Sedik, A, Kolivand, H and Albeedan, M (2024) An efficient image classification and segmentation method for crime investigation applications. Multimedia Tools and Applications. ISSN 1380-7501

Preview

Text s11042-024-19773-w.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview

Abstract

The field of forensic science is experiencing significant growth, largely driven by the increasing integration of holographic and immersive technologies, along with their associated head-mounted displays. These immersive systems have become increasingly vital in resolving critical crimes as they facilitate communication, interaction, and collaboration. Given the sensitive nature of their work, crime investigators require substantial technical support. There is a pressing need for accurate documentation and archiving of crime scenes, which can be addressed by leveraging 3D scanned scenes to accurately represent evidence and expected scenarios. This study aims to develop an enhanced AR. system that can be deployed on hologram facilities such as the Microsoft HoloLens. The proposed system encompasses two main approaches, namely image classification and image segmentation. Image classification utilizes various deep learning models, including lightweight convolutional neural networks (CNNs) and convolutional Long-Short Term Memory (ConvLSTM). Additionally, the image segmentation approach is based on the fuzzy active contour model (FACM). The effectiveness of the proposed system was evaluated for both classification and segmentation tasks, utilizing metrics such as accuracy, sensitivity, precision, and F1 score. The simulation results indicate that the proposed system achieved a 99% accuracy rate in classification and segmentation tasks, positioning it as an effective solution for detecting bloodstain patterns in AR applications.

Item Type:	Article
Uncontrolled Keywords:	0801 Artificial Intelligence and Image Processing; 0803 Computer Software; 0805 Distributed Computing; 0806 Information Systems; Artificial Intelligence & Image Processing; Software Engineering
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare. Criminology Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Divisions:	Computer Science & Mathematics
Publisher:	Springer Science
SWORD Depositor:	A Symplectic
Date Deposited:	17 Jul 2024 11:54
Last Modified:	17 Jul 2024 12:00
DOI or ID number:	10.1007/s11042-024-19773-w
URI:	https://researchonline.ljmu.ac.uk/id/eprint/23759

View Item