Abstract
Due to the deformable characteristics, recognizing a flexible object remains challenging in computer vision. How to extract the target contour from complex backgrounds fast and exactly, as a key preprocessing step, becomes significant. In this paper, a self-adaptive method of contour extraction is proposed for garment recognition based on improved segmentation and Gabor filter, which plays a vital role in robotic folding of garments. A reference region based Moore-Neighbor algorithm is applied first to realize robust binarization. Then, a criterion is developed to judge the extraction effect, according to which the Gabor filter is utilized to remove complex backgrounds. Finally, fine extraction is implemented with Moore-Neighbor algorithm. Based on public online images and our own experimental dataset, the proposed method is verified and shows excellent performance, proving its effectiveness in garment contour extraction.
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Acknowledgements
This work was funded by Natural Science Foundation of Jiangsu Province under Grant No. BK20210233, National Natural Science Foundation of China under Grant No. 52205009, Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems, and Fundamental Research Funds for the Central Universities.
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Wang, Y., Chai, D., Zhang, J., Bao, W., Li, R., Qin, L. (2022). A Contour Extraction Method for Garment Recognition Based on Improved Segmentation and Gabor Filter. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13599. Springer, Cham. https://doi.org/10.1007/978-3-031-20716-7_32
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