Abstract
In this paper, we present a novel 3D object recognition system. In this system, we capture both the color and depth information of 3D objects using Kinect, and represent them in RGB-D images. To alleviate the deformations and partial defects of the obtained 3D surface textures, 3D texture reconstruction techniques are applied. In order to improve the recognition accuracy, we exploit metric learning methods for the K-nearest neighbor (KNN) classifier. Promising results are obtained on a real-world 3D object recognition application.
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CITATION STYLE
Zhong, G., Mao, X., Shi, Y., & Dong, J. (2015). 3D texture recognition for RGB-D images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9257, pp. 518–528). Springer Verlag. https://doi.org/10.1007/978-3-319-23117-4_45
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