基于角度扩展形状上下文描述的目标检测算法研究

An Object Detection Algorithm Based on Angle  Spread Shape Context Descriptor

  • 摘要: 广义Hough变换的轮廓R表以及传统形状上下文是一种较好的形状描述算子,它们可以较好地解决非形变目标定位问题,但是对于解决存在形变的目标检测定位问题却存在不少困难。为解决该问题提出基于角度扩展的改进形状上下文图像特征描述。传统形状上下文形状描述器对于相近的两条边缘线具有不同的角度描述,在描述其相似度时会产生一定的偏差。通过对传统形状上下文描述图像特征的角度参数进行扩展,可以在一定程度上提高检测算法在目标发生形变情况下的鲁棒性。实验表明,本文算法通过对目标样本的训练能够有效的抽取稳定的形状上下文特征,然后通过匹配投票检测出目标位置,在计算机视觉领域具有一定的应用意义。

     

    Abstract: The R-table of generalized Hough transform(GHT) and classic Shape Context(SC) are good shape descriptors. They can locate non-deformed shape object, while it is difficult to solve the problem when the target is similar but not necessarily identical to the user, that to say, there are deformation in some sense. A target locate algorithm based on angle spread Shape Context for solving this problem is presented in this paper. A common problem for the classic shape context is that when two contours are in different angular bins, similar contours have very different histograms. To overcome this problem, overlap spans of adjacent angular bins is presented. Experiments show that our method is capable of detecting object effectively and is valuable in computer vision field’s applications.

     

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