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Lifting wavelet method of target detection

机译:目标检测的提升小波方法

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摘要

Image target recognition plays a very important role in the areas of scientific exploration, aeronautics and space-to-ground observation, photography and topographic mapping. Complex environment of the image noise, fuzzy, all kinds of interference has always been to affect the stability of recognition algorithm. In this paper, the existence of target detection in real-time, accuracy problems, as well as anti-interference ability, using lifting wavelet image target detection methods. First of all, the use of histogram equalization, the goal difference method to obtain the region, on the basis of adaptive threshold and mathematical morphology operations to deal with the elimination of the background error. Secondly, the use of multi-channel wavelet filter wavelet transform of the original image de-noising and enhancement, to overcome the general algorithm of the noise caused by the sensitive issue of reducing the rate of miscarriage of justice will be the multi-resolution characteristics of wavelet and promotion of the framework can be designed directly in the benefits of space-time region used in target detection, feature extraction of targets. The experimental results show that the design of lifting wavelet has solved the movement of the target due to the complexity of the context of the difficulties caused by testing, which can effectively suppress noise, and improve the efficiency and speed of detection.
机译:图像目标识别在科学探索,航空和空对地观察,摄影和地形图绘制领域中发挥着非常重要的作用。复杂环境下的图像噪声,模糊,各种干扰一直以来都影响着识别算法的稳定性。本文采用提升小波图像目标检测方法,存在目标检测的实时性,准确性问题以及抗干扰能力。首先,使用直方图均衡化,目标差法获得区域,在自适应阈值和数学形态学运算的基础上,消除背景误差。其次,利用多通道小波滤波器对原始图像进行小波变换去噪和增强,克服一般算法对噪声造成的敏感问题,降低司法流产率将是多分辨率特征。小波变换和框架的推广可以直接利用时空区域的优势来进行目标检测,目标特征提取。实验结果表明,提升小波的设计解决了由于目标复杂而导致的目标运动的困难,可以有效抑制噪声,提高检测效率和速度。

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