be unique to the gun. In current practice, firearms examiners inspect cartridge cases for “sufficient agreement,” in which case they conclude that they come from the same gun, testifying in courts as such. A 2016 President's Council of Advisors on Science and Technology report questioned the scientific validity of such analysis (President's Committee of Advisors on Science and Technology, Washington, DC, Executive Office of the President). One recommendation was to convert firearms analysis to an objective method. We propose a fully automated, open‐source method for comparing breechface marks on cartridge cases using 2D optical images. We improve on existing methodology by automating the selection of marks, and removing the effects of circular symmetry. We propose an empirical computation of a “random match probability” given a known database, which can be used to quantify the weight of evidence. We demonstrate an improvement in accuracy on images from controlled test fires.},
  doi           = {10.1111/1556-4029.13577},
  eprint        = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/1556-4029.13577},
  keywords      = {forensic science, firearms identification, cartridge cases, breechface marks, circular symmetry, cross‐correlation function},
  url           = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1556-4029.13577},
}

@Unknown{King2013,
  author = {King, William and Wells, William and Katz, Charles and Maguire, Edward and Frank, James},
  title  = {Opening the Black Box of NIBIN: A Descriptive Process and Outcome Evaluation of the Use of NIBIN and Its Effects on Criminal Investigations, Final Report},
  month  = {10},
  year   = {2013},
}

@Comment{jabref-meta: databaseType:bibtex;}
