Automatic target recognition based on local feature extraction

被引：0

作者：

Jia, Ping ^{[1
]}

Xu, Ning ^{[1
,2
]}

Zhang, Ye ^{[1
]}

机构：

[1] Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences

[2] University of Chinese Academy of Sciences

来源：

Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2013年 / 21卷 / 07期

关键词：

Automatic target recognition; Local feature extraction; Nearest feature space classifier; Principle component analysis;

D O I：

10.3788/OPE.20132107.1898

中图分类号：

学科分类号：

摘要：

A target recognition method was proposed to recognize targets with different scales, view-points and illuminations automatically. First, a scale space of images was established, and the local key points in the scale space were extracted by incorporating the Hessian and Harris scale-space detectors. Then, the main orientations of the key points and orientation histograms were calculated and 128 element feature vectors for each key point were established, in which these feature vectors were invariant in different rotations and illuminants. To reinforce the performance, principle component analysis was incorporated to reduce the dimensionality of feature vectors and improve calculating speeds for the recognition. The nearest feature space classifier was used for increasing the recognition speeds in robustness. Experiment results show that this proposed method achieves a significant improvement in automatic target recognition rate, and the recognition rates for varied view-points, scales and illuminations are 61.9%, 80.5%, and 84.4%, respectively. Compared with the Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the proposed method achieves a significant improvement in automatic target recognition rate in presence of varying viewpoints, scales and illuminations.

引用

页码：1898 / 1905

页数：7

共 26 条

[1]

Ratches J.A., Review of current aided/automatic target acquisition technology for military target acquisition tasks, Optical Engineering, 50, 7, pp. 072001-8, (2011)

[2]

Ratches J.A., Walters C., Buser R.C., Et al., Aided and automatic target recognition based upon sensory inputs from image forming systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 9, pp. 1004-1019, (1997)

[3]

Saul M.D., Kober V., Nonlinear synthetic discriminant function filters for illumination-invariant pattern recognition, Optical Engineering, 47, 6, pp. 067201-9, (2008)

[4]

Zhang Z.X., Huang K.Q., Wang Y.H., Et al., Three-dimensional deformable-model-based localization and recognition of road vehicles, IEEE Transactions on Image Processing, 21, 1, pp. 1-13, (2012)

[5]

Ding Y., Jin W.Q., Wang H., A target recognition algorithm based on a support vector machine, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments, (2008)

[6]

Oveisi F., Tree-structured feature extraction using mutual information, IEEE Transactions on Neural Networks and Learning Systems, 23, 1, pp. 127-137, (2012)

[7]

Feng Z.Z.X.D., Ya F.H., Gabor filter approach to joint feature extraction and target recognition, IEEE Transactions on Aerospace and Electronic Systems, 45, 1, pp. 17-30, (2009)

[8]

Ji Y., Gaoyun W.A., Qiu Q.R., Independent gabor analysis of discriminant features fusion for face recognition, Signal Processing Letters, IEEE, 16, 2, pp. 97-100, (2009)

[9]

Kembhavi A., Harwood D., Davis L.S., Vehicle detection using partial least squares, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 6, pp. 1250-1265, (2011)

[10]

Scheirer W.J., Meta-recognition: The theory and practice of recognition score analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 8, pp. 1689-1695, (2011)

← 1 2 3 →