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Object detection with Gabor filters and cumulative histograms

机译:具有Gabor滤波器和累积直方图的对象检测

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Proposes an algorithm for segmentation and extracting an object region by using Gabor filters. Gabor filters are exploited to extract spatial frequency in some orientations, and not only the outputs of Gabor filters but also color information is used to construct the features at each image pixel. The criterion is devised so as to consider the similarity, the region size and the region shape factors in order to efficiently merge the features. In general, a complex object may be segmented into multiple regions. However for the purpose of detecting such a complex object, we represent the object region by the normalized cumulative histogram of features. From experimental results, it is found that the proposed algorithm is able to efficiently detect the object regions such as cars in images of usual traffic scenes.
机译:提出用于通过使用Gabor滤波器分割和提取对象区域的算法。利用Gabor滤波器在某些方向上提取空间频率,而不仅使用Gabor滤波器的输出,还用于构造每个图像像素处的特征。设计标准以考虑相似性,区域大小和区域形状因子,以便有效地合并特征。通常,复杂对象可以分段为多个区域。然而,为了检测这种复杂对象,我们通过归一化的累积直方图来表示对象区域。从实验结果来看,发现该算法能够有效地检测通常交通场景的图像中的目标区域,例如汽车。

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