Adaptive object recognition model using incremental feature representation and hierarchical classification
暂无分享,去创建一个
[1] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[2] Jang-Kyoo Shin,et al. Biologically Inspired Saliency Map Model for Bottom-up Visual Attention , 2002, Biologically Motivated Computer Vision.
[3] Minho Lee,et al. Saliency map model with adaptive masking based on independent component analysis , 2002, Neurocomputing.
[4] Minho Lee,et al. Dynamic obstacle identification based on global and local features for a driver assistance system , 2011, Neural Computing and Applications.
[5] Thomas Serre,et al. Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[6] Christof Koch,et al. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .
[7] D. Hubel,et al. Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.
[8] D. Hubel,et al. Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.
[9] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[10] Alexei A. Efros,et al. Discovering object categories in image collections , 2005 .
[11] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[12] Seiichi Ozawa,et al. A Multitask Learning Model for Online Pattern Recognition , 2009, IEEE Transactions on Neural Networks.
[13] Bernd Fritzke,et al. Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.
[14] Michael Brady,et al. Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.
[15] Seiichi Ozawa,et al. A Neural Network Model for Sequential Multitask Pattern Recognition Problems , 2008, ICONIP.
[16] Horst-Michael Groß,et al. A Vector Quantization Approach for Life-Long Learning of Categories , 2008, ICONIP.
[17] Pietro Perona,et al. A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[18] Kunihiko Fukushima,et al. Neocognitron for handwritten digit recognition , 2003, Neurocomputing.
[19] Jeff A. Bilmes,et al. A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .
[20] Pietro Perona,et al. Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[21] Pietro Perona,et al. One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Stephen R. Marsland,et al. A self-organising network that grows when required , 2002, Neural Networks.
[23] Anil K. Jain,et al. Incremental learning for Bayesian classification of images , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).
[24] Y. Yamane,et al. Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns , 2001, Nature Neuroscience.
[25] Minho Lee,et al. Stereo saliency map considering affective factors and selective motion analysis in a dynamic environment , 2008, Neural Networks.
[26] Thomas Hofmann,et al. Probabilistic Latent Semantic Analysis , 1999, UAI.
[27] Li Fei-Fei. Knowledge transfer in learning to recognize visual objects classes , 2006 .
[28] Ilkay Ulusoy,et al. Generative versus discriminative methods for object recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).