A shoeprint is a mark made when the sole of a shoe comes into contact with a surface. People committing crimes inevitably leave their shoe marks at the crime scene. A study suggests that footwear impressions could be located and retrieved at approximately 35% of all crime scenes. More and more shoeprint images have been collected, leading to a few of shoeprint image databases. The constantly increasing of the size of these databases leads to a problem that it takes too much time to classify or retrieve them manually. In addition, when a shoeprint is actually being made, distortion, capture device-dependent noise, and cutting-out can be introduced.
This thesis deals with the problems involved in the development of an automated shoeprint image classification/ retrieval system. Firstly, it is concerned with investigating the problem of noise and artefact reduction, and the segmentation of a shoeprint from a noisy background. It aims to provide a software package to pre-processing an input shoeprint image from variety of sources. Secondly it is concerned with developing and investigating robust descriptors for a shoeprint image, and it also addresses the problem of matching shoeprint images using these descriptors.
In this thesis, some novel techniques for image quality measure, Gussian noise and Germ-grain noise reduction pattern segmentation and. screening have been developed. In addition, a few of low-level image feature descriptors, pattern & topological spectra and local image feature, have been proposes for indexing and searching a shoeprint image dataset. This thesis also has developed a prototype system to demonstrate the proposed algorithms and the application cases in forensic science. Shoeprint image retrieval tests on a few of datasets (totally more 15,000 images) suggest that local image features, compared with other shoeprint image descriptors, have great potential to be applied in real- world forensic investigations.
|Date of Award||Dec 2007|
- Queen's University Belfast
|Supervisor||Ahmed Bouridane (Supervisor) & Daniel Crookes (Supervisor)|