一种基于多尺度空间的高级形状上下文描述符

Wang Wen-fei, Wen Gong-jian, G. Feng
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引用次数: 0

摘要

传统的形状上下文描述子方法对目标轮廓噪声和形状局部轻微变形具有不变性,同时在没有足够的优先级知识的情况下,需要有效地选择形状上下文模型的邻居半径参数。此外,目标识别性能和计算效率还有待进一步提高。因此,本文提出了一种基于多尺度空间的高级形状上下文描述符。该方法仅从鲁棒轮廓曲率极值点提取形状上下文描述符,有效地抑制轮廓噪声和局部轻微变形的影响,并自动选择相邻半径参数。与传统的形状匹配算法相比,本文方法的鲁棒性和效率都有明显提高,同时识别性能更加可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An advanced shape context descriptor based on multi-scale spaces
Traditional approaches of shape context (SC) descriptor are invariant to object contour noise and shape local slightly deformation, meanwhile, the neighbor radius parameter of shape context model need to be effectively selected without enough apriority knowledge. Additionally, object recognition performance and computational efficiency should be improved in a step future. Therefore, this paper provides an advanced shape context descriptor based on multi-scale spaces. Beyond this method, the shape context descriptor is only extracted from robust contour curvature extremal value point, which is effective to bate the influence of contour noise and local slightly deformation, the neighbor radius parameter is also automatically selected. Comparing with traditional shape matching algorithms, the robustness and the efficiency of this paper approach is tested to be improved distinctly, and the recognition performance is more reliable at the same time.
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