Novel multiresolution-based hybrid approach for 3D footwear outsole feature classification and extraction

Gao, B. and Allinson, Nigel (2010) Novel multiresolution-based hybrid approach for 3D footwear outsole feature classification and extraction. In: 18th European Signal Processing Conference, EUSIPCO 2010, 23 - 27 August 2010, Aalborg, Denmark.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Footwear impressions retrieved from crime scenes are often due to shoes in various stages of wear. This forensicrelated research on footwear recognition is able to extract information-rich 3D outsole patterns and produce 2D shoeprints regardless of different degrees of wear. Based on pattern characteristics, outsoles are categorized into two types, Convex-Pattern-Dominant Outsoles (Convex-PDOs) and Concave-Pattern-Dominant Outsoles (Concave-PDOs). Initial work for extracting 3D Features from Concave-PDOs is reported in this paper. In our proposed method, outsole models are first captured using a 3D scanner. Patterns corresponding to higher and lower curvature variations are subsequently classified using a multiresolution-based curvature analysis approach. In a subsequent step to discard outliers from the extracted 3D features, by modifying contours of 3D outsole models, a pyramid method is employed to generate composite results. Visual analysis on current experimental investigations shows promising results for further 3D feature extraction and 2D shoeprint generation. © EURASIP, 2010.

Keywords:3-D feature extraction, 3-D scanner, Crime scenes, Curvature analysis, Curvature variation, Experimental investigations, Feature classification, Hybrid approach, On currents, Pattern characteristic, Visual analysis, Feature extraction, Shoe manufacture, Signal processing, Three dimensional
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G740 Computer Vision
F Physical Sciences > F410 Forensic Science
Divisions:College of Science > School of Computer Science
ID Code:8537
Deposited On:05 Apr 2013 09:39

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