Open Access
Issue
MATEC Web Conf.
Volume 68, 2016
2016 The 3rd International Conference on Industrial Engineering and Applications (ICIEA 2016)
Article Number 03001
Number of page(s) 6
Section Design and Development of Robots
DOI https://doi.org/10.1051/matecconf/20166803001
Published online 01 August 2016
  1. S.B. Niku, Introduction to Robotics: Analysis, Control, Applications. Prentice Hall/Pearson, (2004). [Google Scholar]
  2. J. Sivic, A. Zisserman, Video Google: A Text Retrieval Approach to Object Matching in Videos. IEEE International Conference on Computer Vision, 1470, (2003). [CrossRef] [Google Scholar]
  3. A. Sampath, A. Sivaramakrishnan, K. Narayan, A Study of Household Object Recognition Using SIFT-Based Bag-of-Words Dictionary and SVMs. Proceedings of the International Conference on Soft Computing Systems. Springer India, (2016). [Google Scholar]
  4. C.T. Zahn, Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Transactions on Computing, 20:68–86, (1971). [CrossRef] [Google Scholar]
  5. P.F. Felzenszwalb, D.P. Huttenlocher, Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59(2):167–181, (2004). [CrossRef] [Google Scholar]
  6. B. Herbert, T. Tinne, V. G. Luc, SURF: Speeded Up Robust Features. Computer Vision & Image Understanding, 110(3):404–417, (2006). [Google Scholar]
  7. D. Lowe, Object recognition from local scale-invariant features. IEEE International Conference on Computer Vision, 2:1150–1157, (1999). [Google Scholar]
  8. Q. Zhu, C. Liu, C. Cai, A Novel Robot Visual Homing Method Based on SIFT Features. Sensors, 15(10):26063–26084, (2015). [CrossRef] [Google Scholar]
  9. Y. Ke, R. Sukthankar, PCA-SIFT: a more distinctive representation for local image descriptors. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2:506–513, (2004). [Google Scholar]
  10. N. A. M. Isa, S. A. Salamah, U. K. Ngah. Adaptive fuzzy moving K-means clustering algorithm for image segmentation. IEEE Transactions on Consumer Electronic, 4(55): 2145–2153, (2009). [CrossRef] [Google Scholar]
  11. C.H. Lin, C.C. Chen, H.L. Lee, Fast K-means algorithm based on a level histogram for image retrieval. Expert Systems with Applications, 41(7): 3276–3283, (2014). [CrossRef] [Google Scholar]
  12. G. Fanelli, M. Dantone, J. Gall, Random Forests for Real Time 3D Face Analysis. International Journal of Computer Vision, 101(3): 437–458, (2013). [CrossRef] [Google Scholar]
  13. C. Cortes, V. Vapnik, Support-vector networks. Machine Learning, 3(20): 273–297, (1995). [Google Scholar]
  14. B.C. Kuo, H.H. Ho, C.H. Li, A Kernel-Based Featur-e Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 7(7): 317–326, (2014). [CrossRef] [Google Scholar]
  15. C.C. Chang, C.J. Lin, LIBSVM: A library for support vector machines. Acm Transactions on Intelligent Systems & Technology, 2(3): 389–396, (2011). [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  16. C. We, C. X. xin, and Z. Li, Box-counting dimension analysis of edge images detected with classical edge detector. International Conference on measuring technology and mechatronics automation, Changsha, China, 756–758, (2010). [Google Scholar]
  17. J. Uijlings, K. Sande, T. Gevers, A. Smeulders, Selective search for object recognition. International Journal of Computer Vision, 104(2): 154–171, (2013). [CrossRef] [Google Scholar]
  18. X. Wan, X. Zheng, X. Pang, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence, 37(9): 1904–1916, (2014). [Google Scholar]
  19. S. Manen, M. Guillaumin, L.V. Gool, Prime object proposals with randomized prim’s algorithm. International Conference on Computer Vision, 2536–2543, (2013). [Google Scholar]
  20. B. Alexe, T. Deselaers, V. Ferrari, Measuring the objectness of image windows. IEEE Transactions on Pattern Analysis & Machine Intelligence, 34(11): 2189–2202, (2012). [CrossRef] [Google Scholar]
  21. J. Rabin, J. Delon, GousseauY., A contrario matching of SIFT-like descriptors. Pattern Recognition, IEEE International Conference on. Pattern Recognition, 1–4, (2009). [Google Scholar]
  22. M. Everingham, L.V. Gool, C.K.I. Williams, J. Winn, A. Zisserman, The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html, (2007). [Google Scholar]
  23. S.X. Hui, Z. Lin, B. Jonathan, W. Ying, Mobile product image search by automatic query object extraction. European Conference on Computer Vision, 114–127, (2012). [Google Scholar]
  24. J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, L. FeiFei, ImageNet Large Scale Visual Recognition Competition 2014. http://www.imagenet.org/challen-ges/LSVRC/2014/, (2014) [Google Scholar]

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