Open Access
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
Published online 01 August 2016
  1. S.B. Niku, Introduction to Robotics: Analysis, Control, Applications. Prentice Hall/Pearson, (2004).
  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]
  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).
  4. C.T. Zahn, Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Transactions on Computing, 20:68–86, (1971). [CrossRef]
  5. P.F. Felzenszwalb, D.P. Huttenlocher, Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59(2):167–181, (2004). [CrossRef]
  6. B. Herbert, T. Tinne, V. G. Luc, SURF: Speeded Up Robust Features. Computer Vision & Image Understanding, 110(3):404–417, (2006).
  7. D. Lowe, Object recognition from local scale-invariant features. IEEE International Conference on Computer Vision, 2:1150–1157, (1999).
  8. Q. Zhu, C. Liu, C. Cai, A Novel Robot Visual Homing Method Based on SIFT Features. Sensors, 15(10):26063–26084, (2015). [CrossRef]
  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).
  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]
  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]
  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]
  13. C. Cortes, V. Vapnik, Support-vector networks. Machine Learning, 3(20): 273–297, (1995).
  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]
  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]
  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).
  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]
  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).
  19. S. Manen, M. Guillaumin, L.V. Gool, Prime object proposals with randomized prim’s algorithm. International Conference on Computer Vision, 2536–2543, (2013).
  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]
  21. J. Rabin, J. Delon, GousseauY., A contrario matching of SIFT-like descriptors. Pattern Recognition, IEEE International Conference on. Pattern Recognition, 1–4, (2009).
  22. M. Everingham, L.V. Gool, C.K.I. Williams, J. Winn, A. Zisserman, The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results., (2007).
  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).
  24. J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, L. FeiFei, ImageNet Large Scale Visual Recognition Competition 2014., (2014)