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Erschienen in:
Background Modeling Using Temporal-Local Sample Density Outlier Detection Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Local Binary Pattern and Its Variants for Target Recognition in Infrared Imagery

verfasst von : Aparna Akula, Ripul Ghosh, Satish Kumar, H. K. Sardana

Erschienen in: Proceedings of International Conference on Computer Vision and Image Processing

Verlag: Springer Singapore

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Abstract

In this research work, local binary pattern (LBP)-based automatic target recognition system is proposed for classification of various categories of moving civilian targets using their infrared image signatures. Target recognition in infrared images is demanding owing to large variations in target signature and limited target to background contrast. This demands robust features/descriptors which can represent possible variations of the target category with minimal intra class variance. LBP, a simple yet efficient texture operator initially proposed for texture recognition of late is gaining popularity in face and object recognition applications. In this work, the suitability of LBP and two of its variants, local ternary pattern (LTP), complete local binary pattern (CLBP) for the task of recognition in infrared images has been evaluated. The performance of the method is validated with target clips obtained from ‘CSIR-CSIO moving object thermal infrared imagery dataset’. The number of classes is four- three different target classes (Ambassador, Auto and Pedestrian) and one class representing the background. Classification accuracies of 89.48 %, 100 % and 100 % were obtained for LBP, LTP and CLBP, respectively. The results indicate the suitability of LBP operator for target recognition in infrared images.

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Vorheriges Kapitel A Hybrid Method for Image Categorization Using Shape Descriptors and Histogram of Oriented Gradients
Nächstes Kapitel Applicability of Self-Organizing Maps in Content-Based Image Classification
Literatur
1.
V. M. Patel, et al., “Sparsity-motivated automatic target recognition,” Applied optics, vol. 50, pp. 1425–1433, 2011.
2.
B. Bhanu, “Automatic target recognition: State of the art survey,” Aerospace and Electronic Systems, IEEE Transactions on, pp. 364–379, 1986.
3.
A. Arora, et al., “A line in the sand: a wireless sensor network for target detection, classification, and tracking,” Computer Networks, vol. 46, pp. 605–634, 2004.
4.
P. Natarajan, et al., “Multi-Camera Coordination and Control in Surveillance Systems: A Survey,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 11, p. 57, 2015.
5.
R. Ghosh, et al., “Time–frequency analysis based robust vehicle detection using seismic sensor,” Journal of Sound and Vibration, vol. 346, pp. 424–434, 2015.
6.
R. Xin and R. Lei, “Search aid system based on machine vision and its visual attention model for rescue target detection,” in Intelligent Systems (GCIS), 2010 Second WRI Global Congress on, 2010, pp. 149–152.
7.
C. N. E. Anagnostopoulos, et al., “A license plate-recognition algorithm for intelligent transportation system applications,” Intelligent Transportation Systems, IEEE Transactions on, vol. 7, pp. 377–392, 2006.
8.
Y. Dong, et al., “Driver inattention monitoring system for intelligent vehicles: A review,” Intelligent Transportation Systems, IEEE Transactions on, vol. 12, pp. 596–614, 2011.
9.
A. Cavoukian, “Surveillance, then and now: Securing privacy in public spaces,” Office of the Information and Privacy Commissioner of Ontario, 2013.
10.
H. C. Choe, et al., “Wavelet-based ground vehicle recognition using acoustic signals,” in Aerospace/Defense Sensing and Controls, 1996, pp. 434–445.
11.
U. Braga-Neto, et al., “Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators,” Journal of Electronic Imaging, vol. 13, pp. 802–813, 2004.
12.
A. Akula, et al., “Moving target detection in thermal infrared imagery using spatiotemporal information,” JOSA A, vol. 30, pp. 1492–1501, 2013.
13.
T. Tuytelaars and K. Mikolajczyk, “Local invariant feature detectors: a survey,” Foundations and Trends® in Computer Graphics and Vision, vol. 3, pp. 177–280, 2008.
14.
D. G. Lowe, “Object recognition from local scale-invariant features,” in Computer vision, 1999. The proceedings of the seventh IEEE international conference on, 1999, pp. 1150–1157.
15.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, pp. 886–893.
16.
H. Bay, et al., “Surf: Speeded up robust features,” in Computer vision–ECCV 2006, ed: Springer, 2006, pp. 404–417.
17.
T. Ojala, et al., “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, pp. 971–987, 2002.
18.
T. Ahonen, et al., “Face description with local binary patterns: Application to face recognition,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 28, pp. 2037–2041, 2006.
19.
D. Huang, et al., “Local binary patterns and its application to facial image analysis: a survey,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 41, pp. 765–781, 2011.
20.
M. Pietikäinen, et al., “Local binary patterns for still images,” in Computer Vision Using Local Binary Patterns, ed: Springer, 2011, pp. 13–47.
21.
Z. Guo, et al., “A completed modeling of local binary pattern operator for texture classification,” Image Processing, IEEE Transactions on, vol. 19, pp. 1657–1663, 2010.
22.
X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” Image Processing, IEEE Transactions on, vol. 19, pp. 1635–1650, 2010.
23.
D. Xia, et al., “Real-time infrared pedestrian detection based on multi-block LBP,” in Computer Application and System Modeling (ICCASM), 2010 International Conference on, 2010, pp. V12–139-V12-142.
24.
S. Liao, et al., “Learning Multi-scale Block Local Binary Patterns for Face Recognition,” in Advances in Biometrics. vol. 4642, S.-W. Lee and S. Li, Eds., ed: Springer Berlin Heidelberg, 2007, pp. 828–837.
25.
J. Sun, et al., “Concave-convex local binary features for automatic target recognition in infrared imagery,” EURASIP Journal on Image and Video Processing, vol. 2014, pp. 1–13, 2014.
26.
X. Wu, et al., “Improved Local Ternary Patterns for Automatic Target Recognition in Infrared Imagery,” Sensors, vol. 15, pp. 6399–6418, 2015.
27.
T. Ojala and M. Pietikäinen, “Unsupervised texture segmentation using feature distributions,” Pattern Recognition, vol. 32, pp. 477–486, 1999.
28.
X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” in Analysis and Modeling of Faces and Gestures, ed: Springer, 2007, pp. 168–182.
29.
Z. Guo and D. Zhang, “A completed modeling of local binary pattern operator for texture classification,” Image Processing, IEEE Transactions on, vol. 19, pp. 1657–1663, 2010.
30.
V. N. Vapnik, “An overview of statistical learning theory,” Neural Networks, IEEE Transactions on, vol. 10, pp. 988–999, 1999.
31.
F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 42, pp. 1778–1790, 2004.
Metadaten
Titel
Local Binary Pattern and Its Variants for Target Recognition in Infrared Imagery
verfasst von
Aparna Akula
Ripul Ghosh
Satish Kumar
H. K. Sardana
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Proceedings of International Conference on Computer Vision and Image Processing

Print ISBN: 978-981-10-2103-9

Electronic ISBN: 978-981-10-2104-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-2104-6_27

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