- Title
- Improved image analysis methodology for detecting changes in evidence positioning at crime scenes
- Creator
- Petty, Mark; Teng, Shyh; Murshed, Manzur
- Date
- 2019
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/173367
- Identifier
- vital:14665
- Identifier
-
https://doi.org/10.1109/DICTA47822.2019.8945934
- Identifier
- ISBN:9781728138572 (ISBN)
- Abstract
- This paper proposed an improved methodology to assist forensic investigators in detecting positional change of objects due to crime scene contamination. Either intentionally or by accident, crime scene contamination can occur during the investigation and documentation process. This new proposed methodology utilises an ASIFT-based feature detection algorithm that compares pre- and post-contaminated images of the same scene, taken from different viewpoints. The contention is that the ASIFT registration technique is better suited to real world crime scene photography, being more robust to affine distortion that occurs when capturing images from different viewpoints. The proposed methodology was tested with both the SIFT and ASIFT registration techniques to show that (1) it could identify missing, planted and displaced objects using both SIFT and ASIFT and (2) ASIFT is superior to SIFT in terms of error in displacement estimation, especially for larger viewpoint discrepancies between the pre- and post-contamination images. This supports the contention that our proposed methodology in combination with ASIFT is better suited to handle real world crime scene photography. © 2019 IEEE.; E1
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019
- Rights
- Copyright 2020 IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- Change detection; Crime scene image analysis; Image registration
- Full Text
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