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Automated Identification Systems

بسم الله الرحمن الرحيم الحمد لله و الصلاة و السلام على رسول الله. A key-Note Presentation on. Automated Identification Systems. Hany Ammar Lane Dept. of Computer Science & Electrical Engineering. The 2 nd International Conference on Computer and

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Automated Identification Systems

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  1. بسم الله الرحمن الرحيمالحمد لله و الصلاة و السلام على رسول الله A key-Note Presentation on Automated Identification Systems Hany Ammar Lane Dept. of Computer Science & Electrical Engineering The 2nd International Conference on Computer and Communication Engineering (ICCCE08), KL, Malaysia الله أكبر و لله الحمد

  2. Outline • Automated Identification Systems • The Center for Identification Technology Research (CITeR) • Examples of Automated Identification Systems Projects • Automated Dental Identification Systems (ADIS) • Research Team • Funding Agencies • Overview of ADIS and the ADIS Architecture • Record Pre-processing • Dental Image Retrieval • Matching • Summary الله أكبر و لله الحمد

  3. Fingerprint Hand Geometry Signature Dental Features Iris Voice Automated Identification Systems • Automated identification of a person based on his/her physiological or behavioral characteristics • Termed as “Biometrics” Identification الله أكبر و لله الحمد

  4. Automated Identification Systems • APPLICATIONS INCLUDE • HIGH SECURITY APPLICATIONS: financial services, health care, law enforcement, Government applications, travel and immigration, and E-commerce • FORENSIC IDENTIFICATION: help solve legal cases and public issues which include bank robberies, homicides, kidnapping cases, and identifying victims of mass disasters (Post Mortem identification) الله أكبر و لله الحمد

  5. Automated Identification Systems • Forensic Post-Mortem (PM) Identification Methods include: • Visual • Fingerprints • DNA • Dental • Dental features • Used to identify 75% of Tsunami victims in Thailand, and 20% of 9/11 victims identified in the 1st year compared to only 0.5% identified using DNA • Resist early decay of body tissues. • Withstand severe conditions in mass disasters. • Unique (Identification can sometimes be made from one tooth). الله أكبر و لله الحمد

  6. Digital Image Rep Mrs. X PM Record: - NCIC codes - Dental Radiographs ADIS Short Match List Forensic Scientist Automated Identification Systems • Example systems • Automated Dental Identification System ADIS الله أكبر و لله الحمد

  7. Video Sequence Ear Segmentation and Localization Image Enhancement 2-D and 3-D Feature Extraction Identification Enrollment Data-base Decision Automated Identification Systems • Example systems • Automated Ear Identification System AEIS Currently being developed WVU-UM الله أكبر و لله الحمد

  8. Automated Identification Systems • Biometrics Lab at WVU – Face Video data acquisition system • Collected a Database of 500 Subjects الله أكبر و لله الحمد

  9. NSF Center at WVUCITeR • The US National Science Foundation Center for Identification Technology Research (CITeR) • Industry/University Cooperative Research Center (I/UCRC) • West Virginia University is the lead institution http://www.citer.wvu.edu/ الله أكبر و لله الحمد

  10. Outline • Automated Identification Systems • The Center for Identification Technology Research (CITeR) • Examples of Automated Identification Systems Projects • Automated Dental Identification Systems (ADIS) • Research Team • Funding Agencies • Overview and the ADIS Architecture • Record Pre-processing • Dental Image Retrieval • Matching • Summary الله أكبر و لله الحمد

  11. ADIS Project Research Team Prof. Hany Ammar, Dr. Gamal Fahmy, Dr. Robert Howell, Dentist, Ph.D. Students: Ayman Abaza, Diaa Nassar, Eyad Haj-Said, MS Students: Mubasher, Zainab Millwallah, Usman Qureishi, Faisal Chaudhry, Mythili, and Satya Checkuri, Ali Bahoo Prof. Mohammad AbdelMottaleb, Ph.D. Students: Omaima Nomair, Mohammad Mahoor, Jindan, Prof. Anil Jain, Ph.D. Student: Hong Chen الله أكبر و لله الحمد

  12. Support $1.5M over 5 years - This research is supported in part by the U.S. National Science Foundation (Digital Government Program) under Award number EIA-0131079 to West Virginia University, - The research is also supported under Award number 2001-RC-CX-K013 from the Office of Justice Programs, National Institute of Justice, U.S. Department of Justice. Points of view in this document are those of the authors and do not necessarily represent position of the U.S. Department of Justice. - The research is conducted in Collaboration with The Criminal Justice Information Services Division (CJIS) of the US Federal Bureau of Investigation الله أكبر و لله الحمد

  13. Dental Identification Overview Forensic Odontologist Compares PM Records with AM records based on: • Dental Work (e.g. Fillings, Restorations ...) • Inherent Dental Characteristics (Crown Morphology, Root Morphology, Spacing …) • Very Time Consuming Process Identification of the victims of 9/11 - 20% of the 973 identified in the first year - Identification of 2,749 took around 40 months. الله أكبر و لله الحمد

  14. Dental Identification is a challenging problem Overview Source: The Bureau of Legal Dentistry (BOLD) - http://www.boldlab.org[2000] AM PM الله أكبر و لله الحمد

  15. Architecture Overview الله أكبر و لله الحمد

  16. ADIS Outline • Overview • Record Pre-processing • Dental Image Retrieval • Matching • Conclusion & Future Work • Comments & Questions الله أكبر و لله الحمد

  17. Reference Record - 16 Subject Record Record Pre-processing 1- Record Cropping: global segmentation of dental films from their corresponding records. The objective: to automate the process of cropping a composite digitized dental record into its constituent films الله أكبر و لله الحمد

  18. Approach Pre-Processing Background Extraction Corner-type Classification Right Round Cropping based on Factor Analysis Cropping based on Arch-Detection Dental Films Post-Processing Record Cropping Dental Record الله أكبر و لله الحمد

  19. Experimental Results Record Cropping Under-segmented الله أكبر و لله الحمد

  20. Experimental Results Randomly selected test sample of 100 dental records (images) from the CJIS ADIS database, the total film count in the test set is 722. Record Cropping • By calculating “”, • “” found to range between 0.49 - 0.91, • “” was used to identify the Under Cropped Segments. • Record cropping time ranges 15-40 sec. الله أكبر و لله الحمد

  21. Record Pre-processing periapical bitewing 3- Film Type Detection: dental films classification into bitewing, periapical, or panoramic. The objective: to automate the process of dental film type detection. الله أكبر و لله الحمد panoramic

  22. 4- Teeth Segmentation: Teeth segmentation from dental radiographic films. The objective: to automate the process of local segmentation of each tooth. Record Pre-processing teeth isolation into a rectangular box الله أكبر و لله الحمد

  23. Record Pre-processing 5-Tooth Contour Extraction: another level of segmentation, to extract the contour of the tooth. The objective: to extract an accurate smooth representative tooth contour, - Representative smooth contour. - Time / tooth = fraction of the second). الله أكبر و لله الحمد

  24. Experimental Result Record Pre-processing For a test set of 20 records, involving ~340 teeth The snake-based algorithm on the same platform takes about 5 sec compared to 0.16 sec. الله أكبر و لله الحمد

  25. RX7 RX4 RX6 RX5 7 5 M P RD7 RD4 RD6 RD5 Record Pre-processing 6-Teeth Labeling:automatic classification of teeth into incisors, canines, premolars and molars as part of creating a dental chart. The objective: • to accurately classify and label teeth, • to accommodate a missing segment. الله أكبر و لله الحمد

  26. Dental Atlas Record Pre-processing • An adult has 32 permanent teeth (8 Incisors, 4 Canines, 8 Premolars and 12 Molars). • Each tooth has a specific structure and position in the mouth. • Dental Atlas for the left half of the upper jaw. American Medical Association, http://www:medem:com الله أكبر و لله الحمد

  27. Teeth Labeling Approach – Eigen Teeth Record Pre-processing - Teeth Classification: added the film type, designed a technique based on Linear Discriminant Analysis (FisherTeeth). - Extended the validation stage for the presence of missing tooth. الله أكبر و لله الحمد

  28. Record Pre-processing Experimental Results of teeth labeling Based on the dataset used in the literature, (50 bitewing films involving about 400 teeth). الله أكبر و لله الحمد

  29. ADIS Outline • Overview • Record Pre-processing • Dental Image Retrieval • Matching • Conclusion & Future Work • Comments & Questions الله أكبر و لله الحمد

  30. Digital Image Repositories Candidate List Dental Image Retrieval 4- Potential Matches Search: searching the dental database in a fast way to find a candidate list. The objective: - to accomplish a relatively short candidate list, with a high probability of having the correct match reference. This objective directly targets the scalability of ADIS system. الله أكبر و لله الحمد

  31. Challenges Reference Record Multiple Representation Of the same tooth (RX6) Subject Record Potential Match Search الله أكبر و لله الحمد

  32. Proposed Approaches Potential Match Search 1- Appearance-based, namely Eigen images. • low computational-cost features; • Limitation: need geometric and gray-scale normalization. 2- shape –based namely moment invariant and edge orientation histogram • Limitation: need accurate teeth contour. الله أكبر و لله الحمد

  33. Minimum fusion, better for shape-based. The appearance-based, better for short candidate list. The edge direction histogram achieves the same performance for slightly longer candidate list. Potential Match Search Experimental Result (Comparison between appearance and shape based) الله أكبر و لله الحمد

  34. ADIS Outline • Overview • Record Pre-processing • Dental Image Retrieval • Matching • Conclusion & Future Work • Comments & Questions الله أكبر و لله الحمد

  35. Image Comparison Component الله أكبر و لله الحمد

  36. Image Comparison Component Teeth Alignment Teeth Alignment: is to align each corresponding pair, in other word to find the transformation matrix that best align the reference and subject segments. The objective: is to achieve an accurate aligned segments in few seconds, so as to allow for a faster Image Comparison Component. الله أكبر و لله الحمد

  37. Micro and Macro Decision-Making (The Strategy) Image Comparison Component • A Hierarchical fusion scheme: • Tooth-level fusion • Case-level fusion • A Ranking Scheme to Sort the Match List الله أكبر و لله الحمد

  38. Results Image Comparison Component الله أكبر و لله الحمد

  39. Outline • Automated Identification Systems • Example Research Projects • Automated Dental Identification Systems (ADIS) • Research Team • Funding Agencies • The ADIS Architecture • Record Pre-processing • Dental Image Retrieval • Matching • Summary الله أكبر و لله الحمد

  40. Summary • Automated Identification Systems are • needed in many applications in the years • to come • They Pose many • challenging problems الله أكبر و لله الحمد

  41. ADIS challenges Summary • Timeliness Performance • Teeth labeling and alignment are time consuming processes • Quality of radiographs are very critical for ADIS • Poor quality can affect the segmentation accuracy significantly • Matching efficiency can also be affected by poor quality radiograph الله أكبر و لله الحمد

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