TELKOMNIKA Telecommunication, Computing, Electronics and Control
Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

Concealed weapon detection, IR image, Principle component analysis, Sigmoidal Hadamard, Support vocter machine

Innovative tactics are employed by terrorists to conceal weapons and
explosives to perpetrate violent attacks, accounting for the deaths of millions of lives every year and contributing to huge economic losses to the global society. Achieving a high threat detection rate during an inspection of crowds to recognize and detect threat elements from a secure distance is the motivation for the development of intelligent image data analysis from a machine learning perspective. A method proposed to reduce the image dimensions with support vector, linearity and orthogonal. The functionality of CWD is contingent upon the plenary characterization of fusion data from multiple image sensors. The proposed method combines multiple sensors by hybrid fusion of sigmoidal Hadamard wavelet transform and PCA basis functions. Weapon recognition and the detection system, using Image segmentation and K means support vector machine A classifier is an
autonomous process for the recognition of threat weapons regardless of
make, variety, shape, or position on the suspect’s body despite concealment.

Ammar Wisam Altaher, Sabah Khudhair Abbas

DOI: 10.12928/TELKOMNIKA.v18i3.13541

Universitas Ahmad Dahlan

June 2020

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

TELKOMNIKA Telecommunication, Computing, Electronics and Control