Analysis of Geometric Moments as Features for Identification of Forensic Ballistics Specimen.
ABSTRACT Firearm identification is one of the most essential, intricate and demanding tasks in crime investigation. Every firearm,
regardless of its size, make and model, has its own unique ‘fingerprint’ with respect to the marks on fired bullet and cartridge
cases. In this study, we investigate the features extracted from the images of the centre of the cartridge case in which firing
pin impression is located. Geometric moments up to the sixth order were computed to obtain the features based on a total of
747 cartridges case images from five different pistols of the same model. These sixteen features were found to be significantly
different using the MANOVA test. Correlation analysis was used to reduce the dimensionality of the features into only six
features. Classification results using cross-validation show that about 74.0% of the images were correctly classified and
this demonstrates the potential of using moment based features for firearm identification.
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ABSTRACT: Many heavy crimes committed such as murders or robberies frequently involve firearms, particularly pistols. In order to solve the crime cases, firearm identification is becoming vital. Unique marks are left on the bullet and the cartridge case when a firearm is fired. The firing pin impression is one of the most vital marks on any cartridge case. In this study, a total of 68 features of firing pin impression images – 20 basic statistical features, and 48 geometric moment features up to the sixth order – were extracted from three regions of the firing pin impression image, namely whole, centre and ring images. Five different types of pistol of the Parabellum Vector SPI 9 mm model were tested, where 50 bullets were fired from each pistol. Preliminary analysis using Pearson correlation shows that the features are significantly highly correlated. Therefore principal component analysis (PCA) was used to analyze the interrelationship among the features and combine them into a smaller set of factors while maintaining maximum information of the original patterns. PCA has reduced the dimensionality of the features into nine significant components of features. Discriminant analysis was used to identify the types of pistols used based on the new components. A total of 85.2% of the images were correctly classified according to the pistols used using cross-validation under discriminant analysis. The result demonstrates the potential of using PCA to reduce the dimensions of the numerical features towards an efficient firearm identification system.Procedia Computer Science. 13:144–151.
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ABSTRACT: Even though rapid advances in intelligent firearm identification have been made in recently years, the major practical and theoretical problems are still unsolved. From the practical point of view, capturing high quality images from ballistics specimen is a difficult task. From the theoretical point of view, extracting the descriptive features from projectile and cartridge images is an open research question in firearm identification. The aim of this paper is to address the research issues with respect to feature extraction and intelligent ballistics recognition. In this paper, different image processing techniques are employed for digitizing the ballistics images. Due to some segments in an image systematically distributed by the image’s geometrical circular center, the existing moment invariants however cannot extract the required pattern features for intelligent recognition. This paper presents the novel feature set called circle moment invariants to overcome the shortcoming of existing moment invariants. In addition, an intelligent system is designed for classifying and evaluating the extracted features of ballistics images. The experimental results indicate that the proposed approach and feature criteria are capable of classifying the cartridge images very efficiently and effectively. Consequently, the circle moment invariants are proved to be the adequate descriptors for describing the pattern features in cartridge images.Expert Syst. Appl. 01/2012; 39:2092-2101.
Conference Paper: Toward Virtual Machine Packing Optimization Based on Genetic Algorithm.[Show abstract] [Hide abstract]
ABSTRACT: To enable efficient resource provisioning in HaaS (Hardware as a Service) cloud systems, virtual machine packing, which migrate virtual machines to minimize running real node, is essential. The virtual machine packing problem is a multi-objective optimization problem with several parameters and weights on parameters change dynamically subject to cloud provider preference. We propose to employ Genetic Algorithm (GA) method, that is one of the meta-heuristics. We implemented a prototype Virtual Machine packing optimization mechanism on Grivon, which is a virtual cluster management system we have been developing. The preliminary evaluation implied the GA method is promising for the problem.Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II; 01/2009