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AUTOMATIC CLASSIFICATION AND RECOGNITION OF SHOEPRINTS

AUTOMATIC CLASSIFICATION AND RECOGNITION OF SHOEPRINTS. BACKGROUND. DAI .WEIYUN. match collected impression against known shoeprint database shoeprint impressions as common clues left at a crime scene need to detect criminal and infer the crime scene. INTRODUCTION.

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AUTOMATIC CLASSIFICATION AND RECOGNITION OF SHOEPRINTS

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  1. AUTOMATIC CLASSIFICATION AND RECOGNITION OF SHOEPRINTS • BACKGROUND DAI .WEIYUN • match collected impression against known shoeprint database • shoeprint impressions as common clues left at a crime scene • need to detect criminal and infer the crime scene

  2. INTRODUCTION • emphasize the importance of shoeprint SIGNIFICANT figure---approximately 30% of burglaries leave shoeprint • a typical application-identify a suspect • methods of matching impression &limitations ①Manually search through paper catalogues ②semi-automatically using a computer database (why the most serious?) ③a manual classification system difficult and hard to agree on a classification NOW PROBLEM—“only 3.5% of recovered prints were identified”, Netherlands THE REASON AND HOW TO IMPROVE SEE NEXT SLIDE

  3. CURRENT SYSTEM • Existing system --accurate classification & problems ①Limit the number of user ②Hard to deal with the increase in the number of imprint patterns Are accidental characteristics important in classification?? • NEW IDEA--automatic classification & advantages (CORE TODAY) What? How? Adv--- no need to manually classify remove amount of training

  4. fractal decomposition analysis AUTOMATIC IMAGE MATCHING FOR A SHOE PRINT DATABASE • fractal pattern matching –remove classification process and replace it • It involves two main steps 1. fractal decomposition analysis During this processing, the smallest change is produced MSNE (Mean Square Noise Error)---compute the smallest change -----determine image with minimum changes with respect to image under test get the best match in practice, BUT not the best.

  5. Get better result in reality --use 3X3 template window --compare the mean value of each window

  6. THE SYSTEM the user can control the search the user can control the search Graphical user interface

  7. SYSTEM RESULTS HOW TO GET IT, JUST DO EXPERIMENTS • EXPERIMENT1 decide the best standard method --MSNE on mean smoothed images to get the best results • EXPERIMENT2 determine the fractal technique’s robustness in rotated forms of shoe print Shows number of correctly identified shoe impressions at R (degree) rotation

  8. EXPERIMENT3 determine it in translated forms of an shoe print Shows number of correct shoe impressions identified at translation T x :y pixels • NORMALIZATION PROCESS • NORMALIZE IMAGE BEFORE ENTERING SHOEPRINTS INTO THE COMPTER • IMPORTANT APPLICATION --identifying partial image • --remove or reconstruct details

  9. FUTURE WORK • Now: focus on the matching of partial impressions • Future: 1.try to get efficient partition size 2.an alternative partitioning scheme 3.extend the tests in scale and grey level range

  10. USEFULL BOOKS TO HELP UNDERSTANDING • 1. Alexandre G, “Computerised classification of the shoeprints of burglars’ soles.”, Forensic Science International, 82(1996) 59-65 • 2. Hamm E, “Track identification: an historical overview.”, J. Forensic Identification, 39(1989) • 3. Informal conversation with Nick Mitchell of the Surrey Police Force. • 4. Rankin B, “Footwear marks - A step by step review.”, Forensic Science Societv Newsletter April (1998) 3 • 5. Geradts Z et al, “The image-database REBEZO for shoeprints with developments on automatic classification of shoe outsole designs.”, Forensic Science International, 82(1996) 21-31 • 6. Belser Ch et al, “Evaluation of the ISASsystem after two years of practical experience in forensic police work.”, Forensic Science International, (1996) 53-58

  11. ANY QUESTIONES ???

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