Employing wavelet-based texture features in ammunition classification

被引：0

作者：

Borzino, Angelo M. C. R. ^{[1
]}

Maher, Robert C. ^{[2
]}

Apolinario, Jose A., Jr. ^{[1
]}

de Campos, Marcello L. R. ^{[3
]}

机构：

[1] Mil Inst Engn, Praca Gen Tiblireio 80, Rio De Janeiro, Brazil

[2] Montana State Univ, POB 173780, Bozeman, MT 59717 USA

[3] Univ Fed Rio de Janeiro, Av Pedro Calmon 550,Cidade Univ, Rio De Janeiro, Brazil

来源：

SENSORS, AND COMMAND, CONTROL, COMMUNICATIONS, AND INTELLIGENCE (C3I) TECHNOLOGIES FOR HOMELAND SECURITY, DEFENSE, AND LAW ENFORCEMENT APPLICATIONS XVI | 2017年 / 10184卷

关键词：

gunshot signal; ammunition classification; image texture features; wavelet transform; pattern recognition;

D O I：

10.1117/12.2262282

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Pattern recognition, a branch of machine learning, involves classification of information in images, sounds, and other digital representations. This paper uses pattern recognition to identify which kind of ammunition was used when a bullet was fired based on a carefully constructed set of gunshot sound recordings. To do this task, we show that texture features obtained from the wavelet transform of a component of the gunshot signal, treated as an image, and quantized in gray levels, are good ammunition discriminators. We test the technique with eight different calibers and achieve a classification rate better than 95%. We also compare the performance of the proposed method with results obtained by standard temporal and spectrographic techniques.

引用

页数：8

共 25 条

[1]

Bishop C., 2006, Pattern recognition and machine learning, P423

[2]

Borzino AMCR, 2014, EUR SIGNAL PR CONF, P1985

[3]

Cawley GC, 2006, IEEE IJCNN, P1661

[4]

Chou W, 2003, PATTERN RECOGNITION IN SPEECH AND LANGUAGE PROCESSING, P1

[5] USE OF GRAY VALUE DISTRIBUTION OF RUN LENGTHS FOR TEXTURE ANALYSIS [J].

CHU, A ;

SEHGAL, CM ;

GREENLEAF, JF .

PATTERN RECOGNITION LETTERS, 1990, 11 (06) :415-419

[6]

CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411

[7]

Daubechies I., 1992, 10 LECT WAVELETS, DOI DOI 10.1137/1.9781611970104

[8]

Debnath L., 2003, APPL NUMERICAL HARMO

[9] Minimum redundancy feature selection from microarray gene expression data [J].

Ding, C ;

Peng, HC .

PROCEEDINGS OF THE 2003 IEEE BIOINFORMATICS CONFERENCE, 2003, :523-528

[10]

Galloway M.M., 1975, COMPUTER GRAPHICS IM, V4, P172, DOI [10.1016/S0146-664X(75)80008-6, DOI 10.1016/S0146-664X(75)80008-6]

← 1 2 3 →