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Autonomous Robots

Erschienen in:

12.04.2016

A method of classifying crumpled clothing based on image features derived from clothing fabrics and wrinkles

verfasst von: Kimitoshi Yamazaki

Erschienen in: Autonomous Robots | Ausgabe 4/2017

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Abstract

This paper describes a method of clothing classification using a single image. The method is intended to be used for building autonomous systems, that can recognize casually thrown ordinary clothing. A set of Gabor filters is applied to an input image, and image features invariant to translation, rotation and scale are then generated. In this paper, we propose descriptions of the features, focusing on clothing fabrics, wrinkles, and cloth overlaps. In addition, to deal with situations involving clumped clothing, the description is extended by combining with superpixel representation. Experiments using a state description and classification using real clothing demonstrate the effectiveness of the proposed method.

Vorheriger Artikel Inverse dynamics with rigid contact and friction

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“Tutorial on Gabor Filters,” http://mplab.ucsd.edu/tutorials/tutorials.html.

Bay, Herbert, Ess, Andreas, Tuytelaars, Tinne, & Van Gool, Luc. (2008). SURF: Speeded Up Robust Features. Computer Vision and Image Understanding (CVIU), 110(3), 346–359.CrossRef

Bigü, J., & du buf, J. M. (1994). N-folded symmetries by complex moments in gabor space and their application to unsupervised texture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, 16(1), 80–87.CrossRef

Chang, C., Lin, C. (2011). LIVSVM—A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm/.

Christoudias, C., Geogescu, B. & Meer, P. (2002). Synergism on low level vision. In International Conference on Pattern Recognition, pp. 150–155.

Comanicui, D., & Meer, P. (2002). Mean shift: A robust approach towead feature space analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence, 24(5), 603–619.CrossRef

Csurka, G., Bray, C., Dance, C. & Fan, L. (2004). Visual categorization with bags of keypoints. In Proceedings of ECCV Workshop on Statistical Learning in Computer Vision, pp. 59–74.

Cuén-Rochín, S., Andrade-Cetto, J. & Torras, C. (2008) Action selection for robotic manipulation of deformable planar objects. In Proceedings of Frontier Science Conference Series for Young Researchers: Experimental Cognitive Robotics, pp. 1–6.

Cusumano-Towner, M., Singh, A., Miller, S., O’Brien, J.F. & Abbeel P. (2011) Bringing clothing into desired configurations with limited perception. In Proceedings of IEEE International Conference on Robotics and Automation, pp. 1–8.

Doumanoglou, A., Kargakos, A., Kim, T-K. & Malassiotis, S. (2014). Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic Planning. In Proceedings of IEEE International Conference on Robotics and Automation.

Galun, M., Sharon, E., Basri, R. & Brandt, A. (2003). Texture segmentation by multiscale aggregation of filter responses and shape elements. In Proceedings of IEEE International Conference on Computer Vision, pp. 716–723.

Geusebroek, J., Smeulders, A., & Weijer, J. (2003). Fast anisotropic Gauss filtering. IEEE Transactions on Image Processing, 12(8), 938–943.MathSciNetCrossRefMATH

Hamajima, K. & Kakikura, M. (2000) Planning strategy for unfolding task of clothes: Isolation of clothes from washed mass. In Proceedings of International Conference on Robots and Systems, pp. 1237–1242.

Kita, Y., Saito F. & Kita, N. (2004). A deformable model driven method for handling clothes. In Proceedings of International Conference on Pattern Recognition.

Leung, T., & Malik, J. (2001). Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43(1), 29–44.CrossRefMATH

Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., & Siddiqi, K. (2009). TurboPixels: Fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12), 2290–2297.CrossRef

Li, Y., Chen, C.-F. & Allen, P.K. (2014).Recognition of deformable object category and pose. In Proceedings of IEEE International Conference on Robotics and Automation, pp. 5558–5564

Li, Y., Wang, Y., Case, M., Chang, S.-F. & Allen, P.K. (2014). Real-time pose estimation of deformable objects using a volumetric approach. In Proceedings of IEEE/RSJ International Conference on Robots and Systems, pp. 1046–1052.

Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRef

Maitin-Sphepard, J., Cusumano-Towner, M., Lei, J. & Abbeel, P. (2010) Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding. In International Conference on Robotics and Automation, pp. 2308–2315.

Martin, D., Fowlkes, C., & Marik, J. (2004). Learning to detect natural image boundary using local brightness, color and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5), 530–549.CrossRef

Mikolajczyk, K., & Schmid, C. (2004). Scale and affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63–86.CrossRef

Ono, E., Okabe, H., Ichijo, H. & Aisaka, N. (1990). Robot hand with sensor for cloth handling. In Proceedings 1990, Japan, USA Symposium on Flexible Automation, pp. 1363–1366.

Oren, M., & Nayar, S. (1992). Generalization of the lambertian model and implications for machine vision. International Journal of Computer Vision, 14(3), 227–251.CrossRef

Osawa, F., Seki, H., & Kamiya, Y. (2007). Unfolding of massive laundry and classification types by dual manipulator. Journal of Advanced Computational Intelligence and Intelligent Informatics, 11(5), 457–463.CrossRef

Osher, S., & Sethian, J. A. (1998). Fronts propageting with curvature dependent speed: Algorithm based on Hamilton–Jacobi formation. Journal of Computational Physics, 79, 12–49.CrossRefMATH

Ramisa, A., Alenya, G., Moreno-Noguer, F. & Torras, C. (2012) Using depth and appearance features for informed robot grasping of highly wrinkled clothes. In Proceedings of IEEE International Conference on Robotics and Automation, pp. 1703–1708.

Ren, X., & Malik, J. (2003). Learning a classification model for segmentation. In Proceedings of 9th International Conference Computer Vision, vol. 1, pp. 10–17.

Rubner, Y., Tomasi, C. (1996) Coalescing texture descriptors. In ARPA Image Understanding Workshop, pp. 927–935

Schmid, C. (2001). Constructing models for content-based image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 39–45.

Sun, L., Aragon-Camarasa, G., Rogers, S. & Siebert, P. (2015). Accurate garment surface analysis using an active stereo robot head with application to dual-arm flattening. In Proceedings of IEEE International Conference on Robotics and Automation, pp. 185–192.

Varma, M., & Zisserman, A. (2005). A statistical approach to texture classification from single images. International Journal of Computer Vision, 62(1–2), 61–81.CrossRef

Vincent, L., & Soille, P. (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), 583–598.CrossRef

Willimon, B., Birchfleld, S. & Walker, I. (2011). Classification of clothing using interactive perception. In Proceedings of IEEE International Conference on Robotics and Automation, pp. 1862–1868.

Yamazaki, K., (2015). Instance recognition of clumped clothing using image features focusing on clothing fabrics and wrinkles. In Proceedings of IEEE International Conference on Robotics and Biomimetics, pp.1102–1108.

Yamazaki, K., & Inaba, M. (2009). A cloth detection method based on image wrinkle feature for a daily assistive robots. In IAPR Conferene on Machine Vision Applications, pp. 366–369.

Yamazaki, K., & Inaba, M. (2013). Clothing classification using image features derived from clothing fabrics, wrinkles and cloth overlaps. In Proceedings of IEEE/RSJ International Conference on Robots and Systems, pp. 2710–2717.

Yu, S., & Shi, J. (2003). Multiclass spectral clustering. In IEEE International Conference on Computer Vision, vol. 1, pp. 313–319.

Zhao, Y., & Karypis, G. (2002). Comparison of agglomerative and partitional document clustering algorithms. University of Minnesota—Computer Science and Engineering Technical Report, No. 02-014.

Titel: A method of classifying crumpled clothing based on image features derived from clothing fabrics and wrinkles
verfasst von: Kimitoshi Yamazaki
Publikationsdatum: 12.04.2016
Verlag: Springer US
Erschienen in: Autonomous Robots / Ausgabe 4/2017
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI: https://doi.org/10.1007/s10514-016-9559-z

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