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A New Edge Patch with Rotation Invariance for Object Detection and Pose Estimation
Local patch-based methods of object detection and pose estimation are promising. However, to the best of the authors’ knowledge, traditional red-green-blue and depth (RGB-D) patches contain scene interference (foreground occlusion and background clutter) and have little rotation invariance. To solve...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7039296/ https://www.ncbi.nlm.nih.gov/pubmed/32046078 http://dx.doi.org/10.3390/s20030887 |
Sumario: | Local patch-based methods of object detection and pose estimation are promising. However, to the best of the authors’ knowledge, traditional red-green-blue and depth (RGB-D) patches contain scene interference (foreground occlusion and background clutter) and have little rotation invariance. To solve these problems, a new edge patch is proposed and experimented with in this study. The edge patch is a local sampling RGB-D patch centered at the edge pixel of the depth image. According to the normal direction of the depth edge, the edge patch is sampled along a canonical orientation, making it rotation invariant. Through a process of depth detection, scene interference is eliminated from the edge patch, which improves the robustness. The framework of the edge patch-based method is described, and the method was evaluated on three public datasets. Compared with existing methods, the proposed method achieved a higher average F1-score (0.956) on the Tejani dataset and a better average detection rate (62%) on the Occlusion dataset, even in situations of serious scene interference. These results showed that the proposed method has higher detection accuracy and stronger robustness. |
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