从深度图创建可识别物体的 3D 场景的算法

Maxim V. Bobyr, S. Emelyanov, N. A. Milostnaya
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引用次数: 0

摘要

研究目的开发一种根据合成深度图构建识别物体的三维场景的算法,以提高实时图像处理的速度。三维场景构建算法基于立体图像构建方法,使用三级模糊深度图构建模型。在该模型的第一层,使用改进的 Canny 算法确定物体的边界;在第二层,根据经模糊逻辑方法改进的绝对差值之和算法计算差距值;在最后一层,首先计算从图像边界到识别物体边缘的距离梯度,然后根据在模糊层次模型的第二层和第三层获得的差距值,计算细化的差距值,用于对深度图进行分析。我们开发了一种利用合成深度图构建识别物体三维场景的算法。实验结果表明,与现有的深度图算法(如共轭点算法和金字塔算法)相比,所提出的算法具有更好的性能。实验结果表明,与所分析的算法(共轭点算法和金字塔算法)相比,所提出的算法具有更低的复杂度。三维场景构建操作的最小平均执行时间约为 1-2 分钟,与共轭点算法相比几乎提高了 120 倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algorithm for Creating 3d Scenes of Recognized Objects from Depth Maps
Purpose of research. Development of an algorithm for constructing 3d scenes of recognized objects from synthesized depth maps in order to improve the speed of real-time image processing.Methods. The 3d scene construction algorithm is based on the method of stereo image construction using a threelevel fuzzy depth map construction model. At the first level of this model the boundaries of objects are determined using a modified Canny algorithm, at the second level the values of disparity are calculated on the basis of the sum of absolute differences algorithm modified by fuzzy logic methods, and at the final level the gradients of distances from the boundaries of images to the edges of recognized objects are calculated first and then according to the obtained values of disparity at the second and third levels of the fuzzy hierarchical model, the refined values of disparity are calculated, which are used to carry out the analysis of the depth map.Results. An algorithm for constructing 3d scenes of recognized objects using synthesized depth maps has been developed. It was determined that the proposed algorithm has better performance compared to existing depth map algorithms such as conjugate point algorithm and pyramidal algorithm.Conclusion. The experimental results showed that the proposed algorithm has a lower complexity compared to the analyzed algorithms (conjugate points and pyramidal). The minimum average execution time of the 3d scene construction operation was about 1-2 minutes, which is almost 120 times better compared to the conjugate point algorithms.
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