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A Target Detection Method in Multimodal Images with Complex Backgrounds and Different Views

机译:具有复杂背景和不同视角的多峰图像中的目标检测方法

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Target detection in multimodal (multisensor) images is a difficult problem especially with the impact of different views and the complex backgrounds. In this paper, we propose a target detection method based on ground region matching and spatial constraints to solve it. First, the extrinsic parameters of camera are used to transform the images to reduce the impact of viewpoints differences. Then the stable ground object regions are extracted by MSER. Those regions are used to build a graph model to describe the reference image with spatial constraints to reduce the impact of multimodal and complex backgrounds. At last, the ground region matching and the model registration with sensed images are used to find the target. Using this method, we overcome those difficulties and obtain a satisfied experiment resu the final detection rate is 94.34% in our data set of visible reference images in top views and infrared sensed images in side views
机译:多模式(多传感器)图像中的目标检测是一个难题,尤其是在不同视图和复杂背景的影响下。本文提出了一种基于地面区域匹配和空间约束的目标检测方法。首先,使用相机的外部参数对图像进行变换,以减少视点差异的影响。然后通过MSER提取稳定的地面物体区域。这些区域用于构建图形模型,以描述具有空间约束的参考图像,以减少多峰和复杂背景的影响。最后,利用地面区域匹配和模型与感应图像的配准来寻找目标。使用这种方法,我们克服了这些困难,获得了满意的实验结果。在俯视图中可见参考图像和侧视图中红外感应图像的数据集中,最终检测率为94.34%

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