{"id":{"repo_id":"qu-belfast","oai_identifier":"oai:pure.qub.ac.uk/portal:studenttheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181"},"canonical_url":"https://search.dev.ndltd.org/etd/qu-belfast/oai:pure.qub.ac.uk/portal:studenttheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181","repository":{"repo_id":"qu-belfast","name":"Queen's University Belfast","base_url":"https://pureadmin.qub.ac.uk/ws/oai"},"display":{"title":"Shoeprint image noise reduction and retrieval","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Su, Hongjiang"],"institution":"Queen's University Belfast","degree_name":"Doctor of Philosophy","degree_level":"Doctoral Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007-12","date_published":"2007-12","updated_at":"2026-07-24T03:55:45Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:pure.qub.ac.uk/portal:studenttheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181"],"render_values":[{"text":"oai:pure.qub.ac.uk/portal:studenttheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181","href":null,"code":true}]}]},"links":{"outbound_url":"https://pure.qub.ac.uk/en/studentTheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Su, Hongjiang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2007-12"]},{"key":"dc:date.issued","label":"Date","values":["2007-12"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Electronics, Electrical Engineering and Computer Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Queen's University Belfast"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://pure.qub.ac.uk/en/studentTheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral Thesis"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:pure.qub.ac.uk/portal:studenttheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181","https://pure.qub.ac.uk/en/studentTheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://pure.qub.ac.uk/files/229040378/Shoeprint_image_noise_reduction_and_retrieval.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Shoeprint image noise reduction and retrieval"]}]}],"canonical_facts":{"dc:creator":["Su, Hongjiang"],"dc:date":["2007-12"],"dc:date.issued":["2007-12"],"dc:description.abstract":["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."],"dc:identifier":["oai:pure.qub.ac.uk/portal:studenttheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181","https://pure.qub.ac.uk/en/studentTheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181"],"dc:identifier.uri":["https://pure.qub.ac.uk/files/229040378/Shoeprint_image_noise_reduction_and_retrieval.pdf"],"dc:language":["eng"],"dc:publisher.department":["School of Electronics, Electrical Engineering and Computer Science"],"dc:publisher.institution":["Queen's University Belfast"],"dc:relation.isreferencedby":["https://pure.qub.ac.uk/en/studentTheses/d5bf5194-6cd3-4466-a37d-beb5e61a7181"],"dc:title":["Shoeprint image noise reduction and retrieval"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral Thesis"],"dc:type.qualificationname":["Doctor of Philosophy"]},"updated_at":"2026-07-24T03:55:45Z"}