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Pixel-based data fusion for a better object detection in automotive applications

机译:基于像素的数据融合,在汽车应用中更好的对象检测

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The proposed technique addresses a fusion method of two imaging sensors on pixel-level. The fused image will provide a scene representation which is robust against illumination changes and different weather conditions. Thus, the combination of the advantages of each camera will extend the capabilities for many computer vision applications, such as video surveillance and automatic object recognition. The presented pixel-based fusion technique is examined on the images of two sensors, a far-infrared (FIR) light camera and a visible light camera which are built-in a vehicle. The sensor images are first decomposed using the Dyadic Wavelet Transform. The transformed data are combined in the wavelet domain controlled by a “goal-oriented” fusion rule. Finally, the fused wavelet representation image will be processed by a pedestrian detection system.
机译:所提出的技术在像素级上解决了两个成像传感器的融合方法。融合图像将提供场景表示,其对照明变化和不同天气条件具有稳健性。因此,每个摄像机的优点的组合将扩展许多计算机视觉应用的能力,例如视频监控和自动对象识别。在两个传感器的图像上检查所呈现的基于像素的融合技术,远红外(FIR)光相机和内置车辆的可见光相机。首先使用Dyadic小波变换分解传感器图像。转换的数据在由“面向目标”融合规则控制的小波域中。最后,熔融小波表示图像将由行人检测系统处理。

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