Magnier Baptiste, Moradi Behrang
IMT Mines Alès, LGI2P, 6. Avenue de Clavières, 30100 Alès, France.
J Imaging. 2019 Sep 23;5(10):77. doi: 10.3390/jimaging5100077.
This paper presents a new, normalized measure for assessing a contour-based object pose. Regarding binary images, the algorithm enables supervised assessment of known-object recognition and localization. A performance measure is computed to quantify differences between a reference edge map and a candidate image. Normalization is appropriate for interpreting the result of the pose assessment. Furthermore, the new measure is well motivated by highlighting the limitations of existing metrics to the main shape variations (translation, rotation, and scaling), by showing how the proposed measure is more robust to them. Indeed, this measure can determine to what extent an object shape differs from a desired position. In comparison with 6 other approaches, experiments performed on real images at different sizes/scales demonstrate the suitability of the new method for object-pose or shape-matching estimation.
本文提出了一种用于评估基于轮廓的物体姿态的新的归一化度量。对于二值图像，该算法能够对已知物体识别和定位进行有监督的评估。计算一种性能度量以量化参考边缘图与候选图像之间的差异。归一化适用于解释姿态评估的结果。此外，通过突出现有度量在主要形状变化（平移、旋转和缩放）方面的局限性，展示所提出的度量对这些变化更具鲁棒性，从而很好地推动了新度量的提出。实际上，该度量可以确定物体形状与期望位置的差异程度。与其他6种方法相比，在不同尺寸/比例的真实图像上进行的实验证明了新方法适用于物体姿态或形状匹配估计。
