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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可在汽车应用中更好地检测物体

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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:所提出的技术解决了像素级上两个成像传感器的融合方法。融合后的图像将提供一个场景表示,可以抵抗光照变化和不同的天气状况。因此,每个摄像机优点的结合将扩展许多计算机视觉应用程序的功能,例如视频监视和自动对象识别。在两个传感器的图像上检查了提出的基于像素的融合技术,这两个传感器是内置在车辆中的远红外(FIR)摄像头和可见光摄像头。首先使用二进小波变换对传感器图像进行分解。变换后的数据在“目标导向”融合规则控制的小波域中合并。最后,融合的小波表示图像将由行人检测系统处理。

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