A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera models and machine perception
Camera models and machine perception
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Intelligent Imaging of Forensic Ballistics Specimens for ID
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 3 - Volume 03
Analysis of Geometric Moments as Features for Identification of Forensic Ballistics Specimen
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Recognitive Aspects of Moment Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moment invariants for pattern recognition
Pattern Recognition Letters
Hi-index | 12.05 |
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.