论文标题
全向图像的失真自适应葡萄束计数
Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images
论文作者
论文摘要
本文提出了全向图像的第一个对象计数方法。由于常规对象计数方法无法处理全向图像的失真，因此我们建议使用立体投影处理它们，这使传统方法能够获得密度函数的良好近似值。但是，通过立体投影获得的图像仍然扭曲。因此，为了管理这种失真，我们提出了两种方法。一种是一种新的数据增强方法，专为全向图像的立体投影而设计。另一个是一种失真自适应的高斯内核，在考虑到立体投影的扭曲时，会产生密度地面真相。使用将葡萄束的计数作为案例研究，我们构建了一个原始的葡萄束图像数据集，该数据集由全向图像组成，并进行了实验以评估所提出的方法。结果表明，所提出的方法的性能优于常规方法的直接应用，将平均绝对误差提高了14.7％，平均平方误差提高了10.5％。
This paper proposes the first object counting method for omnidirectional images. Because conventional object counting methods cannot handle the distortion of omnidirectional images, we propose to process them using stereographic projection, which enables conventional methods to obtain a good approximation of the density function. However, the images obtained by stereographic projection are still distorted. Hence, to manage this distortion, we propose two methods. One is a new data augmentation method designed for the stereographic projection of omnidirectional images. The other is a distortion-adaptive Gaussian kernel that generates a density map ground truth while taking into account the distortion of stereographic projection. Using the counting of grape bunches as a case study, we constructed an original grape-bunch image dataset consisting of omnidirectional images and conducted experiments to evaluate the proposed method. The results show that the proposed method performs better than a direct application of the conventional method, improving mean absolute error by 14.7% and mean squared error by 10.5%.
