复杂环境下的自适应红外目标分割算法
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摘要
针对现有图像分割算法存在分割效果不佳及耗时长等缺点,提出了一种二维Otsu法红外目标阈值分割算法。根据局部邻域熵引入图像背景因子来对图像进行预分类,并采用最佳阈值与类内与类间方差的归一化处理对图像进行图像分类;最终通过约束阈值的搜索范围,来提高算法的准确性与稳定性。实验结果表明,该方法能够对不同类型的图像进行有效的图像分割同时保持较高的编码效率,与其他算法比较,该算法的图像分割效果及消耗时间具有一定的优势。
Considering the disadvantages of segmentation algorithms, such as poor segmentation effects and a time-consuming process, a two-dimensional Otsu infrared target threshold segmentation algorithm is presented. The image is pre-classified by introducing the background factor according to the local neighborhood entropy, and it is classified by the normalization of the best threshold and the intra-classand inter-class variances. Finally, the accuracy and stability of the algorithm are improved by restricting the search range of the threshold. Experimental results demonstrate that this method can effectively segment different types of images while maintaining high coding efficiency. Therefore, compared to other algorithms,our algorithm exhibits advantages in image segmentation and is less time-consuming.
Considering the disadvantages of segmentation algorithms, such as poor segmentation effects and a time-consuming process, a two-dimensional Otsu infrared target threshold segmentation algorithm is presented. The image is pre-classified by introducing the background factor according to the local neighborhood entropy, and it is classified by the normalization of the best threshold and the intra-classand inter-class variances. Finally, the accuracy and stability of the algorithm are improved by restricting the search range of the threshold. Experimental results demonstrate that this method can effectively segment different types of images while maintaining high coding efficiency. Therefore, compared to other algorithms,our algorithm exhibits advantages in image segmentation and is less time-consuming.
引文
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[17]宿丁,张启衡,陶冰洁,等.复杂背景下多源多目标图像的分形分割算法[J].红外与激光工程,2007,36(3):387-390.SU Ding,ZHANG Qiheng,TAO Bingjie,et al.Fractal segmentation for multi-trget from multi-source in complex backgrounds[J].Infrared and Laser Engineering,2007,36(3):387-390.
[2]Theo Gevers.Adaptive Image Segmentation by Combining Photometric Invariant Region and Edge Information[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(6):848-852.
[3]WANG Jie,JU Lili,WANG Xiaoqiang.Image segmentation using local Variation and Edge-Weighted Centroidal Voronoi Tessellations[J].IEEETransactions on Image Processing,2011,20(11):3242-3256.
[4]YU Jinhua,TAN Jinglu.Object density-based image segmentation and its applications in biomedical image analysis[J].Computer Methods and Programs in Biomedicine,2009,96(3):193-204.
[5]Ooi W S,Lim PC P.Fuzzy clustering of color and texture features for image segmentation:A study on satellite image retrieval[J].Journal of Intelligent&Fuzzy Systems:Applications in Engineering and Technology,2006,17(6):297-311.
[6]Ronald H Y Chung.An Efficient Parameterless quadrilateral-based image segmentation method[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(9):1446-1458.
[7]刘坤,郭雷,陈敬松.基于区域分割的序列红外图像融合算法[J].红外与激光工程,2009,38(3):553-558.LIU Kun,GUO Lei,CHEN Jingsong.Sequence infrared image fusion algorithm using region segmentation[J].Infrared and Laser Engineering,2009,38(3):553-558.
[8]Hamed Shah-Hosseini,Reza Safabakhsh.Automatic multilevel thresholding for image segmentation by the growing time adaptive self-organizing map[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(10):1388-1393.
[9]Francisco A Pujol,Mar Pujol,Ramón Rizo,et al.On searching for an optimal threshold for morphological image segmentation[J].Pattern Analysis and Applications,2011,14(3):235-250.
[10]Herrera P J,Pajares G,Guijarro M.A segmentation method using Otsu and fuzzy k-Means for stereovision matching in hemispherical images from forest environments[J].Applied Soft Computing,2011,11(8):4738-4747.
[11]HUANG Dengyuan,WANG Chiahung.Optimal multi-level thresholding using a two-stage Otsu optimization approach[J].Pattern Recognition Letters,2009,30(3):275-284.
[12]Delon J,Desolneux A,Lisani J L,et al.A nonparametric approach for histogram segmentation[J].IEEE Transactions on Image Processing APublication of the IEEE Signal Processing Society,2007,16(1):253-261.
[13]QIN Kun,XU Kai,LIU Feilong,et al.Image segmentation based on histogram analysis utilizing the cloud model[J].Computers&Mathematics with Applications,2011,62(7):2824-2833.
[14]SUN Han,YANG Jingyu,REN Mingwu.A fast watershed algorithm based on chain code and its application in image segmentation[J].Pattern Recognition Letters,2005,26(9):1266-1274.
[15]XU Xiangyang,XU Shengzhou,JIN Lianghai,et al.Characteristic analysis of Otsu threshold and its applications[J].Pattern Recognition Letters,2011,32(7):956-961.
[16]XU Changxin,PENG Guohua.Fast algorithm for 2D Otsu thresholding algorithm[J].Science Press,2012,32(5):1258-1260.
[17]宿丁,张启衡,陶冰洁,等.复杂背景下多源多目标图像的分形分割算法[J].红外与激光工程,2007,36(3):387-390.SU Ding,ZHANG Qiheng,TAO Bingjie,et al.Fractal segmentation for multi-trget from multi-source in complex backgrounds[J].Infrared and Laser Engineering,2007,36(3):387-390.