基于极坐标特征矩阵的多类对象排列结构描述方法
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TP391.4TH39

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黑龙江省自然基金青年项目基金(QC2014C054)资助


Arrangement structure description method of multiclass objects based on polar coordinate feature matrix
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    摘要:

    本文提出一种基于极坐标特征矩阵的多类对象排列结构检测方法。极坐标特征矩阵由极径矩阵和极角矩阵构成,它描述了多类对象排列结构中所包含的距离信息和方向信息。该方法在汽车保险盒检测应用上取得了显著的效果,针对于汽车保险盒检测,首先使用CCD工业相机采集保险盒图像,通过方向梯度直方图特征对汽车保险片的标识码进行表征,并结合支持向量机实现对图像中的保险片的定位与识别。然后依据定位和识别结果计算极坐标特征矩阵对汽车保险盒内汽车保险片的排列结构进行描述。最后以极坐标特征矩阵相似度为判断依据实现对汽车保险盒的检测。实验证明,依据极坐标特征矩阵相似度对汽车保险盒的检测准确率达976%。

    Abstract:

    In this paper, an arrangement structure description method of multiclass objects based on polar coordinate feature matrix (PCFM) is proposed. The polar coordinate feature matrix consists of polar radius matrix (PRM) and Polar Angle Matrix (PAM), which describes the distance and angle information contained in the arrangement structure of multiclass objects. This method was applied in the application of vehicle fuse box detection and achieved obvious effects. Aiming at the vehicle fuse box detection, the method firstly uses a charge coupled device (CCD) industrial camera to acquire the images of the vehicle fuse boxes, employs histogram of oriented gradient (HOG) feature to characterize the ID codes of the vehicle fuse chips and combines SVMs to achieve the localization and recognition of the fuse chips in the images. According to the localization and recognition results, the PCFM is calculated to describe the arrangement structure of vehicle fuse chips in vehicle fuse box. Finally, the PCFM similarity is taken as the judgment criterion to realize the detection of the vehicle fuse box. The experiment proves that using PCFM similarity to detect the vehicle fuse box, the detection accuracy reaches 976%.

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陈国杰,尤波.基于极坐标特征矩阵的多类对象排列结构描述方法[J].仪器仪表学报,2019,40(10):55-65

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  • 在线发布日期: 2022-03-09
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